Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics
Not provided.
Computer Science↗
Engineering topics
Anastasio, Mark A.
Publications and source records attributed to Anastasio, Mark A..
First point-spread function and x-ray phase-contrast imaging results with an 88-mm diameter single crystal
In this paper, we report initial demonstrations of the use of single crystals in indirect x-ray imaging with a benchtop implementation of propagation-based (PB) x-ray phase contrast imaging. Based on single Gaussian peak fits to the x-ray images, we observed a four times smaller system point-spread function (PSF) with the 50-μm thick single crystal scintillators than with the reference polycrystalline phosphor/scintillator. Fiber-optic plate depth-of-focus and Al reflective-coating aspects are also elucidated. Guided by the results from the 25-mm diameter crystal samples, we report additionally the first results with a unique 88-mm diameter single crystal bonded to a fiber optic plate and coupled to the large format CCD. Both PSF and x-ray phase contrast imaging data are quantified and presented.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Imaging of Hydrogel Microsphere Structure and Foreign Body Response Based on Endogenous X-Ray Phase Contrast
Transplantation of functional islets encapsulated in stable biomaterials has the potential to cure Type I diabetes. However, the success of these materials requires the ability to understand their stability in vivo. Imaging techniques that enable monitoring of biomaterial performance are critical to further development in the field. In this study, we demonstrate for the first time that X-ray phase contrast (XPC) imaging techniques enable 3D imaging and evaluation of islet volume, alginate hydrogel structure and local soft tissue response. Islets were encapsulated in alginate systems prepared in methods used in clinical trials and implanted in a rodent omentum pouch model as a treatment for type I diabetes. Microbeads were imaged with XPC prior to implantation and following implantation into an omentum pouch. Islets could be identified within alginate beads and the islet volume quantified. Omental adipose tissue could be distinguished from inflammatory regions resulting from implanted beads. Individual beads and the local encapsulation response were visualized and quantifiable. Measurements were in agreement with histology. The 3D structure of the microbeads could be characterized with XPC and failed beads could also be identified. These results point to the substantial potential of XPC as a tool for imaging biomaterials in small animal models.
59 BASIC BIOLOGICAL SCIENCES↗
Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy
Purpose: One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter- and intraobserver variability. These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to reverify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, the authors developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow. Methods: Considering the radiation therapy structures’ geometric attributes (centroid, volume, and shape), the spatial relationship of neighboring structures, as well as anatomical similarity of individual contours among patients, the authors established GAD models to characterize the interstructural centroid and volume variations, and the intrastructural shape variations of each individual structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations calculated from training sets with verified OAR contours. A new iterative weighted GAD model-fitting algorithm was developed for contouring error detection. Receiver operating characteristic (ROC) analysis was employed in a unique way to optimize the model parameters to satisfy clinical requirements. A total of forty-four head-and-neck patient cases, each of which includes nine critical OAR contours, were utilized to demonstrate the proposed strategy. Twenty-nine out of these forty-four patient cases were utilized to train the inter- and intrastructural GAD models. These training data and the remaining fifteen testing data sets were separately employed to test the effectiveness of the proposed contouring error detection strategy. Results: An evaluation tool was implemented to illustrate how the proposed strategy automatically detects the radiation therapy contouring errors for a given patient and provides 3D graphical visualization of error detection results as well. The contouring error detection results were achieved with an average sensitivity of 0.954/0.906 and an average specificity of 0.901/0.909 on the centroid/volume related contouring errors of all the tested samples. As for the detection results on structural shape related contouring errors, an average sensitivity of 0.816 and an average specificity of 0.94 on all the tested samples were obtained. The promising results indicated the feasibility of the proposed strategy for the detection of contouring errors with low false detection rate. Conclusions: The proposed strategy can reliably identify contouring errors based upon inter- and intrastructural constraints derived from clinically approved contours. It holds great potential for improving the radiation therapy workflow. ROC and box plot analyses allow for analytically tuning of the system parameters to satisfy clinical requirements. Future work will focus on the improvement of strategy reliability by utilizing more training sets and additional geometric attribute constraints.
60 APPLIED LIFE SCIENCES↗
X-ray Phase Contrast Imaging of Calcified Tissue and Biomaterial Structure in Bioreactor Engineered Tissues
Tissues engineered in bioreactor systems have been used clinically to replace damaged tissues and organs. In addition, these systems are under continued development for many tissue engineering applications. The ability to quantitatively assess material structure and tissue formation is critical for evaluating bioreactor efficacy and for preimplantation assessment of tissue quality. These techniques allow for the nondestructive and longitudinal monitoring of large engineered tissues within the bioreactor systems and will be essential for the translation of these strategies to viable clinical therapies. X-ray Phase Contrast (XPC) imaging techniques have shown tremendous promise for a number of biomedical applications owing to their ability to provide image contrast based on multiple X-ray properties, including absorption, refraction, and scatter. In this research, mesenchymal stem cell-seeded alginate hydrogels were prepared and cultured under osteogenic conditions in a perfusion bioreactor. The constructs were imaged at various time points using XPC microcomputed tomography (µCT). Imaging was performed with systems using both synchrotron- and tube-based X-ray sources. XPC µCT allowed for simultaneous three-dimensional (3D) quantification of hydrogel size and mineralization, as well as spatial information on hydrogel structure and mineralization. Samples were processed for histological evaluation and XPC showed similar features to histology and quantitative analysis consistent with the histomorphometry. Furthermore, these results provide evidence of the significant potential of techniques based on XPC for noninvasive 3D imaging engineered tissues grown in bioreactors.
alginate microbeads↗
Automatic CT simulation optimization for radiation therapy: A general strategy
Purpose: In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. Methods: The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Results: Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube potentials for patient sizes of 38, 43, 48, 53, and 58 cm were 120, 140, 140, 140, and 140 kVp, respectively, and the corresponding minimum CTDIvol for achieving the optimal image quality index 4.4 were 9.8, 32.2, 100.9, 241.4, and 274.1 mGy, respectively. For patients with lateral sizes of 43–58 cm, 120-kVp scan protocols yielded up to 165% greater radiation dose relative to 140-kVp protocols, and 140-kVp protocols always yielded a greater image quality index compared to the same dose-level 120-kVp protocols. The trace of target and organ dosimetry coverage and the γ passing rates of seven IMRT dose distribution pairs indicated the feasibility of the proposed image quality index for the predication strategy. Conclusions: A general strategy to predict the optimal CT simulation protocols in a flexible and quantitative way was developed that takes into account patient size, treatment planning task, and radiation dose. The experimental study indicated that the optimal CT simulation protocol and the corresponding radiation dose varied significantly for different patient sizes, contouring accuracy, and radiation treatment planning tasks.
62 RADIOLOGY AND NUCLEAR MEDICINE↗