IEEE Medical image Title and Abstract 2016

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IEEE TRANSACTION ON MEDICAL IMAGING 2016 TOPICS
Automatic Lumbar Spondylolisthesis Measurement in CT Images
Abstract - Lumbar spondylolisthesis is one of the most common spinal diseases. It is caused by
the anterior shift of a lumbar vertebrae relative to its subjacent vertebrae. In current clinical
practices, staging of spondylolisthesis is often conducted in an empirical and qualitative way.
Although meyerding grading system opens the door to diagnose and stage spondylolisthesis in a
more quantitative way, it relies on the manual measurement of the relative shift between
neighboring lumbar vertebrae, which is time consuming and irreproducible. Thus, an automatic
algorithm to measure lumbar vertebrae shift becomes desirable for spondylolisthesis diagnosis
and staging. However, there are two challenges: (1) Accurate detection of the most anterior and
posterior points on the superior and inferior surfaces of each lumbar vertebrae. Due to the small
size of the vertebrae, slight errors of detection may lead to significant measurement errors,
hence, wrong disease stages. (2) Automatic localize and label each lumbar vertebrae is required
to provide the semantic meaning of the measurement. It is difficult since different lumbar
vertebraes have high similarity of both shape and image appearance. To resolve these challenges,
a new auto measurement framework is proposed and the main contributions lie in the following
aspects: First, a learning based spine labeling method that integrates both the image appearance
and spine geometric information is used for detection of each lumbar vertebrae. Second, a
hierarchical method which uses both the population information from atlases and domain-
specific information in the target image is proposed for most anterior and posterior points
positioning. Our method has been extensively evaluated on 258 CT spondylolisthesis patients,
and experimental results show that our method achieves very similar results to manual
measurements by radiologists and significantly increase the measurement efficiency.
IEEE Transactions on Medical Imaging (January 2016)
White Matter MS-Lesion Segmentation Using a Geometric Brain Model
Abstract - Brain magnetic resonance imaging (MRI) in patients with Multiple Sclerosis (MS)
shows regions of signal abnormalities, named plaques or lesions. The spatial lesion distribution
plays a major role for MS diagnosis. In this paper we present a 3D MS-lesion segmentation

method based on an adaptive geometric brain model. We model the topological properties of the
lesions and brain tissues in order to constrain the lesion segmentation to the white matter. As a
result, the method is independent of an MRI atlas. We tested our method on the MICCAI MS
grand challenge proposed in 2008 and achieved competitive results. In addition, we used an in-
house dataset of 15 MS patients, for which we achieved best results in most distances in
comparison to atlas based methods. Besides classical segmentation distances, we motivate and
formulate a new distance to evaluate the quality of the lesion segmentation, while being robust
with respect to minor inconsistencies at the boundary level of the ground truth annotation.
3D reconstruction of human laryngeal dynamics based on endoscopic high-speed
recordings
Abstract - Standard laryngoscopic imaging techniques provide only limited two-dimensional
insights into the vocal fold vibrations not taking the vertical component into account. However,
previous experiments have shown a significant vertical component in the vibration of the vocal
folds. We present a 3D reconstruction of the entire superior vocal fold surface from 2D high-
speed videoendoscopy via stereo triangulation. In a typical camera-laser set-up the structured
laser light pattern is projected on the vocal folds and captured at 4000 fps. The measuring device
is suitable for in vivo application since the external dimensions of the miniaturized set-up barely
exceed the size of a standard rigid laryngoscope. We provide a conservative estimate on the
resulting resolution based on the hardware components and point out the possibilities and
limitations of the miniaturized camera-laser set-up. In addition to the 3D vocal fold surface, we
extended previous approaches with a G2-continuous model of the vocal fold edge. The clinical
applicability was successfully established by the reconstruction of visual data acquired from 2D
in vivo high-speed recordings of a female and a male subject. We present extracted dynamic
parameters like maximum amplitude and velocity in the vertical direction. The additional vertical
component reveals deeper insights into the vibratory dynamics of the vocal folds by means of a
noninvasive method. The successful miniaturization allows for in vivo application giving access
to the most realistic model available and hence enables a comprehensive understanding of the
human phonation process.

A Novel Regularization Technique for Microendoscopic Electrical Impedance Tomography
Abstract - A novel regularization technique is developed for end-fired microendoscopic
electrical impedance tomography using the dual-mesh method. The new regularization technique
coupled with appropriate forward modeling and inverse mesh design is shown to produce
dramatically improved reconstructions over previous methods. 3D absolute and difference
reconstructions from measured saline tank and ex vivo adipose and muscle tissue experiments
are used to validate the approach. The ex vivo experiments are used as a surrogate for prostate
tissue, which is the primary clinical application for the probe. Inclusion center of mass errors
were less than 0.47 mm for tank experiments with inclusion depths and radial offsets ranging less
than 3 mm and 1.5 mm, respectively. Absolute 3D reconstructions on the tissue show
quantitatively good accuracy and the ability to spatially distinguish small tissue features (adipose
strands of approximately 2.5 mm in width). The reconstruction algorithm developed provides
strong evidence for the promise of surgical margin detection using microendoscopic EIT.
Deformable Graph Model for Tracking Epithelial Cell Sheets in Fluorescence Microscopy
Abstract - We propose a novel method for tracking cells that are connected through a visible
network of membrane junctions. Tissues of this form are common in epithelial cell sheets and
resemble planar graphs where each face corresponds to a cell. We leverage this structure and
develop a method to track the entire tissue as a deformable graph. This coupled model in which
vertices inform the optimal placement of edges and vice versa captures global relationships
between tissue components and leads to accurate and robust cell tracking. We compare the
performance of our method with that of four reference tracking algorithms on four data sets that
present unique tracking challenges. Our method exhibits consistently superior performance in
tracking all cells accurately over all image frames, and is robust over a wide range of image
intensity and cell shape profiles. This may be an important tool for characterizing tissues of this

