bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐C‐206‐02: Estimating Coronary Artery Plaque Composition with a Combined Dual‐Energy and Single‐Energy QCT Optimization Model
Authors: Goodsitt M , Chan H , Watcharotone K , Nan B , Christodoulou E 
Published: July 2016

Purpose:A study was performed to evaluate the accuracy of a combined dual‐energy/single‐energy QCT optimization model for estimating the volume fractions of fat, cholesterol, protein, fibrous tissue, and calcium in coronary artery plaque.Methods:This model uses 80kVp‐140kVp CT# data to distinguish calcium, and Gaussian component analysis based on the 140kVp CT#'s of the pure materials to distinguish the other constituents. An experimental study was performed with a thorax section phantom (CIRS, Inc.) containing various mixtures of 0.8–3.2mm thick plastic slabs simulating plaque components. The Z(eff), ρ(g/cc) properties of the plaque components and plaque‐simulating plastics are: [fat(5.82,0.92), LDPE(5.44, 0.92)], [cholesterol(5.65,1.067), polystyrene(5.62,1.05)], protein(6.73,1.38), Delrin (7.07,1.42)], fibrous(7.51, 1.05), nylon(6.21, 1.14]). Two water‐equivalent plastics containing CaCO3 (13% and 22% CaCO3) were also used. Scans of 6.4‐mm‐thick slabs of each plastic alone within the phantom were acquired to determine the CT#'s of the pure materials. All scans were repeated 3 times under each condition and reconstructed with standard (STD) and BONE kernels on a GE CT750HD CT scanner using a 10‐cm reconstruction FOV.Results:The minimum RMS errors for the various plaque‐simulating slab mixtures were 2.2% (BONE kernel) and 4.2% (STD kernel) for a 1.6mm 13% CaCO3, 3.2mm polystyrene, 3.2mm delrin, 3.2mm nylon slab mixture (total=11.2mm). Maximum RMS errors were 7.5% (BONE) and 13.7% (STD) for a mixture containing 1.6mm slabs of 13% CaCO3, LDPE, delrin, and nylon (total=6.4mm).Conclusion:The accuracy of the method decreased as the thicknesses of the slabs decreased, due to volume averaging. Accuracy was better for the BONE kernel that has higher spatial resolution. Even for this kernel, the errors are substantial for a mixture that is about the size of the coronary artery lumen (2–6mm). Higher spatial resolution and more quantitative reconstruction methods are needed to improve the accuracy of the method.NIH RO1 HL106545
SU‐C‐206‐06: Shift‐Invariant Projection and Backprojection for Helical CT Based On A Self‐Consistent Coordinate
Authors: Guo M , Gao H 
Published: July 2016

Purpose:Despite its promising utility, iterative CT image reconstruction is often time‐consuming, especially for helical CT, for which the X‐ray projections and backprojections are computationally dominant. To overcome the insufficient memory storage for projection and backprojection matrices, they are usually computed on‐the‐fly with parallel implementation via GPU. In this work, we develop an efficient method to compute projections and backprojections without repeated on‐the‐fly computations for helical CT.Methods:We design the shift‐invariant projections and backprojections based on a self‐consistent coordinate, so that the size of projection and backprojection matrices can be significantly reduced in order to be practically stored on GPU. For reconstruction accuracy, the projections and backprojections are based on two different strategies, i.e., ray‐driven and pixel‐driven algorithm respectively. Moreover the proposed method can be readily incorporated into standard iterative reconstruction algorithm. Rigorous mathematical analysis is carried out to establish the shift‐invariance for ray‐driven projections and pixel‐driven backprojections.Results:Numerical results using GPU parallelization suggest that the proposed shift‐invariant method has much improved computational efficiency from the conventional on‐the‐fly method, i.e.,by 3–6 fold acceleration for the projection and 3–16 fold acceleration for the backprojection using standard configuration of helical CT.Conclusion:We have proposed a shift‐invariant projection and backprojection method based on a self‐consistent coordinate so that the projection and backprojection matrices can be stored instead of being repeatedly computed on‐the‐fly. The proposed method can be conveniently utilized in standard iterative reconstruction method with minimal modification. Numerical results demonstrate that the proposed method can speed up the projection by 3 to 6 folds and the backprojection by 3 to 16 folds using standard configuration of helical CT.The authors were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).
SU‐C‐206‐07: A Practical Sparse View Ultra‐Low Dose CT Acquisition Scheme for PET Attenuation Correction in the Extended Scan Field‐Of‐View
Authors: Miao J , Fan J , Gopinatha Pillai A 
Published: July 2016

