this slide sharer contents are basic principle of CT fluoroscopy , software and hardware parts of equipment and image aqua cation and radiation dose comparison and videos related to equipment .
Rad 206 p05 Fundamentals of Imaging - Fluoroscopysehlawi
college of health sciences, fundamentals of imaging, image formation, radiography, radiologic, radiologic science, radiologic technologist, university of bahrain
this slide sharer contents are basic principle of CT fluoroscopy , software and hardware parts of equipment and image aqua cation and radiation dose comparison and videos related to equipment .
Rad 206 p05 Fundamentals of Imaging - Fluoroscopysehlawi
college of health sciences, fundamentals of imaging, image formation, radiography, radiologic, radiologic science, radiologic technologist, university of bahrain
Image reconstruction in CT is mostly a mathematical process however, this presentation tries to explain the complicated process of image reconstruction in a visual way, mainly focusing om Filtered back projection, Iterative Reconstruction and AI based image reconstruction.
CT is one of the highest contributor for medical radiation exposure to patients. Some common CT dose descriptors and dose optimizations methods are briefly described in this presentation.
Quality Assurance Programme in Computed TomographyRamzee Small
Introduction to Computed Tomography
Basic description of the components of a CT System
Introduction to Quality Assurance
Quality Assurance and Quality Control Tests in Computed Tomography base on frequency
Objective of QA/QC Test
This presentation is a starter for folks interested in the implementation and application of compressed sensing (CS) MRI. It includes a Matlab demo and list of well-known resources for CS MRI.
Image reconstruction in CT is mostly a mathematical process however, this presentation tries to explain the complicated process of image reconstruction in a visual way, mainly focusing om Filtered back projection, Iterative Reconstruction and AI based image reconstruction.
CT is one of the highest contributor for medical radiation exposure to patients. Some common CT dose descriptors and dose optimizations methods are briefly described in this presentation.
Quality Assurance Programme in Computed TomographyRamzee Small
Introduction to Computed Tomography
Basic description of the components of a CT System
Introduction to Quality Assurance
Quality Assurance and Quality Control Tests in Computed Tomography base on frequency
Objective of QA/QC Test
This presentation is a starter for folks interested in the implementation and application of compressed sensing (CS) MRI. It includes a Matlab demo and list of well-known resources for CS MRI.
The talk gives an overview on pulse sequence design components in general and k-space trajectories in particular. Design problems are solved step by step for multiple trajectories with examples of applications. A few simulations of artifacts from these trajectories are also illustrated
Getting started with Matlab by Hannah Dotson, Vikram Kodibagkar laboratorySairam Geethanath
These slides are put together by Hannah Dotson, a STARS program intern at the Kodibagkar laboratory at UTSW. Folks new to Matlab and its usage at MIRC can find this tutorial material handy. Thanks Hannah!
This is a starter-presentation for folks trying to get their feet wet with medical image processing in a Matlab environment. It discusses certain simple image processing algorithms employed in the context of MR imaging with examples.
This presentation focuses on the role of MRI in biomedical engineering research with examples of a few global studies and the workflow associated with a MR academic project
This talk delvers an hour-long overview of MR physics focusing on multiple topics at an introductory level, proceeds to provide tools that are open source based, for MR enthusiasts and beginners
Proposing an Accelerated Magnetic Resonance Spectroscopic Imaging Acquisition...Uzay Emir
Proposing an Accelerated Magnetic Resonance Spectroscopic Imaging Acquisition as a Promising Tool to Investigate Heterogeneous Renal Cell Carcinoma: Feasibility and Reliability Study at 3 T
Comparison Between 2-Hydroxyglutarate Detection Methods at 3T
False-Positive Measurement at 2-Hydroxyglutarate MR Spectroscopy in Isocitrate Dehydrogenase Wild-Type