type especially in the field of developmental biology where automated cell analysis can help
elucidate the mechanisms behind controlled cell-shape changes.
Multiple-Instance Learning for Anomaly Detection in Digital Mammography
Abstract - This paper describes a computer-aided detection and diagnosis system for breast
cancer, the most common form of cancer among women, using mammography. The system
relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical
decision support in previous works from our team. In the proposed framework, breasts are first
partitioned adaptively into regions. Then, features derived from the detection of lesions (masses
and microcalcifications) as well as textural features, are extracted from each region and
combined in order to classify mammography examinations as “normal” or “abnormal”.
Whenever an abnormal examination record is detected, the regions that induced that automated
diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a
first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly
index to each region; local anomaly indices are then combined into a global anomaly index. In a
second scenario, the local and global anomaly detectors are trained simultaneously, without
manual segmentations, using various MIL algorithms (DD, APR, mi- SVM, MI-SVM and
MILBoost). Experiments on the DDSM dataset show that the second approach, which is only
weaklysupervised, surprisingly outperforms the first approach, even though it is strongly-
supervised. This suggests that anomaly detectors can be advantageously trained on large medical
image archives, without the need for manual segmentation.
Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway
Segmentation
Abstract - Analysis of cranial nerve systems, such as the anterior visual pathway (AVP), from
MRI sequences is challenging due to their thin long architecture, structural variations along the
path, and low contrast with adjacent anatomic structures. Segmentation of a pathologic AVP
(e.g. with low-grade gliomas) poses additional challenges. In this work, we propose a fully
automated partitioned shape model segmentation mechanism for AVP steered by multiple MRI
sequences and deep learning features. Employing deep learning feature representation, this

framework presents a joint partitioned statistical shape model able to deal with healthy and
pathological AVP. The deep learning assistance is particularly useful in the poor contrast
regions, such as optic tracts and pathological areas. Our main contributions are: (1) a fast and
robust shape localization method using conditional space deep learning, (2) a volumetric
multiscale curvelet transform– based intensity normalization method for robust statistical model,
and (3) optimally partitioned statistical shape and appearance models based on regional shape
variations for greater local flexibility. Our method was evaluated on MRI sequences obtained
from 165 pediatric subjects. A mean Dice similarity coefficient of 0.779 was obtained for the
segmentation of the entire AVP (optic nerve only=0.791) using the leave-one-out validation.
Results demonstrated that the proposed localized shape and sparse appearance-based learning
approach significantly outperforms current state-of-the-art segmentation approaches and is as
robust as the manual segmentation.
IEEE Transactions on Medical Imaging (February 2016)
Skull optical clearing solution for enhancing ultrasonic and photoacoustic imaging
Abstract - The performance of photoacoustic microscopy (PAM) degrades due to the turbidity
of the skull that introduces attenuation and distortion of both laser and stimulated ultrasound. In
this manuscript, we demonstrated that a newly developed skull optical clearing solution (SOCS)
could enhance not only the transmittance of light, but also that of ultrasound in the skull in vitro.
Thus the photoacoustic signal was effectively elevated, and the relative strength of the artifacts
induced by the skull could be suppressed. Furthermore in vivo studies demonstrated that SOCS
could drastically enhance the performance of photoacoustic microscopy for cerebral
microvasculature imaging.
Automated Real-time Conjunctival Microvasculature Image Stabilization
Abstract - The bulbar conjunctiva is a thin, vascularized membrane covering the sclera of the
eye. Non-invasive imaging techniques have been utilized to assess the conjunctival vasculature
as a means of studying microcirculatory hemodynamics. However, eye motion often confounds
quantification of these hemodynamic properties. In the current study, we present a novel optical
imaging system for automated stabilization of the conjunctival microvasculature images by real-
time eye motion tracking and realignment of the optical path. The ability of the system to

stabilize conjunctival images acquired over time by reducing image displacements and
maintaining the imaging area was demonstrated.
Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting
Abstract - This paper introduces a statistical estimation framework for magnetic resonance
(MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework,
we present a maximum likelihood (ML) formalism to estimate multiple parameter maps directly
from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting,
the alternating direction method of multipliers, and the variable projection method, is developed
to solve the resulting optimization problem. Representative results from both simulations and in
vivo experiments demonstrate that the proposed approach yields significantly improved accuracy
in parameter estimation, compared to the conventional MR fingerprinting reconstruction.
Moreover, the proposed framework provides new theoretical insights into the conventional
approach. We show analytically that the conventional approach is an approximation to the ML
reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed
algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an
initialization.
Transurethral Photoacoustic Endoscopy for Prostate Cancer: A Simulation Study
Abstract - The purpose of this study was to optimize the configuration of a photoacoustic
endoscope (PAE) for prostate cancer detection and therapy monitoring. The placement of optical
fiber bundles and ultrasound detectors was chosen to maximize the photoacoustic imaging
penetration depth. We performed both theoretical calculations and simulations of this optimized
PAE configuration on a prostate-sized phantom containing tumor and various photosensitizer
concentrations. The optimized configuration of PAE with transurethral light delivery
simultaneously increases the imaging penetration depth and improves image quality. Thermal
safety, investigated via COMSOL Multiphysics, shows that there is only a 4 mK temperature rise
in the urethra during photoacoustic imaging, which will cause no thermal damage. One
application of this PAE has been demonstrated for quasi-quantifying photosensitizer
concentrations during photodynamic therapy. The sensitivity of the photoacoustic detection for

TOOKAD was 0.18ng/mg at a 763 nm laser wavelength. Results of this study will greatly
enhance the potential of prostate PAE for in vivo monitoring of drug delivery and guidance of
the laser-induced therapy for future clinical use.
Feasibility of Swept Synthetic Aperture Ultrasound Imaging
Abstract - Ultrasound image quality is often inherently limited by the physical dimensions of the
imaging transducer.We hypothesize that, by collecting synthetic aperture data sets over a range
of aperture positions while precisely tracking the position and orientation of the transducer, we
can synthesize large effective apertures to produce images with improved resolution and target
detectability. We analyze the two largest limiting factors for coherent signal summation:
aberration and mechanical uncertainty. Using an excised canine abdominal wall as a model phase
screen, we experimentally observed an effective arrival time error ranging from 18.3 ns to 58 ns
(root-mean-square error) across the swept positions. Through this clutter-generating tissue, we
observed a 72.9% improvement in resolution with only a 3.75 dB increase in side lobe amplitude
compared to the control case. We present a simulation model to study the effect of calibration
and mechanical jitter errors on the synthesized point spread function. The relative effects of these
errors in each imaging dimension are explored, showing the importance of orientation relative to
the point spread function. We present a prototype device for performing swept synthetic aperture
imaging using a conventional 1-D array transducer and ultrasound research scanner. Point target
reconstruction error for a 44.2 degree sweep shows a reconstruction precision of 82.8 μm and
17.8 μm in the lateral and axial dimensions respectively, within the acceptable performance
bounds of the simulation model. Improvements in resolution, contrast and contrast-to-noise ratio
are demonstrated in vivo and in a fetal phantom.
Real-Time Visualization of Tissue Surface Biochemical Features Derived from
Fluorescence Lifetime Measurements
Abstract - Fiber based fluorescence lifetime imaging has shown great potential for
intraoperative diagnosis and guidance of surgical procedures. Here we describe a novel method
addressing a significant challenge for the practical implementation of this technique, i.e. the real-
time display of the quantified biochemical or functional tissue properties superimposed on the