Purpose:To further reduce CT dose, a practical sparse‐view acquisition scheme is proposed to provide the same attenuation estimation as higher dose for PET imaging in the extended scan field‐of‐view.Methods:CT scans are often used for PET attenuation correction and can be acquired at very low CT radiation dose. Low dose techniques often employ low tube voltage/current accompanied with a smooth filter before backprojection to reduce CT image noise. These techniques can introduce bias in the conversion from HU to attenuation values, especially in the extended CT scan field‐of‐view (FOV). In this work, we propose an ultra‐low dose CT technique for PET attenuation correction based on sparse‐view acquisition. That is, instead of an acquisition of full amount of views, only a fraction of views are acquired. We tested this technique on a 64‐slice GE CT scanner using multiple phantoms. CT scan FOV truncation completion was performed based on the published water‐cylinder extrapolation algorithm. A number of continuous views per rotation: 984 (full), 246, 123, 82 and 62 have been tested, corresponding to a CT dose reduction of none, 4x, 8x, 12x and 16x. We also simulated sparse‐view acquisition by skipping views from the fully‐acquired view data.Results:FBP reconstruction with Q. AC filter on reduced views in the full extended scan field‐of‐view possesses similar image quality to the reconstruction on acquired full view data. The results showed a further potential for dose reduction compared to the full acquisition, without sacrificing any significant attenuation support to the PET.Conclusion:With the proposed sparse‐view method, one can potential achieve at least 2x more CT dose reduction compared to the current Ultra‐Low Dose (ULD) PET/CT protocol. A pre‐scan based dose modulation scheme can be combined with the above sparse‐view approaches, which can even further reduce the CT scan dose during a PET/CT exam.
SU‐C‐209‐07: Phantoms for Digital Breast Tomosynthesis Imaging System Evaluation
Authors: Jacobson D , Liu Y 
Published: July 2016

Purpose:Digital Breast Tomosynthesis (DBT) is gaining importance in breast imaging. There is a need for phantoms that can be used for image evaluation and comparison. Existing commercially available phantoms for DBT are expensive and may lack clinically relevant test objects. The purpose of this study is to develop phantoms for DBT evaluation.MethodsFour phantoms have been designed and constructed to assess the image quality (IQ) of two DBT systems. The first contains a spiral of 0.3 mm SiC beads in gelatin to measure the tomographic slice thickness profile and uniformity of coverage in a series of tomographic planes. The second contains simulated tumors inclined with respect to the phantom base to assess tomographic image quality. The third has a tilted array of discs with varying contrast and diameter. This phantom was imaged alone and in a stack of TE slabs giving 2 to 10 cm thickness. The fourth has a dual wedge of glandular and adipose simulating materials. One wedge contains discs with varying diameter and thickness; the other supports a mass with six simulated spicules of varying size and a cluster of simulated calcifications. The simulated glandular tissue material varies between 35 and 100% of the total thickness (5.5 cm).Results:All phantoms were scanned successfully. The best IQ comparison was achieved with the dual wedge phantom as demonstrated by the spiculated mass and calcifications. Images were evaluated by two radiologists and one physicist. The projection images and corresponding set of tomographic planes were comparable and the synthesized projection images were inferior to the projection images for both systems.Conclusion:Four phantoms were designed, constructed and imaged on two DBT systems. They successfully demonstrated performance differences between two systems, and between true and synthesized projection images. Future work will incorporate these designs into a single phantom.
SU‐C‐207B‐02: Maximal Noise Reduction Filter with Anatomical Structures Preservation
Authors: Maitree R , Guzman G , Chundury A , Roach M , Yang D 
Published: July 2016