Non-invasive detection of 2-hydroxyglutarate in IDH-mutated gliomas using
Advances and Challenges in Assessing 2-Hydroxyglutarate in Gliomas by Magnetic Resonance Spectroscopy
Detection of oncogenic IDH1 mutations using magnetic resonance spectroscopy of 2-hydroxyglutarate
standardization
Across-vendor
semi-LASER
single-voxel
MRS
3T
GABA spectroscopy
edited GABA 1H MEGA-PRESS spectra
GABA-edited
Magnetic Resonance Spectroscopy, MRI, Human Connectome, 2-HG, 2-hydroxyglutarate, zoom, zoom MRSI, reduced field of View, rFOV, Cerebellum, High-resolution, IDH, Isocitrate, IDH1, IDH2, Cancer, Glioma, Parcellation, Macro Anatomical
Functional
Myeloarchitectonic
functional MRS
MR Spectroscopy Study Group
fMRI - fMRSI
glutamate
gaba
fMRS
ISMRM
standardization
Across-vendor
semi-LASER
single-voxel
MRS
3T
human brain mapping
MRI biomarkers for the spinal cord, webinar with Dr. Julien Cohen-Adad.jcohenadad
The video recording is available here: 👉 https://youtu.be/3_xJCSqu5xs
Neuroimaging MRI biomarkers include volumetric measures, microstructure imaging such as diffusion-weighted imaging and magnetization transfer, and functional MRI. These biomarkers nicely complement clinical indices and provide objective means to monitor disease evolution in patients. While being very popular in the brain, MRI biomarkers have been slow to translate to the spinal cord because of the technical difficulties in imaging this organ. In this talk, I will present state-of-the-art solutions for the acquisition and automatic analysis of MRI biomarkers in the spinal cord. During the first part of the talk, I will talk about a recent initiative to standardize acquisition protocol in the spinal cord: the spine-generic project (https://spine-generic.rtfd.io/). During the second part of the talk, we will go through some of the main features of the Spinal Cord Toolbox (SCT, http://spinalcordtoolbox.com/), a popular open-source software package which performs automatic analysis of spinal cord MRI biomarkers.
Finally, we will show example applications of these advanced acquisition and processing methods in various multi-center studies and applied to a variety of diseases: multiple sclerosis, amyotrophic lateral sclerosis, degenerative cervical myelopathy, chronic pain and cancer.
Dr. Cohen-Adad is an Associate Professor at Polytechnique Montreal, Adjunct Professor in the Department of Neurosciences at University of Montreal, Associate Director of the Neuroimaging Functional Unit at the University of Montreal, and Canada Research Chair in Quantitative Magnetic Resonance Imaging. His research focuses on advancing hardware and software MRI methods to help characterizing pathologies in the central nervous system, with a particular focus in the spinal cord. He has published over 130 articles on that topic (https://scholar.google.ca/). Dr. Cohen-Adad also dedicates efforts in bringing the community together by developing open source solutions and by organizing yearly workshops via the www.spinalcordmri.org platform, which he initiated.
Links to publications and work of Dr. Julien Cohen-Adad:
https://pubmed.ncbi.nlm.nih.gov/33039...
https://pubmed.ncbi.nlm.nih.gov/32572...
https://scholar.google.ca/citations?u...
https://spine-generic.rtfd.io/
Image registration and data fusion techniques.pptx latest saveM'dee Phechudi
Medical imaging is the fundamental tool in conformal radiation therapy. Almost every aspect of patient management involves some form of two or three dimensional image data acquired using one or more modality.
Image data are now used for diagnosis and staging, for treatment planning and delivery and for monitoring patients after therapy.
Begoña Caballero-'La visión computacional se encuentra con la medicina'Fundación Ramón Areces
El 14 de noviembre de 2016, la Fundación Ramón Areces organizó un Simposio Internacional sobre tecnología aplicada al mundo de la medicina de la mano del Instituto Tecnológico de Massachusetts (MIT) y de la Fundación mVision. Este encuentro llevó por título 'La visión computacional se encuentra con la medicina'. Durante esta jornada, se analizó el impacto que están teniendo las nuevas técnicas de imagen en alta resolución para el diagnóstico de todo tipo de enfermedades.
I reviewed 3 papers at 'SNU TF Study Group' in Korea.
3 papers tried to solve segmentation problems in medical images with Deep Learning.