interrogated area. Specifically, an aiming beam (450 nm) generated by a continuous-wave laser
beam was merged with the pulsed fluorescence excitation light in a single delivery/collection
fiber and then imaged and segmented using a color-based algorithm. We demonstrate that this
approach enables continuous delineation of the interrogated location and dynamic augmentation
of the acquired frames with the corresponding fluorescence decay parameters. The method was
evaluated on a fluorescence phantom and fresh tissue samples. Current results demonstrate that
34 frames per second can be achieved for augmenting videos of 640×512 pixels resolution. Also
we show that the spatial resolution of the fluorescence lifetime map depends on the tissue optical
properties, the scanning speed, and the frame rate. The dice similarity coefficient between the
fluorescence phantom and the reconstructed maps was estimated to be as high as 93%. The
reported method could become a valuable tool for augmenting the surgeon’s field of view with
diagnostic information derived from the analysis of fluorescence lifetime data in real-time using
handheld, automated, or endoscopic scanning systems. Current method provides also a means for
maintaining the tissue light exposure within safety limits. This study provides a framework for
using an aiming beam with other point spectroscopy applications.
Probabilistic Modeling of Imaging, Genetics and Diagnosis
Abstract - We propose a unified Bayesian framework for detecting genetic variants associated
with disease by exploiting imagebased features as an intermediate phenotype. The use of
imaging data for examining genetic associations promises new directions of analysis, but
currently the most widely used methods make sub-optimal use of the richness that these data
types can offer. Currently, image features are most commonly selected based on their relevance
to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain
the selected features. In contrast, our method performs these tasks simultaneously in order to
jointly exploit information in both data types. The analysis yields probabilistic measures of
clinical relevance for both imaging and genetic markers. We derive an efficient approximate
inference algorithm that handles the high dimensionality of image and genetic data. We evaluate
the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also
illustrate our method on Alzheimer’s Disease Neuroimaging Initiative data.

OSSI-PET: Open-access database of Simulated [<sup>11</sup>C]Raclopride Scans for the
Inveon preclinical PET scanner: Application to the optimization of reconstruction methods
for dynamic studies
Abstract - A wide range of medical imaging applications benefits from the availability of
realistic ground truth data. In the case of positron emission tomography (PET), ground truth data
is crucial to validate processing algorithms and assessing their performances. The design of such
ground truth data often relies on Monte-Carlo simulation techniques. Since the creation of a large
dataset is not trivial both in terms of computing time and realism, we propose the OSSI-PET
database containing 350 simulated [11C]Raclopride dynamic scans for rats, created specifically
for the Inveon pre-clinical PET scanner. The originality of this database lies on the availability of
several groups of scans with controlled biological variations in the striata. Besides, each group
consists of a large number of realizations (i.e. noise replicates). We present the construction
methodology of this database using rat pharmacokinetic and anatomical models. A first
application using the OSSI-PET database is presented. Several commonly used reconstruction
techniques were compared in terms of image quality, accuracy and variability of the activity
estimates and of the computed kinetic parameters. The results showed that OP-OSEM3D
iterative reconstruction method outperformed the other tested methods. Analytical methods such
as FBP2D and 3DRP also produced satisfactory results. However, FORE followed by OSEM2D
reconstructions should be avoided. Beyond the illustration of the potential of the database, this
application will help scientists to understand the different sources of noise and bias that can
occur at the different steps in the processing and will be very useful for choosing appropriate
reconstruction methods and parameters.
Hybrid-Space SENSE Reconstruction for Simultaneous Multi-Slice MRI
Abstract - Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly
evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic
undersampling patterns to help mitigate the loss in signal-tonoise ratio (SNR) in SMS MRI. To
evaluate the performance of different undersampling patterns, a quantitative description of the
image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead
to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for

each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space
sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a
threedimensional representation of the SMS acquisition. Analytical SNR loss maps are derived
for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we
propose a matrix-decoding correction method that corrects the slicespecific Nyquist ghosting
artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space
SENSE reconstruction generates images with comparable quality to commonly used split-slice-
generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss
maps agree with those calculated by a Monte Carlo based method, but require less computation
time for high quality maps. The analytical maps enable a fair comparison between the
performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS
EPI images show that the matrixdecoding method performs better than the single-slice and
sliceaveraged Nyquist ghosting correction methods under the hybridspace SENSE reconstruction
framework.
Automatic Segmentation of Wrist Bones in CT Using a Statistical Wrist Shape+Pose Model
Abstract - Segmentation of the wrist bones in CT images has been frequently used in different
clinical applications including arthritis evaluation, bone age assessment and image-guided
interventions. The major challenges include non-uniformity and spongy textures of the bone
tissue as well as narrow interbone spaces. In this work, we propose an automatic wrist bone
segmentation technique for CT images based on a statistical model that captures the shape and
pose variations of the wrist joint across 60 example wrists at 9 different wrist positions. To
establish the correspondences across the training shapes at neutral positions, the wrist bone
surfaces are jointly aligned using a group-wise registration framework based on a Gaussian
Mixture Model. Principal component analysis is then used to determine the major modes of
shape variations. The variations in poses not only across the population but also across different
wrist positions are incorporated in two pose models. An intrasubject pose model is developed by
utilizing the similarity transforms at all wrist positions across the population. Further, an inter-
subject pose model is used to model the pose variations across different wrist positions. For
segmentation of the wrist bones in CT images, the developed model is registered to the edge

