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Journal of Medical Imaging

John Virostko, Allison Hainline, Hakmook Kang, Lori R Arlinghaus, Richard G Abramson, Stephanie L Barnes, Jeffrey D Blume, Sarah Avery, Debra Patt, Boone Goodgame, Thomas E Yankeelov, Anna G Sorace
This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis...
January 2018: Journal of Medical Imaging
Finbarr O'Sullivan, Janet N O'Sullivan, Jian Huang, Robert Doot, Mark Muzi, Erin Schubert, Lanell Peterson, Lisa K Dunnwald, David M Mankoff
Blood flow-metabolism mismatch from dynamic positron emission tomography (PET) studies with [Formula: see text]-labeled water ([Formula: see text]) and [Formula: see text]-labeled fluorodeoxyglucose (FDG) has been shown to be a promising diagnostic for locally advanced breast cancer (LABCa) patients. The mismatch measurement involves kinetic analysis with the arterial blood time course (AIF) as an input function. We evaluate the use of a statistical method for AIF extraction (SAIF) in these studies. Fifty three LABCa patients had dynamic PET studies with [Formula: see text] and FDG...
January 2018: Journal of Medical Imaging
Daekeun You, Michelle M Kim, Madhava P Aryal, Hemant Parmar, Morand Piert, Theodore S Lawrence, Yue Cao
To create tumor "habitats" from the "signatures" discovered from multimodality metabolic and physiological images, we developed a framework of a processing pipeline. The processing pipeline consists of six major steps: (1) creating superpixels as a spatial unit in a tumor volume; (2) forming a data matrix [Formula: see text] containing all multimodality image parameters at superpixels; (3) forming and clustering a covariance or correlation matrix [Formula: see text] of the image parameters to discover major image "signatures;" (4) clustering the superpixels and organizing the parameter order of the [Formula: see text] matrix according to the one found in step 3; (5) creating "habitats" in the image space from the superpixels associated with the "signatures;" and (6) pooling and clustering a matrix consisting of correlation coefficients of each pair of image parameters from all patients to discover subgroup patterns of the tumors...
January 2018: Journal of Medical Imaging
Imon Banerjee, Sadhika Malladi, Daniela Lee, Adrien Depeursinge, Melinda Telli, Jafi Lipson, Daniel Golden, Daniel L Rubin
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment...
January 2018: Journal of Medical Imaging
Alfiia Galimzianova, Žiga Lesjak, Daniel L Rubin, Boštjan Likar, Franjo Pernuš, Žiga Špiclin
Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation...
January 2018: Journal of Medical Imaging
Dariya Malyarenko, Andriy Fedorov, Laura Bell, Melissa Prah, Stefanie Hectors, Lori Arlinghaus, Mark Muzi, Meiyappan Solaiyappan, Michael Jacobs, Maggie Fung, Amita Shukla-Dave, Kevin McManus, Michael Boss, Bachir Taouli, Thomas E Yankeelov, Christopher Chad Quarles, Kathleen Schmainda, Thomas L Chenevert, David C Newitt
This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models...
January 2018: Journal of Medical Imaging
Qiao Huang, Lin Lu, Laurent Dercle, Philip Lichtenstein, Yajun Li, Qian Yin, Min Zong, Lawrence Schwartz, Binsheng Zhao
Radiomic features characterize tumor imaging phenotype. Nonsmall cell lung cancer (NSCLC) tumors are known for their complexity in shape and wide range in density. We explored the effects of variable tumor contouring on the prediction of epidermal growth factor receptor (EGFR) mutation status by radiomics in NSCLC patients treated with a targeted therapy (Gefitinib). Forty-six early stage NSCLC patients (EGFR mutant:wildtype = 20:26) were included. Three experienced radiologists independently delineated the tumors using a semiautomated segmentation software on a noncontrast-enhanced baseline and three-week post-therapy CT scan images that were reconstructed using 1...
January 2018: Journal of Medical Imaging
Sarah L Hurrell, Sean D McGarry, Amy Kaczmarowski, Kenneth A Iczkowski, Kenneth Jacobsohn, Mark D Hohenwalter, William A Hall, William A See, Anjishnu Banerjee, David K Charles, Marja T Nevalainen, Alexander C Mackinnon, Peter S LaViolette
Multiparametric magnetic resonance imaging (MP-MRI), including diffusion-weighted imaging, is commonly used to diagnose prostate cancer. This radiology-pathology study correlates prostate cancer grade and morphology with common b-value combinations for calculating apparent diffusion coefficient (ADC). Thirty-nine patients undergoing radical prostatectomy were recruited for MP-MRI prior to surgery. Diffusion imaging was collected with seven b-values, and ADC was calculated. Excised prostates were sliced in the same orientation as the MRI using 3-D printed slicing jigs...
January 2018: 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
Hsin-Wu Tseng, Jiahua Fan, Matthew A Kupinski
Maintaining or even improving image quality while lowering patient dose is always the desire in clinical computed tomography (CT) imaging. Iterative reconstruction (IR) algorithms have been designed to allow for a reduced dose while maintaining or even improving an image. However, we have previously shown that the dose-saving capabilities allowed with IR are different for different clinical tasks. The channelized scanning linear observer (CSLO) was applied to study clinical tasks that combine detection and estimation when assessing CT image data...
