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Journal of Medical Imaging

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https://read.qxmd.com/read/30766895/comparison-of-contrast-enhanced-digital-mammography-and-contrast-enhanced-digital-breast-tomosynthesis-for-lesion-assessment
#1
Hailiang Huang, David A Scaduto, Chunling Liu, Jie Yang, Chencan Zhu, Kim Rinaldi, Jason Eisenberg, Jingxuan Liu, Mathias Hoernig, Julia Wicklein, Sebastian Vogt, Thomas Mertelmeier, Paul R Fisher, Wei Zhao
Contrast-enhanced digital mammography (CEDM) reveals neovasculature of breast lesions in a two-dimensional contrast enhancement map. Contrast-enhanced digital breast tomosynthesis (CEDBT) provides contrast enhancement in three dimensions, which may improve lesion characterization and localization. We aim to compare CEDM and CEDBT for lesion assessment. Women with breast imaging-reporting and data system 4 or 5 suspicious breast lesion(s) were recruited in our study and were imaged with CEDM and CEDBT in succession under one breast compression...
July 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30746394/volumetric-breast-density-measurement-for-personalized-screening-accuracy-reproducibility-consistency-and-agreement-with-visual-assessment
#2
Andreas Fieselmann, Daniel Förnvik, Hannie Förnvik, Kristina Lång, Hanna Sartor, Sophia Zackrisson, Steffen Kappler, Ludwig Ritschl, Thomas Mertelmeier
Assessment of breast density at the point of mammographic examination could lead to optimized breast cancer screening pathways. The onsite breast density information may offer guidance of when to recommend supplemental imaging for women in a screening program. A software application (Insight BD, Siemens Healthcare GmbH) for fast onsite quantification of volumetric breast density is evaluated. The accuracy of the method is assessed using breast tissue equivalent phantom experiments resulting in a mean absolute error of 3...
July 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30746393/prediction-of-reader-estimates-of-mammographic-density-using-convolutional-neural-networks
#3
Georgia V Ionescu, Martin Fergie, Michael Berks, Elaine F Harkness, Johan Hulleman, Adam R Brentnall, Jack Cuzick, D Gareth Evans, Susan M Astley
Mammographic density is an important risk factor for breast cancer. In recent research, percentage density assessed visually using visual analogue scales (VAS) showed stronger risk prediction than existing automated density measures, suggesting readers may recognize relevant image features not yet captured by hand-crafted algorithms. With deep learning, it may be possible to encapsulate this knowledge in an automatic method. We have built convolutional neural networks (CNN) to predict density VAS scores from full-field digital mammograms...
July 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30662927/imaging-of-fiber-like-structures-in-digital-breast-tomosynthesis
#4
Sean D Rose, Emil Y Sidky, Ingrid Reiser, Xiaochuan Pan
Fiber-like features are an important aspect of breast imaging. Vessels and ducts are present in all breast images, and spiculations radiating from a mass can indicate malignancy. Accordingly, fiber objects are one of the three types of signals used in the American College of Radiology digital mammography (ACR-DM) accreditation phantom. Our work focuses on the image properties of fiber-like structures in digital breast tomosynthesis (DBT) and how image reconstruction can affect their appearance. The impact of DBT image reconstruction algorithm and regularization strength on the conspicuity of fiber-like signals of various orientations is investigated in simulation...
July 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30603658/comparison-of-screening-full-field-digital-mammography-and-digital-breast-tomosynthesis-technical-recalls
#5
Lonie R Salkowski, Mai Elezaby, Amy M Fowler, Elizabeth Burnside, Ryan W Woods, Roberta M Strigel
Enhancing quality using the inspection program (EQUIP) augments the FDA/MQSA program ensuring image quality review and implementation of corrective processes. We compared technical recalls between digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM). Prospectively recorded technical recalls of consecutive screening mammograms (10/2013 - 12/2017) were compared for imaging modality [FFDM, DBT + FFDM, DBT + synthesized mammography (SynM)], images requested, and indication(s) (motion, positioning, technical/artifact)...