point cloud extracted from the CT volume through an expectation maximization based
probabilistic approach. Residual registration errors are corrected by application of a non-rigid
registration technique. We validate the proposed segmentation method by registering the wrist
model to a total of 66 unseen CT volumes of average voxel size of 0.38 mm. We report a mean
surface distance error of 0.33 mm and a mean Jaccard index of 0.86.
Erratum to “On Feature Motion Decorrelation in Ultrasound Speckle Tracking” [Feb 13
435-448]
Abstract - In the above paper (ibid., IEEE Trans. Med. Imag., vol. 32, no. 2, pp. 435-448, Feb.
2013), Section VII, the second sentence in the last paragraph should be corrected as "The
program takes about 5 hours for 2-D ultrasound images and is more than 9 times faster than the
CPU-based program in a standard PC."
IEEE Transactions on Medical Imaging (Feb. 2016)
Deep Independence Network Analysis of Structural Brain Imaging: Application to
Schizophrenia
Abstract - Linear independent component analysis (ICA) is a standard signal processing
technique that has been extensively used on neuroimaging data to detect brain networks with
coherent brain activity (functional MRI) or covarying structural patterns (structural MRI).
However, its formulation assumes that the measured brain signals are generated by a linear
mixture of the underlying brain networks and this assumption limits its ability to detect the
inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent
component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter
concentration in schizophrenia patients. For this biomedical application, we further addressed the
issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to
NICE, together with an appropriate control of the complexity of the model and the usage of a
proper approximation of the probability distribution functions of the estimated components. We
show that our results are consistent with previous findings in the literature, but we also
demonstrate that the incorporation of nonlinear associations in the data enables the detection of
spatial patterns that are not identified by linear ICA. Specifically, we show networks including

basal ganglia, cerebellum and thalamus that show significant differences in patients versus
controls, some of which show distinct nonlinear patterns.
Constrained Statistical Modelling of Knee Flexion from Multi-Pose Magnetic Resonance
Imaging
Abstract - Reconstruction of the anterior cruciate ligament (ACL) through arthroscopy is one of
the most common procedures in orthopaedics. It requires accurate alignment and drilling of the
tibial and femoral tunnels through which the ligament graft is attached. Although commercial
computerassisted navigation systems exist to guide the placement of these tunnels, most of them
are limited to a fixed pose without due consideration of dynamic factors involved in different
knee flexion angles. This paper presents a new model for intraoperative guidance of arthroscopic
ACL reconstruction with reduced error particularly in the ligament attachment area. The method
uses 3D preoperative data at different flexion angles to build a subjectspecific statistical model of
knee pose. To circumvent the problem of limited training samples and ensure physically
meaningful pose instantiation, homogeneous transformations between different poses and local-
deformation finite element modelling are used to enlarge the training set. Subsequently, an
anatomical geodesic flexion analysis is performed to extract the subject-specific flexion
characteristics. The advantages of the method were also tested by detailed comparison to
standard Principal Component Analysis (PCA), nonlinear PCA without training set enlargement,
and other state-of-the-art articulated joint modelling methods. The method yielded sub-
millimetre accuracy, demonstrating its potential clinical value.
Automatic Hookworm Detection in Wireless Capsule Endoscopy Images
Abstract - Wireless capsule endoscopy (WCE) has become a widely used diagnostic technique
to examine inflammatory bowel diseases and disorders. As one of the most common human
helminths, hookworm is a kind of small tubular structure with grayish white or pinkish semi-
transparent body, which is with a number of 600 million people infection around the world.
Automatic hookworm detection is a challenging task due to poor quality of images, presence of
extraneous matters, complex structure of gastrointestinal, and diverse appearances in terms of

color and texture. This is the first few works to comprehensively explore the automatic
hookworm detection for WCE images. To capture the properties of hookworms, the multi scale
dual matched filter is first applied to detect the location of tubular structure. Piecewise parallel
region detection method is then proposed to identify the potential regions having hookworm
bodies. To discriminate the unique visual features for different components of gastrointestinal,
the histogram of average intensity is proposed to represent their properties. In order to deal with
the problem of imbalance data, Rusboost is deployed to classify WCE images. Experiments on a
diverse and large scale dataset with 440K WCE images demonstrate that the proposed approach
achieves a promising performance and outperforms the stateof- the-art methods. Moreover, the
high sensitivity in detecting hookworms indicates the potential of our approach for future clinical
application.
Size-Invariant Detection of Cell Nuclei in Microscopy Images
Abstract - Accurate detection of individual cell nuclei in microscopy images is an essential and
fundamental task for many biological studies. In particular, multivariate fluorescence microscopy
is used to observe different aspects of cells in cultures. Manual detection of individual cell nuclei
by visual inspection is time consuming, and prone to induce subjective bias. This makes
automatic detection of cell nuclei essential for large-scale, objective studies of cell cultures. Blur,
clutter, bleed-through, imaging noise and touching and partially overlapping nuclei with varying
sizes and shapes make automated detection of individual cell nuclei a challenging task using
image analysis. In this paper we propose a new automated method for fast and robust detection
of individual cell nuclei based on their radial symmetric nature in fluorescence in-situ
hybridization (FISH) images obtained via confocal microscopy. The main contributions are two-
fold: (1) This work presents a more accurate cell nucleus detection system using the fast radial
symmetry transform (FRST). (2) The proposed cell nucleus detection system is robust against
most occlusions and variations in size and moderate shape deformations. We evaluate the
performance of the proposed algorithm using precision/recall rates, F-score and root-
meansquared distance (RMSD) and show that our algorithm provides improved detection
accuracy compared to existing algorithms.

