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Journal of Medical Imaging

David C Newitt, Dariya Malyarenko, Thomas L Chenevert, C Chad Quarles, Laura Bell, Andriy Fedorov, Fiona Fennessy, Michael A Jacobs, Meiyappan Solaiyappan, Stefanie Hectors, Bachir Taouli, Mark Muzi, Paul E Kinahan, Kathleen M Schmainda, Melissa A Prah, Erin N Taber, Christopher Kroenke, Wei Huang, Lori R Arlinghaus, Thomas E Yankeelov, Yue Cao, Madhava Aryal, Yi-Fen Yen, Jayashree Kalpathy-Cramer, Amita Shukla-Dave, Maggie Fung, Jiachao Liang, Michael Boss, Nola Hylton
Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics...
January 2018: Journal of Medical Imaging
Kayla R Mendel, Hui Li, Li Lan, Cathleen M Cahill, Victoria Rael, Hiroyuki Abe, Maryellen L Giger
The robustness of radiomic texture analysis across different manufacturers of mammography imaging systems is investigated. We quantified feature robustness across mammography manufacturers using a dataset of 111 women who underwent consecutive screening mammography on both general electric and Hologic systems. In each mammogram, a square region of interest (ROI) directly behind the nipple was manually selected. Radiomic features describing parenchymal patterns were automatically extracted on each ROI. Feature comparisons were conducted between manufacturers (and breast densities) using newly developed robustness metrics descriptive of correlation, equivalence, and variability...
January 2018: Journal of Medical Imaging
Robert J Nordstrom
This guest editorial introduces the Special Section honoring Dr. Laurence P. Clarke.
January 2018: Journal of Medical Imaging
Thijs Kooi, Nico Karssemeijer
We investigate the addition of symmetry and temporal context information to a deep convolutional neural network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography. We employ a simple linear mapping that takes the location of a mass candidate and maps it to either the contralateral or prior mammogram, and regions of interest (ROIs) are extracted around each location. Two different architectures are subsequently explored: (1) a fusion model employing two datastreams where both ROIs are fed to the network during training and testing and (2) a stagewise approach where a single ROI CNN is trained on the primary image and subsequently used as a feature extractor for both primary and contralateral or prior ROIs...
October 2017: Journal of Medical Imaging
Jingyao Li, Dongdong Lin, Yu-Ping Wang
Multicolor fluorescence in situ hybridization (M-FISH) is a multichannel imaging technique for rapid detection of chromosomal abnormalities. It is a critical and challenging step to segment chromosomes from M-FISH images toward better chromosome classification. Recently, several fuzzy C-means (FCM) clustering-based methods have been proposed for M-FISH image segmentation or classification, e.g., adaptive fuzzy C-means (AFCM) and improved AFCM (IAFCM), but most of these methods used only one channel imaging information with limited accuracy...
October 2017: Journal of Medical Imaging
Mohammad Javad Shafiee, Audrey G Chung, Farzad Khalvati, Masoom A Haider, Alexander Wong
While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data...
October 2017: Journal of Medical Imaging
Hui Li, Maryellen L Giger, Benjamin Q Huynh, Natalia O Antropova
To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases)...
October 2017: Journal of Medical Imaging
Shaimaa Bakr, Sebastian Echegaray, Rajesh Shah, Aya Kamaya, John Louie, Sandy Napel, Nishita Kothary, Olivier Gevaert
We explore noninvasive biomarkers of microvascular invasion (mVI) in patients with hepatocellular carcinoma (HCC) using quantitative and semantic image features extracted from contrast-enhanced, triphasic computed tomography (CT). Under institutional review board approval, we selected 28 treatment-naive HCC patients who underwent surgical resection. Four radiologists independently selected and delineated tumor margins on three axial CT images and extracted computational features capturing tumor shape, image intensities, and texture...
October 2017: Journal of Medical Imaging
Ruida Cheng, Holger R Roth, Nathan Lay, Le Lu, Baris Turkbey, William Gandler, Evan S McCreedy, Tom Pohida, Peter A Pinto, Peter Choyke, Matthew J McAuliffe, Ronald M Summers
Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization...
October 2017: Journal of Medical Imaging
Hannu Järvinen, Jenia Vassileva, Ehsan Samei, Anthony Wallace, Eliseo Vano, Madan Rehani
Optimization is one of the key concepts of radiation protection in medical imaging. In practice, it involves compromising between the image quality and dose to the patient; the dose should not be higher than necessary to achieve an image quality (or diagnostic information) needed for the clinical task. Monitoring patient dose is a key requirement toward optimization. The concept of diagnostic reference level (DRL) was introduced by the International Commission on Radiological Protection as a practical tool for optimization...
July 2017: Journal of Medical Imaging
Ehsan Samei, Christoph Hoeschen
This guest editorial introduces the special section on Visions of Safety: Perspectives on Radiation Exposure and Risk in Medical Imaging.
