Tomosurgery is a new radiosurgery treatment planning approach that separates the tumor volume into planar slices and treats each slice with a continuous "moving shot" raster pattern to optimize dose distribution. The student aims to implement the Tomosurgery algorithm on a Gamma Knife platform by translating the MATLAB code to be compatible with Gamma Knife software. The goals are to successfully deliver a Tomosurgery plan to a phantom, compare the dose accuracy and delivery times to previous patient plans, and evaluate the utility of Tomosurgery for real-world use. Challenges include the Gamma Knife's inability to move the beam or create a disk-shaped shot.
Digital Tomosynthesis: Theory of OperationCarestream
Digital Tomosynthesis (DT) is a new radiographic imaging technique that is revived from the nearly century-old traditional film-screen tomography. This rejuvenation is all made possible by the recent advances in high frame-rate, high-sensitivity flat-panel digital radiographic detector, rapid pulsed-exposure sequence-capable high-frequency x-ray generator, the widely available and low-cost computer GPU processing power, and the precision motion controls built in the digital radiography system hardware. Read the white paper.
Smart Noise Cancellation Processing: New Level of Clarity in Digital RadiographyCarestream
Smart Noise Cancellation significantly reduces noise in diagnostic images while retaining fine spatial detail –there is no degradation of anatomical sharpness. When SNC is applied, it produces images that are significantly clearer than with standard processing. It also provides better contrast-to-noise ratio for images acquired at a broad range of exposures.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
EVP Plus Software delivers state-of-the-art image processing for CR and DR sy...Carestream
Radiographic technologists expect a high degree of
automation and efficiency in the technology they use in their
daily workflow, which means they expect minimal interaction
with the technology’s modality software. At the same time,
radiologists also need the flexibility to specify their site’s
individualized diagnostic viewing preferences. The CARESTREAM DirectView EVP Plus Software successfully
overcomes this challenge for digital-projection radiography.
EVP Plus automatically processes and delivers diagnostic-quality DR and CR images to PACS, based on look preferences that can be uniquely specified by each site.
Tube and Line and Pneumothorax Visualization SoftwareCarestream
Carestream has implemented companion views in its digital
radiography systems. A companion view is designed to
complement the standard processed radiographic image
delivered from the digital radiography capture modality to
PACS, to provide an additional rendering tailored for the visual
interpretation needed for a specific diagnostic or clinical
purpose. Two companion views are available in Carestream
products for chest radiography: one for the optimal
visualization of tubes and lines in chest radiographs
(CARESTREAM Tube & Line Visualization Software), and the
other for enhancing the conspicuity of a pneumothorax
(CARESTREAM Pneumothorax Visualization Software).
Dual energy imaging and digital tomosynthesis: Innovative X-ray based imaging...Carestream
Dual-energy (DE) imaging and digital tomosynthesis (DT) have been around for a few decades, but recent advancements in digital detectors have made this technology increasingly promising in clinical use. For more information about Carestream's imaging portfolio, visit www.carestream.com/medical or http://www.carestream.com/blog/2016/03/15/dual-energy-imaging-and-digital-tomosynthesis/
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.
Digital Tomosynthesis: Theory of OperationCarestream
Digital Tomosynthesis (DT) is a new radiographic imaging technique that is revived from the nearly century-old traditional film-screen tomography. This rejuvenation is all made possible by the recent advances in high frame-rate, high-sensitivity flat-panel digital radiographic detector, rapid pulsed-exposure sequence-capable high-frequency x-ray generator, the widely available and low-cost computer GPU processing power, and the precision motion controls built in the digital radiography system hardware. Read the white paper.