July 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30525064/monochromatic-breast-computed-tomography-with-synchrotron-radiation-phase-contrast-and-phase-retrieved-image-comparison-and-full-volume-reconstruction
#6
Luca Brombal, Bruno Golosio, Fulvia Arfelli, Deborah Bonazza, Adriano Contillo, Pasquale Delogu, Sandro Donato, Giovanni Mettivier, Piernicola Oliva, Luigi Rigon, Angelo Taibi, Giuliana Tromba, Fabrizio Zanconati, Renata Longo
A program devoted to performing the first in vivo synchrotron radiation (SR) breast computed tomography (BCT) is ongoing at the Elettra facility. Using the high spatial coherence of SR, phase-contrast (PhC) imaging techniques can be used. The latest high-resolution BCT acquisitions of breast specimens, obtained with the propagation-based PhC approach, are herein presented as part of the SYRMA-3D collaboration effort toward the clinical exam. Images are acquired with a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>60</mml:mn> <mml:mtext>-</mml:mtext> <mml:mi>μ</mml:mi> <mml:mi>m</mml:mi> <mml:mtext> </mml:mtext> <mml:mtext>pixel</mml:mtext> </mml:mrow> </mml:math> dead-time-free single-photon-counting CdTe detector...
July 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30746392/adaptive-bayesian-label-fusion-using-kernel-based-similarity-metrics-in-hippocampus-segmentation
#7
David Cárdenas-Peña, Andres Tobar-Rodríguez, German Castellanos-Dominguez, Alzheimer's Disease Neuroimaging Initiative
The effectiveness of brain magnetic resonance imaging (MRI) as a useful evaluation tool strongly depends on the performed segmentation of associated tissues or anatomical structures. We introduce an enhanced brain segmentation approach of Bayesian label fusion that includes the construction of adaptive target-specific probabilistic priors using atlases ranked by kernel-based similarity metrics to deal with the anatomical variability of collected MRI data. In particular, the developed segmentation approach appraises patch-based voxel representation to enhance the voxel embedding in spaces with increased tissue discrimination, as well as the construction of a neighborhood-dependent model that addresses the label assignment of each region with a different patch complexity...
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30746391/evaluation-of-segmentation-algorithms-for-optical-coherence-tomography-images-of-ovarian-tissue
#8
Travis W Sawyer, Photini F S Rice, David M Sawyer, Jennifer W Koevary, Jennifer K Barton
Ovarian cancer has the lowest survival rate among all gynecologic cancers predominantly due to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depth-resolved, high-resolution images of biological tissue in real-time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must first be separated from the image background using the process of segmentation...
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30713851/impact-of-prevalence-and-case-distribution-in-lab-based-diagnostic-imaging-studies
#9
Brandon D Gallas, Weijie Chen, Elodia Cole, Robert Ochs, Nicholas Petrick, Etta D Pisano, Berkman Sahiner, Frank W Samuelson, Kyle J Myers
We investigated effects of prevalence and case distribution on radiologist diagnostic performance as measured by area under the receiver operating characteristic curve (AUC) and sensitivity-specificity in lab-based reader studies evaluating imaging devices. Our retrospective reader studies compared full-field digital mammography (FFDM) to screen-film mammography (SFM) for women with dense breasts. Mammograms were acquired from the prospective Digital Mammographic Imaging Screening Trial. We performed five reader studies that differed in terms of cancer prevalence and the distribution of noncancers...
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30662926/catheter-segmentation-in-three-dimensional-ultrasound-images-by-feature-fusion-and-model-fitting
#10
Hongxu Yang, Caifeng Shan, Arash Pourtaherian, Alexander F Kolen, Peter H N de With
Ultrasound (US) has been increasingly used during interventions, such as cardiac catheterization. To accurately identify the catheter inside US images, extra training for physicians and sonographers is needed. As a consequence, automated segmentation of the catheter in US images and optimized presentation viewing to the physician can be beneficial to accelerate the efficiency and safety of interventions and improve their outcome. For cardiac catheterization, a three-dimensional (3-D) US image is potentially attractive because of no radiation modality and richer spatial information...
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30662925/multiorgan-segmentation-using-distance-aware-adversarial-networks
#11
Roger Trullo, Caroline Petitjean, Bernard Dubray, Su Ruan
Segmentation of organs at risk (OAR) in computed tomography (CT) is of vital importance in radiotherapy treatment. This task is time consuming and for some organs, it is very challenging due to low-intensity contrast in CT. We propose a framework to perform the automatic segmentation of multiple OAR: esophagus, heart, trachea, and aorta. Different from previous works using deep learning techniques, we make use of global localization information, based on an original distance map that yields not only the localization of each organ, but also the spatial relationship between them...
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30603657/special-section-guest-editorial-artificial-intelligence-in-medical-imaging
#12
Elizabeth A Krupinski, Paul Kinahan, Patrick La Riviere
This editorial provides an overview of the articles in the special section.