July 2017: Journal of Medical Imaging
Elizabeth A Krupinski, Kevin M Schartz, Mark S Van Tassell, Mark T Madsen, Robert T Caldwell, Kevin S Berbaum
Our goal was to ascertain how fatigue affects performance in reading computed tomography (CT) examinations of patients with multiple injuries. CT images with multiple fractures from a previous study of satisfaction of search (SOS) were read by radiologists after a day of clinical work. Performance in this study with fatigued readers was compared to a previous study in which readers were not fatigued. Detection accuracy for obvious injuries was not affected by fatigue, but accuracy for subtle fractures was reduced ([Formula: see text])...
July 2017: Journal of Medical Imaging
Sara Moccia, Elena De Momi, Marco Guarnaschelli, Matteo Savazzi, Andrea Laborai, Luca Guastini, Giorgio Peretti, Leonardo S Mattos
Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited...
July 2017: Journal of Medical Imaging
Christopher P Favazza, Andrea Ferrero, Lifeng Yu, Shuai Leng, Kyle L McMillan, Cynthia H McCollough
The use of iterative reconstruction (IR) algorithms in CT generally decreases image noise and enables dose reduction. However, the amount of dose reduction possible using IR without sacrificing diagnostic performance is difficult to assess with conventional image quality metrics. Through this investigation, achievable dose reduction using a commercially available IR algorithm without loss of low contrast spatial resolution was determined with a channelized Hotelling observer (CHO) model and used to optimize a clinical abdomen/pelvis exam protocol...
July 2017: Journal of Medical Imaging
Celina L Li, Yogesh Thakur, Nancy L Ford
This study investigates the dosimetry methodology proposed by the American Association of Physicists in Medicine (AAPM) task group 111 and compares with the computed tomography dose index (CTDI) method and the SEDENTEXCT DI method on one clinical multislice CT and two dental cone beam CT (CBCT) scanners using adult, adolescent, and child head phantoms. Following the AAPM method, the normalized (100 mAs) equilibrium doses ([Formula: see text]) for Toshiba Aquilion One MSCT computed using dose measurements from the central hole of the phantom ([Formula: see text]), the peripheral hole of the phantom, ([Formula: see text]), and by the [Formula: see text] equation ([Formula: see text]) are in the range from 20 to 25 mGy...
July 2017: Journal of Medical Imaging
Stefanie T L Pöhlmann, Yit Y Lim, Elaine Harkness, Susan Pritchard, Christopher J Taylor, Susan M Astley
Assessment of three-dimensional (3-D) morphology and volume of breast masses is important for cancer diagnosis, staging, and treatment but cannot be derived from conventional mammography. Digital breast tomosynthesis (DBT) provides data from which 3-D mass segmentation could be obtained. Our method combined Gaussian mixture models based on intensity and a texture measure indicative of in-focus structure, gray-level variance. Thresholding these voxel probabilities, weighted by distance to the estimated mass center, gave the final 3-D segmentation...
July 2017: Journal of Medical Imaging
Kant M Matsuda, Ana Lopes-Calcas, Michael L Honke, Zoe O'Brien-Moran, Richard Buist, Michael West, Melanie Martin
To advance magnetic resonance imaging (MRI) technologies further for in vivo tissue characterization with histopathologic validation, we investigated the feasibility of ex vivo tissue imaging of a surgically removed human brain tumor as a comprehensive approach for radiology-pathology correlation in histoanatomically identical fashion in a rare case of pigmented ganglioglioma with complex paramagnetic properties. Pieces of surgically removed ganglioglioma, containing melanin and hemosiderin pigments, were imaged with a small bore 7-T MRI scanner to obtain T1-, T2-, and T2*-weighted image and diffusion tensor imaging (DTI)...
July 2017: Journal of Medical Imaging
Gabriel Prieto Renieblas, Agustín Turrero Nogués, Alberto Muñoz González, Nieves Gómez-Leon, Eduardo Guibelalde Del Castillo
The structural similarity index (SSIM) family is a set of metrics that has demonstrated good agreement with human observers in tasks using reference images. These metrics analyze the viewing distance, edge information between the reference and the test images, changed and preserved edges, textures, and structural similarity of the images. Eight metrics based on that family are proposed. This new set of metrics, together with another eight well-known SSIM family metrics, was tested to predict human performance in some specific tasks closely related to the evaluation of radiological medical images...
July 2017: Journal of Medical Imaging
Ma Luo, Sarah F Frisken, Jared A Weis, Logan W Clements, Prashin Unadkat, Reid C Thompson, Alexandra J Golby, Michael I Miga
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections...
July 2017: Journal of Medical Imaging
Xiaochen Yang, Logan W Clements, Ma Luo, Saramati Narasimhan, Reid C Thompson, Benoit M Dawant, Michael I Miga
Intraoperative soft tissue deformation, referred to as brain shift, compromises the application of current image-guided surgery navigation systems in neurosurgery. A computational model driven by sparse data has been proposed as a cost-effective method to compensate for cortical surface and volumetric displacements. We present a mock environment developed to acquire stereoimages from a tracked operating microscope and to reconstruct three-dimensional point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent...
July 2017: Journal of Medical Imaging
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