Smart Noise Cancellation Processing: New Level of Clarity in Digital RadiographyCarestream
Smart Noise Cancellation significantly reduces noise in diagnostic images while retaining fine spatial detail –there is no degradation of anatomical sharpness. When SNC is applied, it produces images that are significantly clearer than with standard processing. It also provides better contrast-to-noise ratio for images acquired at a broad range of exposures.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
EVP Plus Software delivers state-of-the-art image processing for CR and DR sy...Carestream
Radiographic technologists expect a high degree of
automation and efficiency in the technology they use in their
daily workflow, which means they expect minimal interaction
with the technology’s modality software. At the same time,
radiologists also need the flexibility to specify their site’s
individualized diagnostic viewing preferences. The CARESTREAM DirectView EVP Plus Software successfully
overcomes this challenge for digital-projection radiography.
EVP Plus automatically processes and delivers diagnostic-quality DR and CR images to PACS, based on look preferences that can be uniquely specified by each site.
Tube and Line and Pneumothorax Visualization SoftwareCarestream
Carestream has implemented companion views in its digital
radiography systems. A companion view is designed to
complement the standard processed radiographic image
delivered from the digital radiography capture modality to
PACS, to provide an additional rendering tailored for the visual
interpretation needed for a specific diagnostic or clinical
purpose. Two companion views are available in Carestream
products for chest radiography: one for the optimal
visualization of tubes and lines in chest radiographs
(CARESTREAM Tube & Line Visualization Software), and the
other for enhancing the conspicuity of a pneumothorax
(CARESTREAM Pneumothorax Visualization Software).
Dual energy imaging and digital tomosynthesis: Innovative X-ray based imaging...Carestream
Dual-energy (DE) imaging and digital tomosynthesis (DT) have been around for a few decades, but recent advancements in digital detectors have made this technology increasingly promising in clinical use. For more information about Carestream's imaging portfolio, visit www.carestream.com/medical or http://www.carestream.com/blog/2016/03/15/dual-energy-imaging-and-digital-tomosynthesis/
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.
Whitepaper: Image Quality Impact of SmartGrid Processing in Bedside Chest Ima...Carestream
Scattered radiation is known to degrade image quality in
diagnostic X-ray imaging. A new image processing tool, SmartGrid, has been developed that compensates for the effects of X-ray scatter in an image, and produces results comparable to those of a physical antiscatter grid. Read the white paper to learn more.
Comparitive study of brain tumor detection using morphological operatorseSAT Journals
Abstract
Segmentation divides an image into foreground object and the background object. In our case foreground object is brain tumor and background is CSF, white matter, and grey matter. Aim of our study is to detect the tumor and remove the background completely and compare the morphological operations that can be used for this purpose. Segmentation remains a challenging area for researchers since many segmentation methods results in over segmentation or under segmentation and hence, leads to the false interpretation of the results. The proposed work is the comparative study of the morphological segmentation methods for segmenting brain tumor from MRI images. Before segmentation, filtration process is carried out using two method, Non Local mean filter and median filter and their results are compared using MSE and PSNR. NL mean filter preserves sharp edges and fine details in an image hence, preferred over median filter. Also tumor location is identified, to get an approximate idea about the position of the tumor in the brain i.e. in which part the brain tumor is located. The tumor is identified by using different algorithms which are based on morphology such as watershed segmentation, morphological erosion, and hole filling algorithm and comparison between them is carried out based on parameters like accuracy, sensitivity and elapsed time. Each of the segmentation results are compared with the tumor obtained using interactive tool present in MATLAB R2013b.
Keywords: Brain tumor, MRI images, Image segmentation, Morphology, Erosion, Thresholding, Hole filling, Watershed segmentation
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Two-Dimensional Object Detection Using Accumulated Cell Average Constant Fals...ijcisjournal
The extensive work in SONAR is oceanic Engineering which is one of the most developing researches in
engineering. The SideScan Sonars (SSS) are one of the most utilized devices to obtain acoustic images of
the seafloor. This paper proposes an approach for developing an efficient system for automatic object
detection utilizing the technique of accumulated cell average-constant false alarm rate in 2D (ACA-CFAR-
2D), where the optimization of the computational effort is achieved. This approach employs image
segmentation as preprocessing step for object detection, which have provided similar results with other
approaches like undecimated discrete wavelet transform (UDWT), watershed and active contour
techniques. The SSS sea bottom images are segmented for the 2D object detection using these four
techniques and the segmented images are compared along with the experimental results of the proportion
of segmented image (P) and runtime in seconds (T) are presented.