Purpose:All medical images contain noise, which can result in an undesirable appearance and can reduce the visibility of anatomical details. There are varieties of techniques utilized to reduce noise such as increasing the image acquisition time and using post‐processing noise reduction algorithms. However, these techniques are increasing the imaging time and cost or reducing tissue contrast and effective spatial resolution which are useful diagnosis information. The three main focuses in this study are: 1) to develop a novel approach that can adaptively and maximally reduce noise while preserving valuable details of anatomical structures, 2) to evaluate the effectiveness of available noise reduction algorithms in comparison to the proposed algorithm, and 3) to demonstrate that the proposed noise reduction approach can be used clinically.Methods:To achieve a maximal noise reduction without destroying the anatomical details, the proposed approach automatically estimated the local image noise strength levels and detected the anatomical structures, i.e. tissue boundaries. Such information was used to adaptively adjust strength of the noise reduction filter. The proposed algorithm was tested on 34 repeating swine head datasets and 54 patients MRI and CT images. The performance was quantitatively evaluated by image quality metrics and manually validated for clinical usages by two radiation oncologists and one radiologist.Results:Qualitative measurements on repeated swine head images demonstrated that the proposed algorithm efficiently removed noise while preserving the structures and tissues boundaries. In comparisons, the proposed algorithm obtained competitive noise reduction performance and outperformed other filters in preserving anatomical structures. Assessments from the manual validation indicate that the proposed noise reduction algorithm is quite adequate for some clinical usages.Conclusion:According to both clinical evaluation (human expert ranking) and qualitative assessment, the proposed approach has superior noise reduction and anatomical structures preservation capabilities over existing noise removal methods.Senior Author Dr. Deshan Yang received research funding form ViewRay and Varian.
SU‐C‐207B‐07: Deep Convolutional Neural Network Image Matching for Ultrasound Guidance in Radiotherapy
Authors: Zhu N , Najafi M , Hancock S , Hristov D 
Published: July 2016

Purpose:Robust matching of ultrasound images is a challenging problem as images of the same anatomy often present non‐trivial differences. This poses an obstacle for ultrasound guidance in radiotherapy. Thus our objective is to overcome this obstacle by designing and evaluating an image blocks matching framework based on a two channel deep convolutional neural network.Methods:We extend to 3D an algorithmic structure previously introduced for 2D image feature learning [1]. To obtain the similarity between two 3D image blocks A and B, the 3D image blocks are divided into 2D patches Ai and Bi. The similarity is then calculated as the average similarity score of Ai and Bi. The neural network was then trained with public non‐medical image pairs, and subsequently evaluated on ultrasound image blocks for the following scenarios: (S1) same image blocks with/without shifts (A and A_shift_x); (S2) non‐related random block pairs; (S3) ground truth registration matched pairs of different ultrasound images with/without shifts (A_i and A_reg_i_shift_x).Results:For S1 the similarity scores of A and A_shift_x were 32.63, 18.38, 12.95, 9.23, 2.15 and 0.43 for x=ranging from 0 mm to 10 mm in 2 mm increments. For S2 the average similarity score for non‐related block pairs was −1.15. For S3 the average similarity score of ground truth registration matched blocks A_i and A_reg_i_shift_0 (1≤i≤5) was 12.37. After translating A_reg_i_shift_0 by 0 mm, 2 mm, 4 mm, 6 mm, 8 mm, and 10 mm, the average similarity scores of A_i and A_reg_i_shift_x were 11.04, 8.42, 4.56, 2.27, and 0.29 respectively.Conclusion:The proposed method correctly assigns highest similarity to corresponding 3D ultrasound image blocks despite differences in image content and thus can form the basis for ultrasound image registration and tracking.[1] Zagoruyko, Komodakis, “Learning to compare image patches via convolutional neural networks”, IEEE CVPR 2015,pp.4353–4361.
SU‐D‐204‐02: BED Consistent Extrapolation of Mean Dose Tolerances
Authors: Perko Z , Bortfeld T , Hong T , Wolfgang J , Unkelbach J 
Published: July 2016