In Vivo Electrical Conductivity Contrast Imaging in a Mouse Model of Cancer Using High-
frequency Magnetoacoustic Tomography with Magnetic Induction (hfMAT-MI)
Abstract - Cancerous tissues have electrical-conductivity signatures different from normal
tissues, which contain potentially useful information for early detection. Despite recent
advancements in electrical-conductivity imaging and its applications, imaging electrical
conductivities with high spatial resolution remains a challenge for non-invasive diagnosis of
early-stage cancer. Among the various electrical-conductivity imaging methods,
magnetoacoustic tomography with magnetic induction (MAT-MI) is a promising technology for
non-invasive detection of breast cancer. However, previous efforts to use MAT-MI for cancer
imaging have suffered due to insufficient spatial resolution. In this work, we have developed a
high-frequency MAT-MI (hfMAT-MI) system with a 2-D spatial resolution of 1 mm, a
significant improvement over previous methods. Furthermore, we demonstrated the performance
of this method using an in vivo cancer model in nude mice with human breast xenograft
hindlimb tumors. hfMAT-MI was able to resolve not only the boundaries between cancerous and
healthy tissues, but also the tumors’ internal structures. Importantly, we were able to track a
growing tumor using our hfMAT-MI method for the first time in an in vivo mouse model,
demonstrating the promise of this magneto-acoustic imaging system for effective detection and
diagnosis of early-stage breast cancer.
IEEE Transactions on Medical Imaging (April 2016)
Association between Changes in Mammographic Image Features and Risk for Near-term
Breast Cancer Development
Abstract - The purpose of this study is to develop and test a new computerized model for
predicting near-term breast cancer risk based on quantitative assessment of bilateral
mammographic image feature variations in a series of negative full-field digital mammography
(FFDM) images. The retrospective dataset included series of four sequential FFDM
examinations of 335 women. The last examination in each series (“current”) and the three most
recent “prior” examinations were obtained. All “prior” examinations were interpreted as negative
during the original clinical image reading, while in the “current” examinations 159 cancers were
detected and pathologically verified and 176 cases remained cancer-free. From each image, we

initially computed 158 mammographic density, structural similarity, and texture based image
features. The absolute subtraction value between the left and right breasts was selected to
represent each feature. We then built three support vector machine (SVM) based risk models,
which were trained and tested using a leave-one-case-out based cross-validation method. The
actual features used in each SVM model were selected using a nested stepwise regression
analysis method. The computed areas under receiver operating characteristic curves
monotonically increased from 0.666±0.029 to 0.730±0.027 as the time-lag between the “prior” (3
to 1) and “current” examinations decreases. The maximum adjusted odds ratios were 5.63, 7.43,
and 11.1 for the three “prior” (3 to 1) sets of examinations, respectively. This study demonstrated
a positive association between the risk scores generated by a bilateral mammographic feature
difference based risk model and an increasing trend of the near-term risk for having
mammography-detected breast cancer.
Spatial angular compounding of photoacoustic images
Abstract - Photoacoustic (PA) images utilize pulsed lasers and ultrasound transducers to
visualize targets with higher optical absorption than the surrounding medium. However, they are
susceptible to acoustic clutter and background noise artifacts that obfuscate biomedical structures
of interest. We investigated three spatial-angular compounding methods to improve PA image
quality for biomedical applications, implemented by combining multiple images acquired as an
ultrasound probe was rotated about the elevational axis with the laser beam and target fixed.
Compounding with conventional averaging was based on the pose information of each PA
image, while compounding with weighted and selective averaging utilized both the pose and
image content information. Weighted-average compounding enhanced PA images with the least
distortion of signal size, particularly when there were large (i.e. 2:5 mm and 7) perturbations
from the initial probe position. Selective-average compounding offered the best improvement in
image quality with up 181, 1665, and 1568 times higher contrast, CNR, and SNR, respectively,
compared to the mean values of individual PA images. The three presented spatial compounding
methods have promising potential to enhance image quality in multiple photoacoustic
applications.

High-frame-rate echocardiography using coherent compounding with Doppler-based
motion-compensation
Abstract - High-frame-rate ultrasonography based on coherent compounding of unfocused
beams can potentially transform the assessment of cardiac function. As it requires successive
waves to be combined coherently, this approach is sensitive to high-velocity tissue motion. We
investigated coherent compounding of tilted diverging waves, emitted from a 2.5 MHz clinical
phased array transducer. To cope with high myocardial velocities, a triangle transmit sequence of
diverging waves is proposed, combined with tissue Doppler imaging to perform motion
compensation (MoCo). The compound sequence with integrated MoCo was adjusted from
simulations and was tested in vitro and in vivo. Realistic myocardial velocities were analyzed in
an in vitro spinning disk with anechoic cysts. While a 8 dB decrease (no motion vs. high motion)
was observed without MoCo, the contrast- to-noise ratio of the cysts was preserved with the
MoCo approach. With this method, we could provide high-quality in vivo B-mode cardiac
images with tissue Doppler at 250 frames per second. Although the septum and the anterior
mitral leaflet were poorly apparent without MoCo, they became well perceptible and well
contrasted with MoCo. The septal and lateral mitral annulus velocities determined by tissue
Doppler were concordant with those measured by pulsed-wave Doppler with a clinical scanner
(r2 = 0.7, y = 0.9 x + 0.5, N = 60). To conclude, highcontrast echocardiographic B-mode and
tissue Doppler images can be obtained with diverging beams when motion compensation is
integrated in the coherent compounding process.
Spatially Variant Resolution Modelling for Iterative List-Mode PET Reconstruction
Abstract - A spatially variant resolution modelling technique is presented which estimates the
system matrix on-the-fly during iterative list-mode reconstruction. This is achieved by
redistributing the endpoints of each list-mode event according to derived probability density
functions describing the detector response function and photon acollinearity, at each iteration
during the reconstruction. Positron range is modelled using an imagebased convolution. When
applying this technique it is shown that the maximum-likelihood expectation maximisation
(MLEM) algorithm is not compatible with an obvious acceleration strategy. The image space
reconstruction algorithm (ISRA), however, after being adapted to a list-mode based

implementation, is wellsuited to the implementation of the model. A comparison of ISRA and
MLEM is made to confirm that ISRA is a suitable alternative to MLEM. We demonstrate that
this model agrees with measured point spread functions and we present results showing an
improvement in resolution recovery, particularly for off-centre objects, as compared to
commercially available software, as well as the standard technique of using a stationary Gaussian
convolution to model the resolution, for equal iterations and only slightly higher computation
time.
Improvements in RF shimming in high field MRI using high permittivity materials with
low order pre-fractal geometries
Abstract - Ultra-high field MRI is an area of great interest for clinical research and basic science
due to the increased signal-to-noise, spatial resolution and magnetic-susceptibility-based
contrast. However, the fact that the electromagnetic wavelength in tissue is comparable to the
relevant body dimensions means that the uniformity of the excitation field is much poorer than at
lower field strengths. In addition to techniques such as transmit arrays, one simple but effective
method to counteract this effect is to use high permittivity “pads”. Very high permittivities
enable thinner, flexible pads to be used, but the limiting factor is wavelength effects within the
pads themselves, which can lead to image artifacts. So far, all studies have used simple
continuous rectangular/circular pad geometries. In this work we investigate how the wavelength
effects can be partially mitigated utilizing shaped pad with holes. Several arrangements have
been simulated, including low order pre-fractal geometries, which maintain the overall coverage
of the pad, but can provide better image homogeneity in the region of interest or higher
sensitivity depending on the setup. Experimental data in the form of in-vivo human images at 7 T
were acquired to validate the simulation results.
Detailed Evaluation of Five 3D Speckle Tracking Algorithms using Synthetic
Echocardiographic Recordings

