Med Phys
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To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. ⋯ The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.
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Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. ⋯ The authors' method achieves robust segmentation and can serve as an efficient tool for processing large clinical datasets for quantifying the fibroglandular tissue content in breast MRI. It holds a great potential to support clinical applications in the future including breast cancer risk assessment.
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Setting a proper margin is crucial for not only delivering the required radiation dose to a target volume, but also reducing the unnecessary radiation to the adjacent organs at risk. This study investigated the independent one-dimensional symmetric and asymmetric margins between the clinical target volume (CTV) and the planning target volume (PTV) for linac-based single-fraction frameless stereotactic radiosurgery (SRS). ⋯ Margin expansion formulas were derived for single-fraction frameless SRS such that the CTV would receive the prescribed dose in 95% of the patients treated for brain cancer. The margins defined in this study are machine-specific and account for nonzero mean systematic error. The margin for single-fraction SRS for a group of machines was also derived in this paper.
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To present a computer-aided detection tool for identifying, quantifying, and evaluating tuberculosis (TB) cavities in the infected lungs from computed tomography (CT) scans. ⋯ The authors presented a fully automatic method for cavitary TB detection, quantification, and evaluation. The performance of every step of the algorithm was qualitatively and quantitatively assessed. With the proposed method, airways and cavities were automatically detected and subsequently delineated in high accuracy with heightened efficiency. Furthermore, not only morphological information of cavities were obtained through the authors' proposed framework, but their spatial relation to airways, and longitudinal analysis was also provided to get further insight on cavity formation in tuberculosis disease. To the authors' best of knowledge, this is the first study in computerized analysis of cavitary tuberculosis from CT scans.
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Fast B1 mapping based on short-TR sequences is prone to T1-induced errors. The purpose of this study is to develop a novel fast B1 mapping method that is less prone to T1-induced errors. ⋯ ITFA excitations made it possible to reduce the T1-effects on B1 mapping of the human-brain-mimicking phantom and the human brain at 3T. The authors expect the ITFA method can be used for B1 shimming once the optimal flip angles have been predetermined for the target imaging region and for the preferred TR.