October 2017: Journal of Medical Imaging
Yongjin Sung, Rajiv Gupta, Brandon Nelson, Shuai Leng, Cynthia H McCollough, William S Graves
X-ray phase-contrast imaging (XPCI) overcomes the problem of low contrast between different soft tissues achieved in conventional x-ray imaging by introducing x-ray phase as an additional contrast mechanism. This work describes a compact x-ray light source (CXLS) and compares, via simulations, the high quality XPCI results that can be produced from this source to those produced using a microfocus x-ray source. The simulation framework is first validated using an image acquired with a microfocus-source, propagation-based XPCI (PB-XPCI) system...
October 2017: Journal of Medical Imaging
Pablo F Ordóñez, Carlos M Cepeda, Jose Garrido, Sumit Chakravarty
Convolutional neural networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting referable diabetic retinopathy. Having a size of [Formula: see text] of the total image, microaneurysms are early lesions in diabetic retinopathy that are difficult to classify. A model that includes two CNNs with different input image sizes, [Formula: see text] and [Formula: see text], was developed. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity [Formula: see text], a specificity [Formula: see text], and an area under the receiver operating characteristics curve [Formula: see text]...
October 2017: Journal of Medical Imaging
Srijata Chakravorti, Brian J Bussey, Yiyuan Zhao, Benoit M Dawant, Robert F Labadie, Jack H Noble
Cochlear implants (CIs) are surgically implantable neuroprosthetic devices used to treat profound hearing loss. Recent literature indicates that there is a correlation between the final intracochlear positioning of the CI electrode arrays and the ultimate hearing outcome of the patient, indicating that further studies to better understand the relationship between electrode position and outcomes could have significant implications for future surgical techniques, array design, and processor programming methods...
October 2017: Journal of Medical Imaging
Benjamin Béouche-Hélias, David Helbert, Cynthia de Malézieu, Nicolas Leveziel, Christine Fernandez-Maloigne
Although a lot of work has been done on optical coherence tomography and color images in order to detect and quantify diseases such as diabetic retinopathy, exudates, or neovascularizations, none of them is able to evaluate the diffusion of the neovascularizations in retinas. Our work has been to develop a tool that is able to quantify a neovascularization and the fluorescein leakage during an angiography. The proposed method has been developed following a clinical trial protocol; images are taken by a Spectralis (Heidelberg Engineering)...
October 2017: Journal of Medical Imaging
Jianing Wang, Yuan Liu, Jack H Noble, Benoit M Dawant
Medical image registration establishes a correspondence between images of biological structures, and it is at the core of many applications. Commonly used deformable image registration methods depend on a good preregistration initialization. We develop a learning-based method to automatically find a set of robust landmarks in three-dimensional MR image volumes of the head. These landmarks are then used to compute a thin plate spline-based initialization transformation. The process involves two steps: (1) identifying a set of landmarks that can be reliably localized in the images and (2) selecting among them the subset that leads to a good initial transformation...
October 2017: Journal of Medical Imaging
Wei Zhou, Juan Montoya, Ralf Gutjahr, Andrea Ferrero, Ahmed Halaweish, Steffen Kappler, Cynthia McCollough, Shuai Leng
An ultra-high resolution (UHR) mode, with a detector pixel size of [Formula: see text] relative to isocenter, has been implemented on a whole body research photon-counting detector (PCD) computed tomography (CT) system. Twenty synthetic lung nodules were scanned using UHR and conventional resolution (macro) modes and reconstructed with medium and very sharp kernels. Linear regression was used to compare measured nodule volumes from CT images to reference volumes. The full-width-at-half-maximum of the calculated curvature histogram for each nodule was used as a shape index, and receiver operating characteristic analysis was performed to differentiate sphere- and star-shaped nodules...
October 2017: Journal of Medical Imaging
Shuang Liu, Yiting Xie, Artit Jirapatnakul, Anthony P Reeves
A three-dimensional (3-D) convolutional neural network (CNN) trained from scratch is presented for the classification of pulmonary nodule malignancy from low-dose chest CT scans. Recent approval of lung cancer screening in the United States provides motivation for determining the likelihood of malignancy of pulmonary nodules from the initial CT scan finding to minimize the number of follow-up actions. Classifier ensembles of different combinations of the 3-D CNN and traditional machine learning models based on handcrafted 3-D image features are also explored...
October 2017: Journal of Medical Imaging
Hirohisa Oda, Kanwal K Bhatia, Masahiro Oda, Takayuki Kitasaka, Shingo Iwano, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Julia A Schnabel, Kensaku Mori
This paper presents a local intensity structure analysis based on an intensity targeted radial structure tensor (ITRST) and the blob-like structure enhancement filter based on it (ITRST filter) for the mediastinal lymph node detection algorithm from chest computed tomography (CT) volumes. Although the filter based on radial structure tensor analysis (RST filter) based on conventional RST analysis can be utilized to detect lymph nodes, some lymph nodes adjacent to regions with extremely high or low intensities cannot be detected...
October 2017: Journal of Medical Imaging
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