A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred
to as 3D speckle tracking techniques, are available from academia and industry. Although the
benefits of single methods over alternative ones have been reported in separate publications, the
intrinsic differences in the data and definitions used makes it hard to compare the relative
performance of different solutions. To address this issue, we have recently proposed a
framework to simulate realistic 3D echocardiographic recordings and used it to generate a
common set of ground-truth data for 3D speckle tracking algorithms, which was made available
online. The aim of this study was therefore to use the newly developed database to contrast non-
commercial speckle tracking solutions from research groups with leading expertise in the field.
The five techniques involved cover the most representative families of existing approaches,
namely block-matching, radio-frequency tracking, optical flow and elastic image registration.
The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the
obtained strain measurements to diagnose pathology was also tested for ischemia and
dyssynchrony.
IEEE Transactions on Medical Imaging (March 2016)
Aortic Valve Tract Segmentation from 3D-TEE Using Shape-Based B-spline Explicit
Active Surfaces
Abstract - A novel semi-automatic algorithm for aortic valve (AV) wall segmentation is
presented for 3D transesophageal echocardiography (TEE) datasets. The proposed methodology
uses a 3D cylindrical formulation of the B-spline Explicit Active Surfaces (BEAS) framework in
a dual-stage energy evolution process, comprising a threshold-based and a localized regionbased
stage. Hereto, intensity and shape-based features are combined to accurately delineate the AV
wall from the ascending aorta (AA) to the left ventricular outflow tract (LVOT). Shapeprior
information is included using a profile-based statistical shape model (SSM), and embedded in
BEAS through two novel regularization terms: one confining the segmented AV profiles to
shapes seen in the SSM (hard regularization) and another penalizing according to the profile’s
degree of likelihood (soft regularization). The proposed energy functional takes thus advantage
of the intensity data in regions with strong image content, while complementing it with shape
knowledge in regions with nearly absent image data. The proposed algorithm has been validated
in 20 3D-TEE datasets with both stenotic and non-stenotic valves. It was shown to be accurate,

robust and computationally efficient, taking less than 1 second to segment the AV wall from the
AA to the LVOT with an average accuracy of 0.78 mm. Semi-automatically extracted
measurements at four relevant anatomical levels (LVOT, aortic annulus, sinuses of Valsalva and
sinotubular junction) showed an excellent agreement with experts’ ones, with a higher
reproducibility than manually-extracted measures.
Abnormality detection via iterative deformable registration and basis-pursuit
decomposition
Abstract - We present a generic method for automatic detection of abnormal regions in medical
images as deviations from a normative data base. The algorithm decomposes an image, or more
broadly a function defined on the image grid, into the superposition of a normal part and a
residual term. A statistical model is constructed with regional sparse learning to represent
normative anatomical variations among a reference population (e.g. healthy controls), in
conjunction with a Markov Random Field regularization that ensures mutual consistency of the
regional learning among partially overlapping image blocks. The decomposition is performed in
a principled way so that the normal part fits well with the learned normative model, while the
residual term absorbs pathological patterns, which may then be detected through a statistical
significance test. The decomposition is applied to multiple image features from an individual
scan, detecting abnormalities using both intensity and shape information. We form an iterative
scheme that interleaves abnormality detection with deformable registration, gradually improving
robustness of the spatial normalization and precision of the detection. The algorithm is evaluated
with simulated images and clinical data of brain lesions, and is shown to achieve robust
deformable registration and localize pathological regions simultaneously. The algorithm is also
applied on images from Alzheimer’s Disease patients to demonstrate the generality of the
method.
A Hybrid Approach for Segmentation and Tracking of Myxococcus xanthus Swarms
Abstract - Cell segmentation and motion tracking in timelapse images are fundamental problems
in computer vision, and are also crucial for various biomedical studies. Myxococcus xanthus is a

type of rod-like cells with highly coordinated motion. The segmentation and tracking of M.
xanthus are challenging, because cells may touch tightly and form dense swarms that are difficult
to identify individually in an accurate manner. The known cell tracking approaches mainly fall
into two frameworks, detection association and model evolution, each having its own advantages
and disadvantages. In this paper, we propose a new hybrid framework combining these two
frameworks into one and leveraging their complementary advantages. Also, we propose an active
contour model based on the Ribbon Snake, which is seamlessly integrated with our hybrid
framework. Evaluated by 10 different datasets, our approach achieves considerable improvement
over the state-of-the-art cell tracking algorithms on identifying complete cell trajectories, and
higher segmentation accuracy than performing segmentation in individual 2D images.
A projection algorithm for gradient waveforms design in Magnetic Resonance Imaging
Abstract - Collecting the maximal amount of information in a given scanning time is a major
concern in Magnetic Resonance Imaging (MRI) to speed up image acquisition. The hardware
constraints (gradient magnitude, slew rate, ...), physical distortions (e.g., off-resonance effects)
and sampling theorems (Shannon, compressed sensing) must be taken into account
simultaneously, which makes this problem extremely challenging. To date, the main approach to
design gradient waveform has consisted of selecting an initial shape (e.g. spiral, radial lines, ...)
and then traversing it as fast as possible using optimal control. In this paper, we propose an
alternative solution which first consists of defining a desired parameterization of the trajectory
and then of optimizing for minimal deviation of the sampling points within gradient constraints.
This method has various advantages. First, it better preserves the density of the input curve
which is critical in sampling theory. Second, it allows to smooth high curvature areas making the
acquisition time shorter in some cases. Third, it can be used both in the Shannon and CS
sampling theories. Last, the optimized trajectory is computed as the solution of an efficient
iterative algorithm based on convex programming. For piecewise linear trajectories, as compared
to optimal control reparameterization, our approach generates a gain in scanning time of 10% in
echo planar imaging while improving image quality in terms of signal-tonoise ratio (SNR) by
more than 6 dB. We also investigate original trajectories relying on traveling salesman problem
solutions. In this context, the sampling patterns obtained using the proposed projection algorithm