Deep Learning 을 이용하여 의료 영상에서 Segmentation 문제를 풀고자 한 3가지 논문을 리뷰하였습니다. :)
The Computed Tomography (CT) dose output of some selected hospitals in the Federal capital Territory, Abuja, Nigeria have been determined by calculating the Effective doses of CT Chest and Abdomen-Pelvis of selected hospitals and compared its average with the Mean Reference Dose of CT Chest and Abdomen-Pelvis from four hospitals in the Federal Capital Territory, Abuja, Nigeria. Effective Dose and Scan type were extracted from the CT Chest and Abdomen-Pelvis examinations recorded. The Effective Dose of each patient undergoing the Chest and Abdomen-Pelvis examinations were calculated using the coefficient factor and the DLP values. Patients’ CT dose data from the ages of 18 to 60years from each of the 4 centres for each study type from January, 2013 to December, 2014 was extracted. A total of 112 patients’ CT dose data was extracted. Chest CT Effective Dose ranged from 9.0 to 34.0mSv, while Abdomen-Pelvis CT Effective Dose ranged from 15.9 to 61.0 for all the Centres in Federal Capital Territory, Abuja. This is higher than the recommended Reference Effective Dose range for CT Chest which is from 5 – 7mSv. and for CT Abdomen-Pelvis is from 8 – 14mSv. The mean effective dose from the Chest CT is 21.8mSv and from the Abdomen-Pelvis is 31.9mSv.
Determine the amount of human body components fat by x-ray spectral information using MARS spectral X-ray scanner and also, study of the x-ray spectral information.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
11. ¡ CS: what is it all about?
¡ Matlab demo
¡ Steps ahead on CS
¡ Resources on CS
12. ¡ Number of non-zero coefficients in a data
vector
¡ Importance due to conservation of energy
¡ Sinusoidal signal for 3 hours in time
domain or frequency domain?
¡ Move towards time-frequency transforms
13. ¡ Childlike question on compression
¡ Acceleration technique involving both
acquisition and reconstruction paradigms
¡ Technically challenging, pragmatically
feasible and clinically valuable
14. • Good data quality but takes a long time!
• Hence, may not be suitable for certain imaging protocols.
• Limits spatial and temporal resolutions
• Higher spatial resolution aids in morphological analysis of
tumors – breast DCE-MRI
• Temporal resolution is important for accurate
pharmacokinetic analysis.
• Several approaches like keyhole, parallel imaging and other
fast sequences have been used.2D FFT
2D
IFFT
50
100
150
200
X
1 0 Data provided by Baek
16. Complete data reconstruction
Wavelet
Transform
Data provided by Baek
[1] David L. Donoho, IEEE Transactions on Information theory, Vol.52, no. 4, April 2006
[2] Candes, E.J. et al., IEEE Transactions on Information theory, Vol.52, no.2, Feb. 2006
• Most objects in nature are approximately sparse in a transformed domain.
• Utilize above concept to obtain very few measurements and yet reconstruct
with high fidelity [1,2]
Only 33% of complete
data
X
17. ¡ Generate a 2D phantom
¡ Cartesian undersampling of data
¡ Obtain undersampled data and zfwdc recon
¡ Choice of ROI if required for diagnostic evaluation purposes
¡ Recon params, post L-curve optimization
¡ Nonlinear conjugate gradient iterative reconstruction
¡ Comparative quality
20. ¡ Every MRI method:
§ Angiography
§ DWI/DTI/SWI/DCE-MRI/ASL
§ fMRI/MRSI/CMR
§ ….
¡ Because MRI is inherently a slow acquisition
process, mostly dictated by the physics of
acquisition
¡ Magnetic Resonance Fingerprinting
22. ¡ It has been well established that magnetic resonance imaging (MRI)
provides critical information about cancer [3].
¡ Magnetic resonance spectroscopic imaging (MRSI) furthers this
capability by providing information about the presence of certain
‘metabolites’ which are known to be important prognostic markers
of cancer [4] (stroke, AD, energy metabolism, TCA cycle).
¡ MRSI provides information about the spatial distribution of these
metabolites, hence enabling metabolic imaging.
[3] Huk WJ et al., Neurosurgical Review 7(4) 1984;
[4] Preul MC et al., Nat. Med. 2(3) 1996;
23. ¡ Increased choline level
¡ Reduced
N-Acetylaspartate (NAA)
level
¡ Reduced creatine level
[5] H Kugel et al., Radiology 183 June 1992
[5]
CANCER
NORMAL
24. ¡ Long acquisition times for MRSI
§ A typical MRSI protocol (32 X 32 X 512) takes ~ 20 minutes
§ Difficult to maintain anatomical posture for long time
§ Increases patient discomfort, likelihood of early termination of study
§ Discourages routine clinical use of this powerful MRI technique
¡ To increase throughput (decreased scanner time, technician time)
¡ Reduction of acquisition time is usually accomplished by under
sampling measured data (k-space).