Multimodal Medical Image Fusion Based On SVDIOSR Journals
Image fusion is a promising process in the field of medical image processing, the idea behind is to
improve the content of medical image by combining two or more multimodal medical images. In this paper a
novel fusion framework based on singular value decomposition - based image fusion algorithm is proposed.
SVD is an image adaptive transform, it transforms the matrix of the given image into product USVT
, which
allows to refactor a digital image into three matrices called tensors. The proposed algorithm picks out
informative image patches of source images to constitute the fused image by processing the divided subtensors
rather than the whole tensor and a novel sigmoid-function-like coefficient-combining scheme is applied to
construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion
approach.
Art - Through Davince's Eye's July 2011 menu containing: All Day Breakfast, Light Meals, Main Meals, Desserts, Ice Cream, Pancakes, Kids Meals, Coffee, Tea, Cold Beverages, Non-Alcoholic Drinks,
Whitepaper: Image Quality Impact of SmartGrid Processing in Bedside Chest Ima...Carestream
Scattered radiation is known to degrade image quality in
diagnostic X-ray imaging. A new image processing tool, SmartGrid, has been developed that compensates for the effects of X-ray scatter in an image, and produces results comparable to those of a physical antiscatter grid. Read the white paper to learn more.
Comparitive study of brain tumor detection using morphological operatorseSAT Journals
Abstract
Segmentation divides an image into foreground object and the background object. In our case foreground object is brain tumor and background is CSF, white matter, and grey matter. Aim of our study is to detect the tumor and remove the background completely and compare the morphological operations that can be used for this purpose. Segmentation remains a challenging area for researchers since many segmentation methods results in over segmentation or under segmentation and hence, leads to the false interpretation of the results. The proposed work is the comparative study of the morphological segmentation methods for segmenting brain tumor from MRI images. Before segmentation, filtration process is carried out using two method, Non Local mean filter and median filter and their results are compared using MSE and PSNR. NL mean filter preserves sharp edges and fine details in an image hence, preferred over median filter. Also tumor location is identified, to get an approximate idea about the position of the tumor in the brain i.e. in which part the brain tumor is located. The tumor is identified by using different algorithms which are based on morphology such as watershed segmentation, morphological erosion, and hole filling algorithm and comparison between them is carried out based on parameters like accuracy, sensitivity and elapsed time. Each of the segmentation results are compared with the tumor obtained using interactive tool present in MATLAB R2013b.
Keywords: Brain tumor, MRI images, Image segmentation, Morphology, Erosion, Thresholding, Hole filling, Watershed segmentation
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Two-Dimensional Object Detection Using Accumulated Cell Average Constant Fals...ijcisjournal
The extensive work in SONAR is oceanic Engineering which is one of the most developing researches in
engineering. The SideScan Sonars (SSS) are one of the most utilized devices to obtain acoustic images of
the seafloor. This paper proposes an approach for developing an efficient system for automatic object
detection utilizing the technique of accumulated cell average-constant false alarm rate in 2D (ACA-CFAR-
2D), where the optimization of the computational effort is achieved. This approach employs image
segmentation as preprocessing step for object detection, which have provided similar results with other
approaches like undecimated discrete wavelet transform (UDWT), watershed and active contour
techniques. The SSS sea bottom images are segmented for the 2D object detection using these four
techniques and the segmented images are compared along with the experimental results of the proportion
of segmented image (P) and runtime in seconds (T) are presented.
Multimodal Medical Image Fusion Based On SVDIOSR Journals
Image fusion is a promising process in the field of medical image processing, the idea behind is to
improve the content of medical image by combining two or more multimodal medical images. In this paper a
novel fusion framework based on singular value decomposition - based image fusion algorithm is proposed.