Purpose:The safe use of radiotherapy requires the knowledge of tolerable organ doses. For experimental fractionation schemes (e.g. hypofractionation) these are typically extrapolated from traditional fractionation schedules using the Biologically Effective Dose (BED) model. This work demonstrates that using the mean dose in the standard BED equation may overestimate tolerances, potentially leading to unsafe treatments. Instead, extrapolation of mean dose tolerances should take the spatial dose distribution into account.Methods:A formula has been derived to extrapolate mean physical dose constraints such that they are mean BED equivalent. This formula constitutes a modified BED equation where the influence of the spatial dose distribution is summarized in a single parameter, the dose shape factor. To quantify effects we analyzed 14 liver cancer patients previously treated with proton therapy in 5 or 15 fractions, for whom also photon IMRT plans were available.Results:Our work has two main implications. First, in typical clinical plans the dose distribution can have significant effects. When mean dose tolerances are extrapolated from standard fractionation towards hypofractionation they can be overestimated by 10–15%. Second, the shape difference between photon and proton dose distributions can cause 30–40% differences in mean physical dose for plans having the same mean BED. The combined effect when extrapolating proton doses to mean BED equivalent photon doses in traditional 35 fraction regimens resulted in up to 7–8 Gy higher doses than when applying the standard BED formula. This can potentially lead to unsafe treatments (in 1 of the 14 analyzed plans the liver mean dose was above its 32 Gy tolerance).Conclusion:The shape effect should be accounted for to avoid unsafe overestimation of mean dose tolerances, particularly when estimating constraints for hypofractionated regimens. In addition, tolerances established for a given treatment modality cannot necessarily be applied to other modalities with drastically different dose distributions.
SU‐D‐204‐06: Integration of Machine Learning and Bioinformatics Methods to Analyze Genome‐Wide Association Study Data for Rectal Bleeding and Erectile Dysfunction Following Radiotherapy in Prostate Cancer
Authors: Oh J , Kerns S , Ostrer H , Rosenstein B , Deasy J 
Published: July 2016

Purpose:We investigated whether integration of machine learning and bioinformatics techniques on genome‐wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation‐induced late rectal bleeding and erectile dysfunction in prostate cancer patients.Methods:We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning‐based predictive model of late radiation‐induced toxicities: rectal bleeding and erectile dysfunction. The model building process was performed using 2/3 of samples (training) and the predictive model was tested with 1/3 of samples (validation). To identify important single nucleotide polymorphisms (SNPs), we computed the SNP importance score, resulting from our random forest regression model. We performed gene ontology (GO) enrichment analysis for nearby genes of the important SNPs.Results:After univariate analysis on the training dataset, we filtered out many SNPs with p>0.001, resulting in 749 and 367 SNPs that were used in the model building process for rectal bleeding and erectile dysfunction, respectively. On the validation dataset, our random forest regression model achieved the area under the curve (AUC)=0.70 and 0.62 for rectal bleeding and erectile dysfunction, respectively. We performed GO enrichment analysis for the top 25%, 50%, 75%, and 100% SNPs out of the select SNPs in the univariate analysis. When we used the top 50% SNPs, more plausible biological processes were obtained for both toxicities. An additional test with the top 50% SNPs improved predictive power with AUC=0.71 and 0.65 for rectal bleeding and erectile dysfunction. A better performance was achieved with AUC=0.67 when age and androgen deprivation therapy were added to the model for erectile dysfunction.Conclusion:Our approach that combines machine learning and bioinformatics techniques enabled designing better models and identifying more plausible biological processes associated with the outcomes.
SU‐D‐201‐02: A Systematic Planning Quality Monitoring Program for VMAT Treatment
Authors: Zeng G , Robertson M , Murphy J , Lamey M , Wang Y 
Published: July 2016

Purpose:To demonstrate a systematic tracking program for planning quality; to advocate that commercial treatment planning systems develop tools to monitor population‐based quality in order to justify technology change in respect to clinical outcome.Methods:The DVH (Dose Volume Histogram) files of VMAT patients since 2011 have been exported from Varian Eclipse treatment planning system. The nomenclature of structures has been standardized and the planning process has been semi‐automated for clinical sites where such efforts bring clinical and operational benefits, such as GU. An in‐house program was developed in C++ to extract the dose volume points of targets and organs at risk from the exported DVH files. The control points were determined by QUANTEC (Quantitative Analyses of Normal Tissue Effects in the Clinic) organ specific dose volume recommendations for toxicity control. Monitor units were used as the indication of treatment efficiency. Control charts for dose volume points and monitor units were created to monitor planning quality dynamically.Results:The figures in the supporting document show some examples of prostate 78Gy plans over the most recent 30 months. The contoured CTV, rectum and bladder are statistically stable at 74±27 cm3, 77±28cm3 and 366±165cm3, respectively, reflecting consistence in target delineation, bowel and bladder preparation. Rectum V50, V60, V65, V70 and V75 remain at 24±6 %, 18±5%, 16±5%, 13±4 % and 9±4%, respectively, well below QUANTEC recommendations of 50%, 30%, 25%, 20% and 15%. The stability is also observed in targets and other OARs’ doses. However, monitor units have increased by 11%, from 533±42MU to 594±40MU, after Eclipse version 11 upgrade in which the optimization algorithm was changed.Conclusion:Monitoring population‐wise quality provides an assessment of technology changes and clinical outcomes, which can help guide future development. We therefore recommend these types of tools be incorporated in commercial treatment planning systems.
SU‐D‐201‐03: During‐Treatment Delivery Monitoring System for TomoTherapy
Authors: Chen Q , Read P 
Published: July 2016

