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Tomography: a Journal for Imaging Research

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https://www.readbyqxmd.com/read/28286871/semiautomated-workflow-for-clinically-streamlined-glioma-parametric-response-mapping
#1
Lauren Keith, Brian D Ross, Craig J Galbán, Gary D Luker, Stefanie Galbán, Binsheng Zhao, Xiaotao Guo, Thomas L Chenevert, Benjamin A Hoff
Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28149958/radiomics-of-lung-nodules-a-multi-institutional-study-of-robustness-and-agreement-of-quantitative-imaging-features
#2
Jayashree Kalpathy-Cramer, Artem Mamomov, Binsheng Zhao, Lin Lu, Dmitry Cherezov, Sandy Napel, Sebastian Echegaray, Daniel Rubin, Michael McNitt-Gray, Pechin Lo, Jessica C Sieren, Johanna Uthoff, Samantha K N Dilger, Brandan Driscoll, Ivan Yeung, Lubomir Hadjiiski, Kenny Cha, Yoganand Balagurunathan, Robert Gillies, Dmitry Goldgof
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28127586/accrual-patterns-for-clinical-studies-involving-quantitative-imaging-results-of-an-nci-quantitative-imaging-network-qin-survey
#3
Brenda F Kurland, Sameer Aggarwal, Thomas E Yankeelov, Elizabeth R Gerstner, James M Mountz, Hannah M Linden, Ella F Jones, Kellie L Bodeker, John M Buatti
Patient accrual is essential for the success of oncology clinical trials. Recruitment for trials involving the development of quantitative imaging biomarkers may face different challenges than treatment trials. This study surveyed investigators and study personnel for evaluating accrual performance and perceived barriers to accrual and for soliciting solutions to these accrual challenges that are specific to quantitative imaging-based trials. Responses for 25 prospective studies were received from 12 sites...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28111634/temporal-feature-extraction-from-dce-mri-to-identify-poorly-perfused-subvolumes-of-tumors-related-to-outcomes-of-radiation-therapy-in-head-and-neck-cancer
#4
Daekeun You, Madhava Aryal, Stuart E Samuels, Avraham Eisbruch, Yue Cao
This study aimed to develop an automated model to extract temporal features from DCE-MRI in head-and-neck (HN) cancers to localize significant tumor subvolumes having low blood volume (LBV) for predicting local and regional failure after chemoradiation therapy. Temporal features were extracted from time-intensity curves to build classification model for differentiating voxels with LBV from those with high BV. Support vector machine (SVM) classification was trained on the extracted features for voxel classification...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28105470/bladder-cancer-segmentation-in-ct-for-treatment-response-assessment-application-of-deep-learning-convolution-neural-network-a-pilot-study
#5
COMMENT
Kenny H Cha, Lubomir M Hadjiiski, Ravi K Samala, Heang-Ping Chan, Richard H Cohan, Elaine M Caoili, Chintana Paramagul, Ajjai Alva, Alon Z Weizer
Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases. 65 000 regions of interests were extracted from pre-treatment CT images to train a deep-learning convolution neural network (DL-CNN) for tumor boundary detection using leave-one-case-out cross-validation...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28105469/qin-dawg-validation-of-gradient-nonlinearity-bias-correction-workflow-for-quantitative-diffusion-weighted-imaging-in-multicenter-trials
#6
Dariya I Malyarenko, Lisa J Wilmes, Lori R Arlinghaus, Michael A Jacobs, Wei Huang, Karl G Helmer, Bachir Taouli, Thomas E Yankeelov, David Newitt, Thomas L Chenevert
Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28105468/simulating-the-effect-of-spectroscopic-mri-as-a-metric-for-radiation-therapy-planning-in-patients-with-glioblastoma
#7
J Scott Cordova, Shravan Kandula, Saumya Gurbani, Jim Zhong, Mital Tejani, Oluwatosin Kayode, Kirtesh Patel, Roshan Prabhu, Eduard Schreibmann, Ian Crocker, Chad A Holder, Hyunsuk Shim, Hui-Kuo Shu
Due to glioblastoma's infiltrative nature, an optimal radiation therapy (RT) plan requires targeting infiltration not identified by anatomical magnetic resonance imaging (MRI). Here, high-resolution, whole-brain spectroscopic MRI (sMRI) is used to describe tumor infiltration alongside anatomical MRI and simulate the degree to which it modifies RT target planning. In 11 patients with glioblastoma, data from preRT sMRI scans were processed to give high-resolution, whole-brain metabolite maps normalized by contralateral white matter...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28090589/spiral-perfusion-imaging-with-consecutive-echoes-spice%C3%A2-for-the-simultaneous-mapping-of-dsc-and-dce-mri-parameters-in-brain-tumor-patients-theory-and-initial-feasibility
#8
Eric S Paulson, Douglas E Prah, Kathleen M Schmainda
Dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) are the perfusion imaging techniques most frequently used to probe the angiogenic character of brain neoplasms. With these methods, T1- and T2/T2*-weighted imaging sequences are used to image the distribution of gadolinium (Gd)-based contrast agents. However, it is well known that Gd exhibits combined T1, T2, and T2* shortening effects in tissue, and therefore, the results of both DCE- and DSC-MRI can be confounded by these opposing effects...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28090588/quantitative-magnetization-transfer-imaging-of-the-breast-at-3-0-t-reproducibility-in-healthy-volunteers
#9
Lori R Arlinghaus, Richard D Dortch, Jennifer G Whisenant, Hakmook Kang, Richard G Abramson, Thomas E Yankeelov
Quantitative magnetization transfer magnetic resonance imaging provides a means for indirectly detecting changes in the macromolecular content of tissue noninvasively. A potential application is the diagnosis and assessment of treatment response in breast cancer; however, before quantitative magnetization transfer imaging can be reliably used in such settings, the technique's reproducibility in healthy breast tissue must be established. Thus, this study aims to establish the reproducibility of the measurement of the macromolecular-to-free water proton pool size ratio (PSR) in healthy fibroglandular (FG) breast tissue...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28083563/the-quantitative-imaging-network-in-precision-medicine
#10
Robert J Nordstrom
Precision medicine is a healthcare model that seeks to incorporate a wealth of patient information to identify and classify disease progression and to provide tailored therapeutic solutions for individual patients. Interventions are based on knowledge of molecular and mechanistic causes, pathogenesis and pathology of disease. Individual characteristics of the patients are then used to select appropriate healthcare options. Imaging is playing an increasingly important role in identifying relevant characteristics that help to stratify patients for different interventions...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066810/dynamic-susceptibility-contrast-mri-quantification-software-tool-development-and-evaluation
#11
Panagiotis Korfiatis, Timothy L Kline, Zachary S Kelm, Rickey E Carter, Leland S Hu, Bradley J Erickson
Relative cerebral blood volume (rCBV) is a magnetic resonance imaging biomarker that is used to differentiate progression from pseudoprogression in patients with glioblastoma multiforme, the most common primary brain tumor. However, calculated rCBV depends considerably on the software used. Automating all steps required for rCBV calculation is important, as user interaction can lead to increased variability and possible inaccuracies in clinical decision-making. Here, we present an automated tool for computing rCBV from dynamic susceptibility contrast-magnetic resonance imaging that includes leakage correction...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066809/deep-feature-transfer-learning-in-combination-with-traditional-features-predicts-survival-among-patients-with-lung-adenocarcinoma
#12
Rahul Paul, Samuel H Hawkins, Yoganand Balagurunathan, Matthew B Schabath, Robert J Gillies, Lawrence O Hall, Dmitry B Goldgof
Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066808/effect-of-mr-imaging-contrast-thresholds-on-prediction-of-neoadjuvant-chemotherapy-response-in-breast-cancer-subtypes-a-subgroup-analysis-of-the-acrin-6657-i-spy-1-trial
#13
Wen Li, Vignesh Arasu, David C Newitt, Ella F Jones, Lisa Wilmes, Jessica Gibbs, John Kornak, Bonnie N Joe, Laura J Esserman, Nola M Hylton
Functional tumor volume (FTV) measurements by dynamic contrast-enhanced magnetic resonance imaging can predict treatment outcomes for women receiving neoadjuvant chemotherapy for breast cancer. Here, we explore whether the contrast thresholds used to define FTV could be adjusted by breast cancer subtype to improve predictive performance. Absolute FTV and percent change in FTV (ΔFTV) at sequential time-points during treatment were calculated and investigated as predictors of pathologic complete response at surgery...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066807/evaluation-of-cross-calibrated-68-ge-68-ga-phantoms-for-assessing-pet-ct-measurement-bias-in-oncology-imaging-for-single-and-multicenter-trials
#14
Darrin W Byrd, Robert K Doot, Keith C Allberg, Lawrence R MacDonald, Wendy A McDougald, Brian F Elston, Hannah M Linden, Paul E Kinahan
Quantitative PET imaging is an important tool for clinical trials evaluating the response of cancers to investigational therapies. The standardized uptake value, used as a quantitative imaging biomarker, is dependent on multiple parameters that may contribute bias and variability. The use of long-lived, sealed PET calibration phantoms offers the advantages of known radioactivity activity concentration and simpler use than aqueous phantoms. We evaluated scanner and dose calibrator sources from two batches of commercially available kits, together at a single site and distributed across a local multicenter PET imaging network...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066806/automated-segmentation-of-hyperintense-regions-in-flair-mri-using-deep-learning
#15
Panagiotis Korfiatis, Timothy L Kline, Bradley J Erickson