SVD is an image adaptive transform, it transforms the matrix of the given image into product USVT
, which
allows to refactor a digital image into three matrices called tensors. The proposed algorithm picks out
informative image patches of source images to constitute the fused image by processing the divided subtensors
rather than the whole tensor and a novel sigmoid-function-like coefficient-combining scheme is applied to
construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion
approach.
Art - Through Davince's Eye's July 2011 menu containing: All Day Breakfast, Light Meals, Main Meals, Desserts, Ice Cream, Pancakes, Kids Meals, Coffee, Tea, Cold Beverages, Non-Alcoholic Drinks,
In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.
A novel CAD system to automatically detect cancerous lung nodules using wav...IJECEIAES
A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
A summary of recent innovations in radiation oncology focussing on the priniciples of different techniques and their application. An overview of clinical results has also been given
The CARESTREAM Tube and Grid Alignment System
provides better image quality and consistent
techniques for portable diagnostic radiography.
For more about Carestream's software solutions, visit http://carestream.com/software
NUMERICAL STUDIES OF TRAPEZOIDAL PROTOTYPE AUDITORY MEMBRANE (PAM)IJCSEA Journal
In this research, we developed numerically a Prototype Auditory Membrane (PAM) for a fully implantable and self contained artificial cochlea. Cochleae are one of the important organs for hearing in the human and animals. Material of the prototype and implant of PAM are made of Polyvinylidene fluoride (PVDF)- Kureha, Japan which is fabricated using MEMS and thin film technologies. Another important thing in the characteristic of the PAM is not only convert the acoustic wave into electric signal but also the frequency selectivity. The thickness, Young’s modulus and density of the PAM are 40 μm, 4 GPa, and 1.79 103 kg/m3, respectively. The shape and dimension of the PAM is trapezoidal with the width is linearly changed from 2.0 to 4.0 mm with the length are 30 mm. Numerically, we develop the model of PAM is based on commercial CFD software, Fluent 6.3.26 and Gambit 2.4.6. The geometry model of the PAM consists of one-sided blocks of quadrilateral elements for 2D model and tetrahedral elements for 3 D model respectively. In this study we set the flow as laminar and carried out using unsteady time dependent calculation. The results show that the frequency selectivity of the membrane is detected on the membrane surface.
2. What is “Tomosurgery”? Tomosurgery is a new, patented approach to stereotactic radiosurgical treatment planning, developed primarily by Eric Hu, Ph.D., and collaborators Dr. David Dean, and Dr. Robert J. Maciunas. It is a unique inverse planning approach that: Separates the treatment volume into planar slices uses a “moving shot” to treat each volume-slice in a continuous, raster-scanning pattern. Tomosurgery is not device-dependent, but the Leksell Gamma Knife (and AccurayCyberKnife) hardware is currently the most conducive to its implementation.
3. Radiosurgery Treatment with the Leksell Gamma Knife The Elekta (Stockholm, Sweden) Leksell Gamma Knife is a radiosurgery device that delivers up to 192 non-lethal radiation beams that converge lethally at an isocenter within the patient’s skull. The sources are arranged into 8 groups of 24, with customizable beam sizes. For each planned shot, the system closes the sources and repositions the patient with an automated positioning system (APS). The LGK, along with GammaPlan® software, can deliver highly accurate and precise radiation dosages to target tissue while sparing normal tissue.
4. What problem does Tomosurgery solve? The traditional treatment planning approach centers around planning discrete 3-D “shots” to create a volumetric dose that conforms to the tumor, avoiding as much normal tissue as possible. However, for geometrically complex and/or large tumors: this “shot-packing” problem is difficult to optimize quickly for any inverse treatment algorithm, even with today’s computers. requires significant operator involvement and time investment. doesn’t currently consider dose contribution from other shots a priori, requiring adjustments after isodose lines have been calculated.