¡ Limitations of Shannon-Nyquist criterion.
¡ Compressed sensing provides a framework to achieve sub-Nyquist
sampling rates with good data fidelity.
25. Brain - normal
(N=6)
Brain - cancer
(N=2)
Prostate -cancer
(N=2)
MRSI data Scanner TR(ms) TE(ms) # Averages Grid Size FOV (mm3)
Brain - normal
(N=6)
Siemens 3.0T
Trio Tim
1700 270 4 16 x 16 x 1024 100 x 100 x 15
Brain cancer
(N=2)
Philips 3.0T
Achieva
1000
112
112
2
2
18 x 21 x 1024
19 x 22 x 1024
180 x 210 x15
190 x 220 x 15
Prostate cancer
(N=2)
Philips 3.0T
Achieva
1200
1000
140
140
1
1
14 x 10 x 1024
16 x 12 x 1024
25 x 50 x 33
20 x 51 x 26
26. ¡ Minimal data processing done using jMRUI [7]
¡ FID Apodization – Gaussian (~3Hz)
¡ Removal of water peak using HLSVD
¡ Phase correction
§ To allow correct integration of the real part of the spectra
¡ QUEST based quantitation. [8]
§ To generate specific metabolite maps.
[7] A. Naressi, et al., Computers in Biology and Medicine, vol. 31, 2001.
[8] H. Ratiney, et al., Magnetic Resonance Materials in Physics Biology and Medicine, vol. 16, 2004.
30. § Mean ± SD of pooled
data for each data
type
§ 2 tailed paired
t-test
§ Ratio: CNI for brain
data and (Cho + cr)/
Cit for prostate data
§ Excluded voxels with
denominator value
of 0 in 1X case
§ For CS cases, if the
denominator had a
value of 0, the ratio
was set to 0
§ P value less than
0.05 was chosen as a
significant
difference
(* p <0.05)
NAA
(a.u.)
Cr
(a.u.)
Cho
(a.u.)
Cit
(a.u.)
Ratio
Brain
(Normal)
1X 200 ± 96.8 51.83 ± 27.6 13.8 ± 8.87 0.075 ± 0.047
2X 200 ± 98.9 51.99 ± 34.5 13.8 ± 10.2 0.073 ± 0.064
5X 202 ± 110 51.71 ± 30.7 13. 9 ± 10.6 0.082 ± 0.152
10X 241 ± 138* 65.22 ± 39.3* 17.9 ± 13.2* 0.086 ± 0.083*
Brain
(Cancer)
1X 10.7 ± 6.35 4.23 ± 2.43 3.21 ± 1.38 0.468 ± 0.519
2X 10.8 ± 6.45 4.27 ± 2.60 3.21 ± 1.37 0.625 ± 1.50
5X 10.6 ± 7.42 4.19 ± 2.35 3.21 ± 1.36 0.712 ± 1.82
10X 11.1 ± 8.78 3.72 ± 1.72* 3.27 ± 1.47 0.837 ± 1.89*
Prostate
(Cancer)
1X 499 ± 821 2010 ± 1730 188 ± 166 19.25 ± 25.23
2X 427 ± 830 1850 ± 1460 194 ± 131 14.10 ± 10.21
5X 382 ± 541 1830 ± 1450 193 ± 131 16.12 ± 16.44
10X 378 ± 540 1470 ± 958* 135 ± 111* 16.38 ± 23.59
31. N = total number of elements of the MRSI data;
Θ, Θ’ = the data reconstructed from full k-space and undersampled k-space respectively.
∑=
Θ−Θ=
N
i
ii
N
RMSE
1
2
)'(
1
'
32. ¡ Application of compressed sensing on 1H MRSI has been performed for the
first time
¡ It has been demonstrated that compressed sensing based reconstruction can
be successfully applied on 1H MRSI in vivo human brain (normal and cancer),
prostate cancer data and in vitro, computer generated phantom data sets
¡ Our results indicate a potential to reduce MRSI acquisition times by 75%
thus significantly reducing the time spent by the patient in the MR
scanner for spectroscopic studies
¡ Current and future work involves the implementation of compressed sensing
based pulse sequences on preclinical and clinical scanners
¡ Other groups in the world are working on this demonstration now!