are shown to provide significantly better reconstructions (more than 6 dB) while lasting the same
scanning time.
Sparse Multi-Response Tensor Regression for Alzheimer's Disease Study with Multivariate
Clinical Assessments
Abstract - Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative
disorder that has recently seen serious increase in the number of affected subjects. In the last
decade, neuroimaging has been shown to be a useful tool to understand AD and its prodromal
stage, amnestic mild cognitive impairment (MCI). The majority of AD/MCI studies have focused
on disease diagnosis, by formulating the problem as classification with a binary outcome of
AD/MCI or healthy controls. There have recently emerged studies that associate image scans
with continuous clinical scores that are expected to contain richer information than a binary
outcome. However, very few studies aim at modeling multiple clinical scores simultaneously,
even though it is commonly conceived that multivariate outcomes provide correlated and
complementary information about the disease pathology. In this article, we propose a sparse
multi-response tensor regression method to model multiple outcomes jointly as well as to model
multiple voxels of an image jointly. The proposed method is particularly useful to both infer
clinical scores and thus disease diagnosis, and to identify brain subregions that are highly
relevant to the disease outcomes. We conducted experiments on the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) dataset, and showed that the proposed method enhances the
performance and clearly outperforms the competing solutions.
Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy
Abstract - We have developed a technique to study how good computers can be at diagnosing
gastrointestinal lesions from regular (white light and narrow banded) colonoscopic videos
compared to two levels of clinical knowledge (expert and beginner). Our technique includes a
novel tissue classification approach which may save clinician’s time by avoiding
chromoendoscopy, a time-consuming staining procedure using indigo carmine. Our technique
also discriminates the severity of individual lesions in patients with many polyps, so that the
gastroenterologist can directly focus on those requiring polypectomy. Technically, we have

designed and developed a framework combining machine learning and computer vision
algorithms, which performs a virtual biopsy of hyperplastic lesions, serrated adenomas and
adenomas. Serrated adenomas are very difficult to classify due to their mixed/hybrid nature and
recent studies indicate that they can lead to colorectal cancer through the alternate serrated
pathway. Our approach is the first step to avoid systematic biopsy for suspected hyperplastic
tissues. We also propose a database of colonoscopic videos showing gastrointestinal lesions with
ground truth collected from both expert image inspection and histology. We not only compare
our system with the expert predictions, but we also study if the use of 3D shape features
improves classification accuracy, and compare our technique’s performance with three
competitor methods.
Quantitative Susceptibility Mapping using Structural Feature based Collaborative
Reconstruction (SFCR) in the Human Brain
Abstract - The reconstruction of MR quantitative susceptibility mapping (QSM) from local
phase measurements is an ill posed inverse problem and different regularization strategies
incorporating a priori information extracted from magnitude and phase images have been
proposed. However, the anatomy observed in magnitude and phase images does not always
coincide spatially with that in susceptibility maps, which could give erroneous estimation in the
reconstructed susceptibility map. In this paper, we develop a structural feature based
collaborative reconstruction (SFCR) method for QSM including both magnitude and
susceptibility based information. The SFCR algorithm is composed of two consecutive steps
corresponding to complementary reconstruction models, each with a structural feature based l1
norm constraint and a voxel fidelity based l2 norm constraint, which allows both the structure
edges and tiny features to be recovered, whereas the noise and artifacts could be reduced. In the
M-step, the initial susceptibility map is reconstructed by employing a k-space based compressed
sensing model incorporating magnitude prior. In the S-step, the susceptibility map is fitted in
spatial domain using weighted constraints derived from the initial susceptibility map from the M-
step. Simulations and in vivo human experiments at 7T MRI show that the SFCR method
provides high quality susceptibility maps with improved RMSE and MSSIM. Finally, the

susceptibility values of deep gray matter are analyzed in multiple head positions, with the supine
position most approximate to the gold standard COSMOS result.
Simultaneous Quantitative Imaging of Electrical Properties and Proton Density from B1
Maps Using MRI
Abstract - Electrical conductivity and permittivity of biological tissues are important diagnostic
parameters and are useful for calculating subject-specific specific absorption rate distribution. On
the other hand, water proton density also has clinical relevance for diagnosis purposes. These
two kinds of tissue properties are inevitably associated in the technique of electrical properties
tomography (EPT), which can be used to map in vivo electrical properties based on the measured
B1 field distribution at Larmor frequency using magnetic resonance imaging (MRI). The signal
magnitude in MR images is locally proportional to both the proton density of tissue and the
receive B1 field; this is a source of artifact in receive B1-based EPT reconstruction because these
two quantities cannot easily be disentangled. In this study, a new method was proposed for
simultaneously extracting quantitative conductivity, permittivity and proton density from the
measured magnitude of transmit B1 field, proton density-weighted receive B1 field, and
transceiver phase, in a multi-channel radiofrequency (RF) coil using MRI, without specific
assumptions to derive the proton density distribution. We evaluated the spatial resolution,
sensitivity to contrast, and accuracy of the method using numerical simulations of B1 field in a
phantom and in a realistic human head model. Using the proposed method, conductivity,
permittivity and proton density were then experimentally obtained ex vivo in a pork tissue
sample on a 7T MRI scanner equipped with a 16-channel microstrip transceiver RF coil.
System Characterization of a Highly Integrated Preclinical Hybrid MPI-MRI Scanner
Abstract - Magnetic Particle Imaging (MPI) is a novel tracer-based in vivo imaging modality
allowing quantitative measurements of the spatial distributions of superparamagnetic iron oxide
(SPIO) nanoparticles in three dimensions (3D) and in real time using electromagnetic fields.
However, MPI lacks the detection of morphological information which makes it difficult to
unambiguously assign spatial SPIO distributions to actual organ structures. To compensate for
this, a preclinical highly integrated hybrid system combining MPI and Magnetic Resonance

