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Response to "Comment on 'Large area CMOS active pixel sensor x-ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization' " [Med. Phys. 43, 1578-1579 (2016)]

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MEDICAL PHYSICS 2016年第11期43卷 6210-6212页

作者： Zhao, Chumin Konstantinidis, Anastasios C. Kanicki, Jerzy Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Christie NHS Fdn Trust Diagnost Radiol & Radiat Protect Christie Med Phys & Engn Manchester M20 4BX Lancs England

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Comment on "Large area CMOS active pixel sensor x-ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization" [Med. Phys. 42, 6294-6308 (2015)]

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MEDICAL PHYSICS 2016年第3期43卷 1578-1579页

作者： Chan, Heang-Ping Univ Michigan Dept Radiol 1500 E Med Ctr DrMed Inn Bldg C477 Ann Arbor MI 48109 USA

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Library-Based X-Ray Scatter Correction for Dedicated Cone-Beam Breast CT: Clinical Validation

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MEDICAL PHYSICS 2016年第6期43卷 3819-3819页

作者： Shi, L. Vedantham, S. Karellas, A. Zhu, L. Nuclear and Radiological Engineering and Medical Physics Programs The George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta Georgia 30332. Department of Radiology University of Massachusetts Medical School Worcester Massachusetts 01655.

OBJECTIVE:The image quality of dedicated cone beam breast CT (CBBCT) is limited by substantial scatter contamination, resulting in cupping artifacts and contrast-loss in reconstructed images. Such effects obscure the visibility of soft-tissue lesions and calcifications, which hinders breast cancer detection and diagnosis. In this work, we propose a library-based software approach to suppress scatter on CBBCT images with high efficiency, accuracy, and reliability.;METHODS:The authors precompute a scatter library on simplified breast models with different sizes using the geant4-based Monte Carlo (MC) toolkit. The breast is approximated as a semiellipsoid with homogeneous glandular/adipose tissue mixture. For scatter correction on real clinical data, the authors estimate the breast size from a first-pass breast CT reconstruction and then select the corresponding scatter distribution from the library. The selected scatter distribution from simplified breast models is spatially translated to match the projection data from the clinical scan and is subtracted from the measured projection for effective scatter correction. The method performance was evaluated using 15 sets of patient data, with a wide range of breast sizes representing about 95% of general population. Spatial nonuniformity (SNU) and contrast to signal deviation ratio (CDR) were used as metrics for evaluation.;RESULTS:Since the time-consuming MC simulation for library generation is precomputed, the authors' method efficiently corrects for scatter with minimal processing time. Furthermore, the authors find that a scatter library on a simple breast model with only one input parameter, i.e., the breast diameter, sufficiently guarantees improvements in SNU and CDR. For the 15 clinical datasets, the authors' method reduces the average SNU from 7.14% to 2.47% in coronal views and from 10.14% to 3.02% in sagittal views. On average, the CDR is improved by a factor of 1.49 in coronal views and 2.12 in sagittal views.;

关键词： biological organs biological tissues cancer computerised tomography image reconstruction image representation medical image processing Monte Carlo methods reliability Computed tomography Cancer Probability theory, stochastic processes, and statistics Computerised tomographs Biological material, e.g. blood, urine Haemocytometers Digital computing or data processing equipment or methods, specially adapted for specific applications image data processing or generation, in general breast computed tomography cone-beam breast CT scatter correction Monte Carlo simulation X-ray scattering Monte Carlo methods X-ray detectors Photons Computed tomography Medical image reconstruction Photon scattering Medical image artifacts Tissues

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A new look for Medical Physics and refocused editorial vision

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MEDICAL PHYSICS 2016年第1期43卷 I-III页

作者： Williamson, Jeffrey F. Das, Shiva K. Goodsitt, Mitchell M.

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Ultrashort echo-time MRI versus CT for skull aberration correction in MR-guided transcranial focused ultrasound: In vitro comparison on human calvaria

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MEDICAL PHYSICS 2015年第5期42卷 2223-2233页

作者： Miller, G. Wilson Eames, Matthew Snell, John Aubry, Jean-Francois Univ Virginia Dept Radiol & Med Imaging Charlottesville VA 22908 USA Univ Virginia Dept Biomed Engn Charlottesville VA 22908 USA Focused Ultrasound Fdn Charlottesville VA 22903 USA Univ Virginia Dept Neurosurg Charlottesville VA 22908 USA Univ Virginia Dept Radiat Oncol Charlottesville VA 22908 USA ESPCI ParisTech CNRS UMR 7587 INSERM U979 Inst Langevin Ondes & Images F-75005 Paris France