34. C(t) =
f(ΔR1(t))
T1 – weighted
images for
baseline
T1 shortening
contrast agent
[10] Yankeelov TE, et. al MRI;23(4). 2005
*Model implemented by Dr. Vikram Kodibagkar in MATLAB
[10]
Tissue perfusion, microvascular
density and
extravascular -extracellular
volume -- tumor staging,
monitor treatment response
35. Spre(ω) = Lpre(ω) + Hpre(ω) (1a)
Spost(ω) = Lpost(ω) + Hpre(ω) (1b)
Є( Idiff) = || FIdiff – ydiff||2 + λLI || WIdiff ||1 +λTV(Idiff) (2)
Keyhole for DCE
CS for DCE
Ipost-contrast Ipre-contrast Idiff
Data was
normalized to a
range of 0 to 1
before
retrospective
reconstruction
Spost(ω) Spre(ω) ydiff
[11] Vanvaals JJ et. al. JMRI; 3(4) 1993
[12] Jim J et. al. IEEE TMI 2008
[13] Lustig M et. al. MRM;58(6) 2007
[11]
[12,13]
36. ¡ 5 DCE-MRI breast cancer data sets consisting of 64 frames (4 pre-
contrast images and 60 post-contrast images) were used for
retrospective reconstructions.
¡ The contrast agent used was Omniscan (intravenously administered
through the tail vein at a dose of 0.1 mmol/kg).
¡ Reconstructions based on 2 approaches: keyhole and compressed
sensing, were performed as function of masks and acceleration
factors were performed.
¡ These reconstructions were quantified by the root mean square error
metric defined below
∑=
Θ−Θ=
N
i
ii
N
RMSE
1
2
)'(
1
'
42. ¡ It has been shown here and previously that DCE MRI can be
reliably accelerated through methods like compressed sensing and
keyhole reconstructions to obtain increased spatial and/or
temporal resolution.
¡ CS based masks – Gauss and Gthresh provide better performance
when compared to Glines mask, which out do the keyhole masks
as observed by the RMSE graphs.
¡ Keyhole based masks – keyhole mask performs relatively poorer
when compared to keythresh and keylines masks
¡ Acceleration factors – the values of RMSE increases with
acceleration as expected (not shown); the CS masks show a RMSE
of less than 0.075 even at an acceleration factor of 5 while
keyhole masks result in a RMSE of less than 0.1
44. [14] D.Idiyatullin et al., JMR, 181, 2006. [14]
§ Sweep imaging with Fourier transformation [14]
§ Time domain signals are acquired during a swept radiofrequency
excitation in a time shared way
§ This results in a significantly negligible echo time.
§ Insensitive to motion, restricted dynamic range, low gradient noise
GRE SWIFT Photograph
Bovine tibia
45. ¡ Full k-space recon was performed using gridding. The volume was restricted
to a range of [0,1] by normalizing it to the highest absolute value.
¡ Prospective implementation is straight forward due to the nature of k-space
trajectory. Acceleration of 5.33 X was achieved – directly proportional to time
saved
¡ MR data is sparse in the total variation domain. Since the data in this case is
3D, a 3D total variation norm is most apt.
¡ Reconstruction involves minimization of the convex functional given below.
This is accomplished by a custom implementation of non-linear conjugate
gradient algorithm.
Є(m) = || Fum – y||2 +λTV TV(m)
where m is the desired MRI volume,
Fu is the Fourier transform operator,
TV is the 3D total variation operator,
||.||2 is the L2 norm operator,
λTV is the regularization parameter for the TV term respectively, and
Є is the value of the cost function.
46. ¡ The initial estimate of the volume is given by the zero-filled case with
density compensation (zfwdc). This produces artifacts which are
incoherent as can be seen in the zfwdc images.
¡ A total of 8 iterations were used and the recon was performed in 4 mins.
¡ NRMSE given by RMSE/ range of input; i.e. 1; hence NRMSE = RMSE
calculated as given below
N = total number of elements of the MRI volume; Θ, Θ’= the data reconstructed from full k-
space and undersampled k-space respectively.
∑=
Θ−Θ=
N
i
ii
N
RMSE
1
2
)'(
1
'