Imaging (MRI) has been designed and gets characterized in this work. This hybrid MPI-MRI
system offers a high grade of integration with respect to its hard- and software and enables
sequential measurements of MPI and MRI within one seamless study and without the need for
object repositioning. Therefore, time-resolved measurements of SPIO distributions acquired with
MPI as well as morphological and functional information acquired with MRI can be combined
with high spatial coregistration accuracy. With this initial phantom study, the feasibility of a
highly integrated MPI-MRI hybrid systems has been proven successfully. This will enable dual-
modal in vivo preclinical investigations of mice and rats with high confidence of success,
offering the unique feature of precise MPI FOV planning on the basis of MRI data and vice
versa.
Mixed Confidence Estimation for Iterative CT Reconstruction
Abstract - Dynamic (4D) CT imaging is used in a variety of applications, but the two major
drawbacks of the technique are its increased radiation dose and longer reconstruction time. Here
we present a statistical analysis of our previously proposed Mixed Confidence Estimation (MCE)
method that addresses both these issues. This method, where framed iterative reconstruction is
only performed on the dynamic regions of each frame while static regions are fixed across
frames to a composite image, was proposed to reduce computation time. In this work, we
generalize the previous method to describe any application where a portion of the image is
known with higher confidence (static, composite, lower-frequency content, etc.) and a portion of
the image is known with lower confidence (dynamic, targeted, etc). We show that by splitting the
image space into higher and lower confidence components, MCE can lower the estimator
variance in both regions compared to conventional reconstruction. We present a theoretical
argument for this reduction in estimator variance and verify this argument with proof-of-
principle simulations. We also propose a fast approximation of the variance of images
reconstructed with MCE and confirm that this approximation is accurate compared to analytic
calculations of and multirealization image variance. This MCE method requires less computation
time and provides reduced image variance for imaging scenarios where portions of the image are
known with more certainty than others allowing for potentially reduced radiation dose and/or
improved dynamic imaging.

Bayesian Community Detection in the Space of Group-Level Functional Differences
Abstract - We propose a unified Bayesian framework to detect both hyper- and hypo-active
communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs
that exhibit population-level differences in functional synchrony between a control and clinical
group. We derive a variational EM algorithm to solve for the latent posterior distributions and
parameter estimates, which subsequently inform us about the afflicted network topology. We
demonstrate that our method provides valuable insights into the neural mechanisms underlying
social dysfunction in autism, as verified by the Neurosynth metaanalytic database. In contrast,
both univariate testing and community detection via recursive edge elimination fail to identify
stable functional communities associated with the disorder.
Design Features and Mutual Compatibility Studies of the Time-of-Flight PET Capable GE
SIGNA PET/MR System
Abstract - A recent entry into the rapidly evolving field of integrated PET/MR scanners is
presented in this paper: a whole body hybrid PET/MR system (SIGNA PET/MR, GE Healthcare)
capable of simultaneous acquisition of both time-of-flight (TOF) PET and high resolution MR
data. The PET ring was integrated into an existing 3T MR system resulting in a (patient) bore
opening of 60 cm diameter, with a 25 cm axial FOV. The PET ring is placed between the
shielded RF body coil and the gradient coils. The PET detectors are based on silicon
photomultipliers coupled to lutetium based scintillators. PET performance was evaluated both on
a standalone PET ring and on the same detector integrated into the MR system, to assess the
level of mutual interference between both subsystems. In both configurations we obtained
detector performance data. To evaluate PET image quality and image resolution, PET data was
acquired using the NEMA IQ phantom with MR idle and with MR active. Impact of PET on MR
IQ was assessed by comparing SNR with PET acquisition on and off. B0 and B1 homogeneities
were acquired before and after the integration of the PET ring inside the magnet. In vivo brain
and whole body head-to-thighs data was acquired to demonstrate clinical image quality. PET
detector performance was virtually unaffected by integration into the MR system. The global
energy resolution was within 2% (10.3% vs. 10.5%). The mean system timing resolution showed

a maximum change of < 3% (385 ps vs. 394 ps) when measured outside MR and during
simultaneous PET/MRI acquisitions. The timing resolution measurements acquired as a function
of source activity indicate minimal impact of MR activity. PET images from NEMA IQ phantom
with and without MR ON were visually comparable. The SNR of MR images showed small
degradations (< 5%) when the PET ring was integrated inside the MR system compared to the
baseline. After the PET ring installation, the magnet was shimmed to equivalent homogeneity
level.
Geometrical calibration of X-ray imaging with RGB cameras for 3D reconstruction
Abstract - We present a methodology to recover the geometrical calibration of conventional X-
ray settings with the help of an ordinary video camera and visible fiducials that are present in the
scene. After calibration, equivalent points of interest can be easily identifiable with the help of
the epipolar geometry. The same procedure also allows the measurement of real anatomic
lengths and angles and obtains accurate 3D locations from image points. Our approach
completely eliminates the need for X-ray-opaque reference marks (and necessary supporting
frames) which can sometimes be invasive for the patient, occlude the radiographic picture, and
end up projected outside the imaging sensor area in oblique protocols. Two possible frameworks
are envisioned: a spatially shifting X-ray anode around the patient/object and a moving patient
that moves/rotates while the imaging system remains fixed. As a proof of concept, experiences
with a device under test (DUT), an anthropomorphic phantom and a real brachytherapy session
have been carried out. The results show that it is possible to identify common points with a
proper level of accuracy and retrieve three-dimensional locations, lengths and shapes with a
millimetric level of precision. The presented approach is simple and compatible with both
current and legacy widespread diagnostic X-ray imaging deployments and it can represent a
good and inexpensive alternative to other radiological modalities like CT.
Real-time Model-based Inversion in Cross-sectional Optoacoustic Tomography
Abstract - Analytical (closed-form) inversion schemes have been the standard approach for
image reconstruction in optoacoustic tomography due to their fast reconstruction abilities and
low memory requirements. Yet, the need for quantitative imaging and artifact reduction has led

to the development of more accurate inversion approaches, which rely on accurate forward
modeling of the optoacoustic wave generation and propagation. In this way, multiple
experimental factors can be incorporated, such as the exact detection geometry, spatio-temporal
response of the transducers, and acoustic heterogeneities. The modelbased inversion commonly
results in very large sparse matrix formulations that require computationally extensive and
memory demanding regularization schemes for image reconstruction, hindering their effective
implementation in real-time imaging applications. Herein, we introduce a new discretization
procedure for efficient model-based reconstructions in two-dimensional optoacoustic
tomography that allows for parallel implementation on a graphics processing unit (GPU) with a
relatively low numerical complexity. By on-the-fly calculation of the model matrix in each
iteration of the inversion procedure, the new approach results in imaging frame rates exceeding
10Hz, thus enabling real-time image rendering using the model-based approach.
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