Purpose: Transcranial magnetic resonance-guided focused ultrasound (TcMRgFUS) brain treatment systems compensate for skull-induced beam aberrations by adjusting the phase and amplitude of individual ultrasound transducer elements. These corrections are currently calculated based on a preacquired computed tomography (CT) scan of the patient's head. The purpose of the work presented here is to demonstrate the feasibility of using ultrashort echo-time magnetic resonance imaging (UTE MRI) instead of CT to calculate and apply aberration corrections on a clinical TcMRgFUS system. Methods: Phantom experiments were performed in three ex-vivo human skulls filled with tissue-mimicking hydrogel. Each skull phantom was imaged with both CT and UTE MRI. The MR images were then segmented into "skull" and "not-skull" pixels using a computationally efficient, threshold-based algorithm, and the resulting 3D binary skull map was converted into a series of 2D virtual CT images. Each skull was mounted in the head transducer of a clinical TcMRgFUS system (ExAblate Neuro, Insightec, Israel), and transcranial sonications were performed using a power setting of approximately 750 acousticwatts at several different target locations within the electronic steering range of the transducer. Each target locationwas sonicated three times: once using aberration corrections calculated from the actual CT scan, once using corrections calculated from the MRI-derived virtual CT scan, and once without applying any aberration correction. MR thermometry was performed in conjunction with each 10-s sonication, and the highest single-pixel temperature rise and surrounding-pixel mean were recorded for each sonication. Results: The measured temperature rises were similar to 45% larger for aberration-corrected sonications than for noncorrected sonications. This improvement was highly significant (p < 10(-4)). The difference between the single-pixel peak temperature rise and the surrounding-pixel mean, which refle

关键词： biomedical MRI biomedical ultrasonics brain computerised tomography image segmentation medical image processing phantoms transducers Magnetic resonance imaging Biological effects of ultrasound, ultrasonic tomography Computed tomography Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging Computerised tomographs Diagnosis using ultrasonic, sonic or infrasonic waves Biological material, e.g. blood, urine Haemocytometers Digital computing or data processing equipment or methods, specially adapted for specific applications image data processing or generation, in general transcranial MR-guided focused ultrasound ultrashort echo time (UTE) MRI bone segmentation skull aberration correction Computed tomography Optical aberrations Medical magnetic resonance imaging Medical image segmentation Brain Temperature measurement Transducers Ultrasonography

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Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma

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MEDICAL PHYSICS 2015年第11期42卷 6725-6735页

作者： Yang, Dalu Rao, Ganesh Martinez, Juan Veeraraghavan, Ashok Rao, Arvind Univ Texas MD Anderson Canc Ctr Dept Bioinformat & Computat Biol Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Neurosurg Houston TX 77030 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

Purpose: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain cancer. Four molecular subtypes of GBM have been described but can only be determined by an invasive brain biopsy. The goal of this study is to evaluate the utility of texture features extracted from magnetic resonance imaging (MRI) scans as a potential noninvasive method to characterize molecular subtypes of GBM and to predict 12-month overall survival status for GBM patients. Methods: The authors manually segmented the tumor regions from postcontrast T1 weighted and T2 fluid-attenuated inversion recovery (FLAIR) MRI scans of 82 patients with de novo GBM. For each patient, the authors extracted five sets of computer-extracted texture features, namely, 48 segmentation-based fractal texture analysis (SFTA) features, 576 histogram of oriented gradients (HOGs) features, 44 run-length matrix (RLM) features, 256 local binary patterns features, and 52 Haralick features, from the tumor slice corresponding to the maximum tumor area in axial, sagittal, and coronal planes, respectively. The authors used an ensemble classifier called random forest on each feature family to predict GBM molecular subtypes and 12-month survival status (a dichotomized version of overall survival at the 12-month time point indicating if the patient was alive or not at 12 months). The performance of the prediction was quantified and compared using receiver operating characteristic (ROC) curves. Results: With the appropriate combination of texture feature set, image plane (axial, coronal, or sagittal), and MRI sequence, the area under ROC curve values for predicting different molecular subtypes and 12-month survival status are 0.72 for classical (with Haralick features on T1 postcontrast axial scan), 0.70 for mesenchymal (with HOG features on T2 FLAIR axial scan), 0.75 for neural (with RLM features on T2 FLAIR axial scan), 0.82 for proneural (with SFTA features on T1 postcontrast coronal scan), and 0.69 for 12-mont

关键词： biomedical MRI cancer feature extraction fractals image segmentation medical image processing tumours Segmentation Clinical applications Cancer Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging Biological material, e.g. blood, urine Haemocytometers Digital computing or data processing equipment or methods, specially adapted for specific applications image data processing or generation, in general glioblastoma texture features imaging genomics molecular subtypes Cancer Magnetic resonance imaging Brain Fractals Medical image segmentation Three dimensional image processing image analysis Rotation invariant pattern recognition Genomics

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Introduction: Advances and trends in image formation in X-ray computed tomography

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MEDICAL PHYSICS 2015年第5期42卷 2656-2656页

作者： Noo, Frederic Karellas, Andrew Univ Utah Utah Ctr Adv Imaging Res Salt Lake City UT 84108 USA Univ Massachusetts Sch Med Dept Radiol Worcester MA 01605 USA

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Imaging in Nuclear Medicine

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Medical physics 2016年第6期43卷 3207页

作者： Matthew Palmer

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Molecular Imaging of Small Animals

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Medical physics 2016年第6期43卷 3208页

作者： Jamal Zweit Sundaresan Gobalakrishnan

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image processing in Radiation Therapy

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Medical Physics 2015年第2期42卷 1138-1139页

作者： James M. Balter

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