Purpose:Multiple error pathways can lead to delivery errors during the treatment course that cannot be caught with pre‐treatment QA. While in vivo solutions are being developed for linacs, no such solution exists for tomotherapy. The purpose of this study is to develop a near real‐time system for tomotherapy that can monitor the delivery and dose accumulation process during the treatment‐delivery, which enable the user to assess the impact of delivery variations and/or errors and to interrupt the treatment if necessary.Methods:A program running on a tomotherapy planning station fetches the raw DAS data during treatment. Exit detector data is extracted as well as output, gantry angle, and other machine parameters. For each sample, the MLC open‐close state is determined. The delivered plan is compared with the original plan via a Monte Carlo dose engine which transports fluence deviations from a pre‐treatment Monte Carlo run. A report containing the difference in fluence, dose and DVH statistics is created in html format. This process is repeated until the treatment is completed.Results:Since we only need to compute the dose for the difference in fluence for a few projections each time, dose with 2% statistical uncertainty can be computed in less than 1 second on a 4‐core cpu. However, the current bottleneck in this near real‐time system is the repeated fetching and processing the growing DAS data file throughout the delivery. The frame rate drops from 10Hz at the beginning of treatment to 5Hz after 3 minutes and to 2Hz after 10 minutes.Conclusion:A during‐treatment delivery monitor system has been built to monitor tomotherapy treatments. The system improves patient safety by allowing operators to assess the delivery variations and errors during treatment delivery and adopt appropriate actions.
SU‐D‐201‐05: On the Automatic Recognition of Patient Safety Hazards in a Radiotherapy Setup Using a Novel 3D Camera System and a Deep Learning Framework
Authors: Santhanam A , Min Y , Beron P , Agazaryan N , Kupelian P , Low D 
Published: July 2016

Purpose:Patient safety hazards such as a wrong patient/site getting treated can lead to catastrophic results. The purpose of this project is to automatically detect potential patient safety hazards during the radiotherapy setup and alert the therapist before the treatment is initiated.Methods:We employed a set of co‐located and co‐registered 3D cameras placed inside the treatment room. Each camera provided a point‐cloud of fraxels (fragment pixels with 3D depth information). Each of the cameras were calibrated using a custom‐built calibration target to provide 3D information with less than 2 mm error in the 500 mm neighborhood around the isocenter. To identify potential patient safety hazards, the treatment room components and the patient's body needed to be identified and tracked in real‐time. For feature recognition purposes, we used a graph‐cut based feature recognition with principal component analysis (PCA) based feature‐to‐object correlation to segment the objects in real‐time. Changes in the object's position were tracked using the CamShift algorithm. The 3D object information was then stored for each classified object (e.g. gantry, couch). A deep learning framework was then used to analyze all the classified objects in both 2D and 3D and was then used to fine‐tune a convolutional network for object recognition. The number of network layers were optimized to identify the tracked objects with >95% accuracy.Results:Our systematic analyses showed that, the system was effectively able to recognize wrong patient setups and wrong patient accessories. The combined usage of 2D camera information (color + depth) enabled a topology‐preserving approach to verify patient safety hazards in an automatic manner and even in scenarios where the depth information is partially available.Conclusion:By utilizing the 3D cameras inside the treatment room and a deep learning based image classification, potential patient safety hazards can be effectively avoided.