Med Phys
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Visibility of nigrosome 1 in the substantia nigra (SN) is used as an MR imaging biomarker for Parkinson's disease. Because of lower susceptibility induced tissue contrast and SNR visualization of the SN pars compacta (SNPC) using conventional imaging technique in the clinical field strength (≤3T) has been limited. Susceptibility map-weighted imaging (SMWI) has been proposed to visualize SNPC at 3T. To better visualize nigrosome 1 and SN areas using SMWI, accurate estimation of the quantitative susceptibility mapping (QSM) map is essential. In SMWI processing, however, QSM processing time using conventional algorithms is the most time-consuming step and may limit clinical use. In this study, we introduce an efficient SMWI processing approach using the deep neural network (QSMnet). To improve the processing speed of SMWI while maintaining similar image quality to that obtained with the conventional method, QSMnet was applied to generate a susceptibility mask for SMWI processing. ⋯ In this study, we assessed an efficient approach for SMWI visualizing SN and nigrosome 1 on 3T. QSMnet provides a similar SMWI image to that obtained with the conventional iterative QSM algorithm and improves QSM processing speed by avoiding iterative computation. Since QSM is the most time-consuming step of SMWI processing, QSMnet can help to achieve a higher processing speed of SMWI. These results suggest that SMWI imaging with susceptibility masks using QSMnet is a more efficient approach.
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Complex blood flow is commonly observed in the carotid bifurcation, although the factors that regulate these patterns beyond arterial geometry are unknown. The emergence of high-frame-rate ultrasound vector flow imaging allows for noninvasive, time-resolved analysis of complex hemodynamic behavior in humans, and it can potentially help researchers understand which physiological stressors can alter carotid bifurcation hemodynamics in vivo. Here, we seek to pursue the first use of vector projectile imaging (VPI), a dynamic form of vector flow imaging, to analyze the regulation of carotid bifurcation hemodynamics during experimental reductions in cardiac output induced via a physiological stressor called lower body negative pressure (LBNP). ⋯ Using VPI, intuitive visualization of complex hemodynamic changes can be obtained in healthy humans subjected to LBNP. This imaging tool has potential for further applications in vascular physiology to identify and quantify complex hemodynamic features in humans during different physiological stressor tests that regulate hemodynamics.
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Dynamic electron arc radiotherapy (DEAR) is a novel dynamic technique that achieves highly conformal dose through simultaneous couch and gantry motion during delivery. The purpose of this study is to develop a framework integrating a Monte Carlo dose engine (VirtuaLinac) to a treatment planning system (TPS, Eclipse) for DEAR. A quality assurance (QA) procedure is also developed. ⋯ A framework has been developed for DEAR dose calculation using VirtuaLinac Monte Carlo dose engine. The VirtuaLinac calculated dose was validated against measurement. A feasible and practical DEAR QA method has been developed for dose measurement in phantom. The hybrid dose calculation technique is efficient and suitable for DEAR QA purpose.
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Metal artifact is a quite common problem in diagnostic dental computed tomography (CT) images. Due to the high attenuation of heavy materials such as metal, severe global artifacts can occur in reconstructions. Typical metal artifact reduction (MAR) techniques segment out the metal regions and estimate the corrupted projection data by various interpolation methods. However, interpolations are not accurate and introduce new artifacts or even deform the teeth in the reconstructed image. This work presents a new strategy to take advantage of the power of deep learning for metal artifact reduction. ⋯ This work presents a strategy to build a transferable learning from simulations to practical systems for metal artifact reduction using a supervised deep learning method. The system transforms the MAR analyses to an interpolation-artifact reduction problem to recover structural details from the coarse interpolation reconstruction. In this way, training data from simulations with ground truth labels can easily model the similar features in real data with I-MAR as the bridge. The network can seamlessly optimize both simulations and real data. The whole method is easily implemented with little computational cost. Test results demonstrated that this is an effective MAR method applicable to practical dental CT systems.
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Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the myocardium from cardiac cine MR images is challenging due to variability of the normal and abnormal anatomy, as well as the imaging protocols. This study proposes a multi-task learning (MTL)-based regularization of a convolutional neural network (CNN) to obtain accurate segmenation of the cardiac structures from cine MR images. ⋯ We have presented a method for accurate segmentation of cardiac structures from cine MR images. Our experiments verify that the proposed method exceeds the segmentation performance of three existing state-of-the-art methods. Furthermore, several cardiac indices that often serve as diagnostic biomarkers, specifically blood pool volume, myocardial mass, and ejection fraction, computed using our method are better correlated with the indices computed from the reference, ground truth segmentation. Hence, the proposed method has the potential to become a non-invasive screening and diagnostic tool for the clinical assessment of various cardiac conditions, as well as a reliable aid for generating patient specific models of the cardiac anatomy for therapy planning, simulation, and guidance.