5. What problem does Tomosurgery solve? This results in a “trial and error”-based planning approach, in which shots are planned and then reconfigured based on the new calculated isodose lines until an acceptable plan is created. The current GammaPlan® (Version 10) inverse planning tools require multi-step optimization and significant operator involvement. Tomosurgery is a full-automated inverse treatment planning approach which centers on reducing the 3-dimensional problem of “shot-packing” into a series of 2-D problems, which are simpler and faster to solve. The resulting individual 2D treatment plans are then recombined into a 3-D treatment plan, which is also computationally simpler.
6. How does Tomosurgery work? The tumor volume is divided into slices, each slice to be treated independently first. The dose kernel is moved along a raster-like path within the treatment slice, “painting” the dose to fill the entire slice. A cost-function optimizes the path and speed. Next, the 2D treatment plans are assembled and optimized to account for dose from previous slices and critical structures.
7. How does Tomosurgery work? Tomosurgery is predicated on the concept of “Continuous Dose Delivery”, rather than the traditional “Step-and-Shoot” method. The beams should remain on while the patient is moved within the field, allowing a “moving shot” to treat each treatment slice. Two main advantages of a “moving shot”: Saves time spent on closing sources and repositioning Allows dose “weight” to be controlled by modulating the speed of the moving shot, or “intensity modulated” radiosurgery, similar to radiotherapy devices (i.e. IMRT) Dealing with Critical Structures During the optimization stages, tissue volume ROIs are given an importance weighting from 0.0 to 1.0 (Tumor, Non-tumor, and Critical Structure). Both the weight of the raster-scan lines AND the weight of each treatment slice are optimized according to these importance factors.
8. Outcomes of Previous Work Eric Hu prototyped the Tomosurgery work and tested the algorithm on 11 previously treated patients (7 without CS, 4 with CS) In all cases, the calculated Dose Volume Histograms (DVHs) were at least as good as the traditional treatment, and many cases had significantly steeper dose drop-offs than traditional treatment (especially in CS cases). The amount of time spent during treatment planning time was vastly reduced, with the Tomosurgery plans ranging from 5-35 minutes to calculate compared to the actual 1-3 hours spent (Hu et al, 2007). This was not including the potential savings of continuous dose delivery methods vs. step-and-shoot.
9.
10.
11.
12. Code has been modified to support parallel processing – we expect a significant decrease in computation time over the original program. Each treatment slice is processed in parallel.
13.
14. Pilot Work Updating from the 4C to the Perfexion http://www.rmgk.com/explained.html
15.
16. I wrote a new program that accounts for the new source-and-sector configuration of the Perfexion™, and updates the Tomosurgery algorithm to work with the new dose kernel.
