Med Phys
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Recent advances in compressed sensing (CS) enable accurate CT image reconstruction from highly undersampled and noisy projection measurements, due to the sparsifiable feature of most CT images using total variation (TV). These novel reconstruction methods have demonstrated advantages in clinical applications where radiation dose reduction is critical, such as onboard cone-beam CT (CBCT) imaging in radiation therapy. The image reconstruction using CS is formulated as either a constrained problem to minimize the TV objective within a small and fixed data fidelity error, or an unconstrained problem to minimize the data fidelity error with TV regularization. However, the conventional solutions to the above two formulations are either computationally inefficient or involved with inconsistent regularization parameter tuning, which significantly limit the clinical use of CS-based iterative reconstruction. In this paper, we propose an optimization algorithm for CS reconstruction which overcomes the above two drawbacks. ⋯ We propose ABOCS for CBCT reconstruction. As compared to other published CS-based algorithms, our method has attractive features of fast convergence and consistent parameter settings for different datasets. These advantages have been demonstrated on phantom studies.
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There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI). ⋯ This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accurately localizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.
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Craniospinal irradiation were traditionally treated the central nervous system using two or three adjacent field sets. A intensity-modulated radiotherapy (IMRT) plan (Jagged-Junction IMRT) which overcomes problems associated with field junctions and beam edge matching, improves planning and treatment setup efficiencies with homogenous target dose distribution was developed. ⋯ Jagged-Junction IMRT planning provided good dose homogeneity and conformity to the target while maintaining a low dose to the organs at risk. Jagged-Junction IMRT optimization smoothly distributed dose in the junction between field sets. Since there was no beam matching, this treatment technique is less likely to produce hot or cold spots at the junction in contrast to conventional techniques.
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To compare the dosimetric benefits of Rapidarc (RA) combined with deep inspiration breath-hold (DIBH) with those of other standard techniques, including free breathing (FB) during fixed-field intensity modulated radiation therapy (IMRT) and dual arc RA, in the treatment of patients with thoracic esophageal carcinoma (EC). ⋯ Compared with conventional FB, RA combined with DIBH significantly reduced cardiac and pulmonary doses without compromising the target coverage and may reduce treatment toxicity, enabling dose escalation in future prospective studies of patients with EC.
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Inverse planned intensity modulated radiation therapy (IMRT) has helped many centers implement highly conformal treatment planning with beamlet-based techniques. The many comparisons between IMRT and 3D conformal (3DCRT) plans, however, have been limited because most 3DCRT plans are forward-planned while IMRT plans utilize inverse planning, meaning both optimization and delivery techniques are different. This work avoids that problem by comparing 3D plans generated with a unique inverse planning method for 3DCRT called inverse-optimized 3D (IO-3D) conformal planning. Since IO-3D and the beamlet IMRT to which it is compared use the same optimization techniques, cost functions, and plan evaluation tools, direct comparisons between IMRT and simple, optimized IO-3D plans are possible. Though IO-3D has some similarity to direct aperture optimization (DAO), since it directly optimizes the apertures used, IO-3D is specifically designed for 3DCRT fields (i.e., 1-2 apertures per beam) rather than starting with IMRT-like modulation and then optimizing aperture shapes. The two algorithms are very different in design, implementation, and use. The goals of this work include using IO-3D to evaluate how close simple but optimized IO-3D plans come to nonconstrained beamlet IMRT, showing that optimization, rather than modulation, may be the most important aspect of IMRT (for some sites). ⋯ The unique IO-3D algorithm illustrates that inverse planning can achieve high quality 3D conformal plans equivalent (or nearly so) to unconstrained beamlet IMRT plans, for many sites. IO-3D thus provides the potential to optimize flat or few-segment 3DCRT plans, creating less complex optimized plans which are efficient and simple to deliver. The less complex IO-3D plans have operational advantages for scenarios including adaptive replanning, cases with interfraction and intrafraction motion, and pediatric patients.