We present a deep convolutional neural network application based on autoencoders aimed at segmentation of increased signal regions in fluid-attenuated inversion recovery magnetic resonance imaging images. The convolutional autoencoders were trained on the publicly available Brain Tumor Image Segmentation Benchmark (BRATS) data set, and the accuracy was evaluated on a data set where 3 expert segmentations were available. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to provide the ground truth for comparison, and Dice coefficient, Jaccard coefficient, true positive fraction, and false negative fraction were calculated...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066805/evaluation-of-soft-tissue-sarcoma-response-to-preoperative-chemoradiotherapy-using-dynamic-contrast-enhanced-magnetic-resonance-imaging
#16
Wei Huang, Brooke R Beckett, Alina Tudorica, Janelle M Meyer, Aneela Afzal, Yiyi Chen, Atiya Mansoor, James B Hayden, Yee-Cheen Doung, Arthur Y Hung, Megan L Holtorf, Torrie J Aston, Christopher W Ryan
This study aims to assess the utility of quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters in comparison with imaging tumor size for early prediction and evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy. In total, 20 patients with intermediate- to high-grade soft tissue sarcomas received either a phase I trial regimen of sorafenib + chemoradiotherapy (n = 8) or chemoradiotherapy only (n = 12), and underwent DCE-MRI at baseline, after 2 weeks of treatment with sorafenib or after the first chemotherapy cycle, and after therapy completion...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28044146/bloch-siegert-b1-mapping-improves-accuracy-and-precision-of-longitudinal-relaxation-measurements-in-the-breast-at-3-t
#17
Jennifer G Whisenant, Richard D Dortch, William Grissom, Hakmook Kang, Lori R Arlinghaus, Thomas E Yankeelov
Variable flip angle (VFA) sequences are a popular method of calculating T1 values, which are required in a quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). B1 inhomogeneities are substantial in the breast at 3 T, and these errors negatively impact the accuracy of the VFA approach, thus leading to large errors in the DCE-MRI parameters that could limit clinical adoption of the technique. This study evaluated the ability of Bloch-Siegert B1 mapping to improve the accuracy and precision of VFA-derived T1 measurements in the breast...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28367502/an-overdetermined-system-of-transform-equations-in-support-of-robust-dce-mri-registration-with-outlier-rejection
#18
Adam Johansson, James Balter, Mary Feng, Yue Cao
Quantitative hepatic perfusion parameters derived by fitting dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of liver to a pharmacokinetic model are prone to errors if the dynamic images are not corrected for respiratory motion by image registration. The contrast-induced intensity variations in pre- and postcontrast phases pose challenges for the accuracy of image registration. We propose an overdetermined system of transformation equations between the image volumes in the DCE-MRI series to achieve robust alignment...
September 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/27795998/a-classification-system-for-the-spread-of-polymethyl-methacrylate-in-vertebral-bodies-treated-with-vertebral-augmentation
#19
Joseph Frankl, Michael P Sakata, Gagandeep Choudhary, Seung Hur, Andrew Peterson, Charles T Hennemeyer
In this study, we develop a classification system for describing polymethyl methacrylate (PMMA) spread in vertebral bodies after kyphoplasty or vertebroplasty for vertebral compression fractures (VCFs) and for assessing whether PMMA spread varies between operators, VCF etiology, or vertebral level. Intraoperative fluoroscopic images of 198 vertebral levels were reviewed in 137 patients (women, 84; men, 53; mean age, 75.8 ± 12.5; and those with a diagnosis of osteoporosis, 63%) treated with kyphoplasty between January 01, 2015 and May 31, 2015 at a single center to create a 5-class descriptive system...
September 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/27774518/magnetic-resonance-imaging-based-radiomic-profiles-predict-patient-prognosis-in-newly-diagnosed-glioblastoma-before-therapy
#20
Sean D McGarry, Sarah L Hurrell, Amy L Kaczmarowski, Elizabeth J Cochran, Jennifer Connelly, Scott D Rand, Kathleen M Schmainda, Peter S LaViolette
Magnetic resonance imaging (MRI) is used to diagnose and monitor brain tumors. Extracting additional information from medical imaging and relating it to a clinical variable of interest is broadly defined as radiomics. Here, multiparametric MRI radiomic profiles (RPs) of de novo glioblastoma (GBM) brain tumors is related with patient prognosis. Clinical imaging from 81 patients with GBM before surgery was analyzed. Four MRI contrasts were aligned, masked by margins defined by gadolinium contrast enhancement and T2/fluid attenuated inversion recovery hyperintensity, and contoured based on image intensity...
September 2016: Tomography: a Journal for Imaging Research
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