17. Allows for customization of individual sectors sizes (4,8, 16mm), and displays midplanes of isodose distribution.
Thank you all for attending. I appreciate all of you taking time out of your schedules to be here. I have met and worked with all of you in some capacity, but for formality I’ll introduce myself. My name is Neel Gowdar, and I am a medical student here at Case and am pursuing a Master’s degree in biomedical engineering, with a concentration in imaging. My proposed thesis revolves around implementing the Tomosurgery algorithm, which was conceptually pioneered by a previous grad student named Eric Hu, who also worked in Dr. Dean’s lab.I’d first like to start with a brief introduction to the idea of Tomosurgery and how it works…
Tomosurgery is a new, patented approach to stereotactic treatment planningIt was developed by Eric Hu, Dr. David Dean, and Dr. MaciunasTomosurgery at its core is an inverse treatment planning method, which finds an optimum treatment plan by separating the tumor into 2D slices, and then treating these slices with a “moving shot” that moves in a raster-like pattern. I will explain this concept in the following slides.It is important to note that tomosurgery is not device dependent, and could be adapted for use with a variety of radiosurgery machines. However, currently the most promising and available hardware solutions are the Leksell GammaKnife and the AccurayCyberKnife, since the hardware on these devices is most compatible with the demands of the tomosurgery machine as of now.----- Meeting Notes (2/21/11 16:25) -----sloan BrainLabnot device depenedent
Just a quick overview of the gamma knife machine: Developed by Leksell, it usese 192 cobalt sources to deliver nonlethal beams which converge in a lethal isocenter within the patient skull.Sectors of sourcesAPSNormally planned with gammaplan
So what is the problem that Tomosurgery offers a solution to?CLICKTypically, a gamma knife treatment plan consists of trying to place discrete spherical “shots” with a treatment volume, the goal being to place these shots such that the resulting dose distribution covers as much of the tumor while sparing as much normal tissue or critical structure tissue as possible.CLICKFor uncomplicated cases with small or simply shaped tumors, this approach is sufficient and can often be automated by software.CLICKHowever, for large, geometrically complex tumors, or tumors that are complicated by adjacent critical structures (explain critical structures), this “shot-packing” approach starts to become very inefficient.CLICKFirstly, the concept of “packing” as a mathematical model is not a trivial problem to solve. Even in 3-dimensions, even todays fastest computers can’t automate this process within a reasonable timeframe.CLICKThis leads to the need for significant operator intervention, and the need to place shots manually.CLICKThe shots are often then re-adjusted by the operator once the dose distribution has been calculated, because there is no way to predict the isodose lines a priori – only after the shots have been placed and the dose recalculated. CLICK
CLICKResults in a trial and error approach, New shots planned and then reconfigured based on new calculated isodose linesUntil new plan is deemed “acceptable”.But this may not necessarily be the most optimum plan, only the plan the team chose to settle on, possible even due to time constraintsCLICKGamma plan 10CLICKTomosurgery is a fully automated…
CLICKIn order to maintain dose homogeneity and paint a uniform dose, tomosurgery relies on a concept Known as “Continuous dose delivery”, as opposed to the step and shoot method.CLICKThe beams should remain onCLICKThe yellow bar you see is the dose distribution resulting from a dose kernel at a constant speedCLICKTwo main advantages: time saved and intensity-modulationCLICKCritical structure weightings----- Meeting Notes (2/21/11 16:25) -----moving dose
Eric prototyped the algorithm on 11 patientsCLICKThe calculated Dose Volume Histograms were at least as good in all cases, with many faring better with steeper dose drop offs at the boundaries.CLICKThe amount of time spent during the planning phase was vastly reduced, with tomosurgery plans ranging from 5-35 minutes compared to the actual 1-3 hours spent. Note that this was not including the potential time savings from the use of continuous dose delivery versus step-and-shoot methods.
CLICK, CLICKI successfully portedParallel processingI successfully ported the tomosurgery algorithm into C# using Microsoft’s .NET Visual Studio, and for the most part it replicates the output of the original.
The original work done by Eric was with the older model of the Gamma Knife, known as the 4C unit. The 4C had 201 sources, and in order to change shot sizes, the entire helmet had to be changed. The advantage of the 4C was that individual sources could be blocked. By only allowing the 5th ring of sources to be open, the kernel became a disk-shape, which was ideally suited for 2D rastering of the kernel within a single slice of the tumor.
However, with the update to the recent Perfexion unit, the hardware underwent a significant overhaul. In the new unit, there are only 192 sources, arranged in 8 sectors of 24 each. In addition, each sector has multiple collimator sizings that can be chosen independently of the other sectors, allowing for composite shots of different collimator sizings.
Eric’s original work used a C program to calculate the dose distribution, using the angles of the sources as well as the reference depths of the skull. With the geometries now being changed with the Perfexion, I wrote a new program in .NET using the new angles and of each source. It calculates the dose-rate kernel based on the sector size and the reference depth measurements, which tailor the dose kernel to the patient’s individual head geometry. This is necessary since the beams have to travel through the skull to get to the isocenter, so this attenuation distance factors into the overall dose rate.