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30397632/fully-connected-neural-network-for-virtual-monochromatic-imaging-in-spectral-computed-tomography
#13
Chuqing Feng, Kejun Kang, Yuxiang Xing
Spectral computed tomography (SCT) has advantages in multienergy material decomposition for material discrimination and quantitative image reconstruction. However, due to the nonideal physical effects of photon counting detectors, including charge sharing, pulse pileup and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> </mml:math> -escape, it is difficult to obtain precise system models in practical SCT systems. Serious spectral distortion is unavoidable, which introduces error into the decomposition model and affects material decomposition accuracy...
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30310824/breast-ultrasound-lesions-recognition-end-to-end-deep-learning-approaches
#14
Moi Hoon Yap, Manu Goyal, Fatima M Osman, Robert Martí, Erika Denton, Arne Juette, Reyer Zwiggelaar
Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast lesions. We use pretrained models based on ImageNet and transfer learning to overcome the issue of data deficiency. We evaluate our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions...
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30276222/evaluation-of-deep-learning-methods-for-parotid-gland-segmentation-from-ct-images
#15
Annika Hänsch, Michael Schwier, Tobias Gass, Tomasz Morgas, Benjamin Haas, Volker Dicken, Hans Meine, Jan Klein, Horst K Hahn
The segmentation of organs at risk is a crucial and time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and low contrast to surrounding structures, segmenting the parotid gland is challenging. Motivated by the recent success of deep learning, we study the use of two-dimensional (2-D), 2-D ensemble, and three-dimensional (3-D) U-Nets for segmentation. The mean Dice similarity to ground truth is <mml:math xmlns:mml="http://www...
January 2019: Journal of Medical Imaging
https://read.qxmd.com/read/30525063/moving-table-magnetic-particle-imaging-a-stepwise-approach-preserving-high-spatio-temporal-resolution
#16
Patryk Szwargulski, Nadine Gdaniec, Matthias Graeser, Martin Möddel, Florian Griese, Kannan M Krishnan, Thorsten M Buzug, Tobias Knopp
Magnetic particle imaging (MPI) is a highly sensitive imaging method that enables the visualization of magnetic tracer materials with a temporal resolution of more than 46 volumes per second. In MPI, the size of the field of view (FoV) scales with the strengths of the applied magnetic fields. In clinical applications, those strengths are limited by peripheral nerve stimulation, specific absorption rates, and the requirement to acquire images of high spatial resolution. Therefore, the size of the FoV is usually a few cubic centimeters...
October 2018: Journal of Medical Imaging
https://read.qxmd.com/read/30525062/multireader-sample-size-program-for-diagnostic-studies-demonstration-and-methodology
#17
Stephen L Hillis, Kevin M Schartz
The software "Multireader sample size program for diagnostic studies," written by Kevin Schartz and Stephen Hillis, performs sample size computations for diagnostic reader-performance studies. The program computes the sample size needed to detect a specified difference in a reader-performance measure between two imaging modalities when using the analysis methods initially proposed by Dorfman, Berbaum, and Metz, and Obuchowski and Rockette, and later unified and improved by Hillis and colleagues. A commonly used reader-performance measure is the area under the receiver-operating-characteristic curve...
October 2018: Journal of Medical Imaging
https://read.qxmd.com/read/30525061/phantom-with-multiple-active-points-for-ultrasound-calibration
#18
Haichong K Zhang, Alexis Cheng, Younsu Kim, Qianli Ma, Gregory S Chirikjian, Emad M Boctor
Accurate tracking and localization of ultrasound (US) images are used in various computer-assisted interventions. US calibration is a preoperative procedure to recover the transformation bridging the tracking sensor and the US image coordinate systems. Although many calibration phantom designs have been proposed, a limitation that hinders the resulted calibration accuracy is US elevational beam thickness. Previous studies have proposed an active-echo (AE)-based calibration concept to overcome this limitation by utilizing dynamic active US feedback from a single PZT element-based phantom, which assists in placing the phantom within the US elevational plane...
October 2018: Journal of Medical Imaging
https://read.qxmd.com/read/30525060/deep-neural-networks-for-a-line-based-plaque-classification-in-coronary-intravascular-optical-coherence-tomography-images
#19
Chaitanya Kolluru, David Prabhu, Yazan Gharaibeh, Hiram Bezerra, Giulio Guagliumi, David Wilson
We develop neural-network-based methods for classifying plaque types in clinical intravascular optical coherence tomography (IVOCT) images of coronary arteries. A single IVOCT pullback can consist of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>></mml:mo> <mml:mn>500</mml:mn> </mml:mrow> </mml:math> microscopic-resolution images, creating both a challenge for physician interpretation during an interventional procedure and an opportunity for automated analysis...
October 2018: Journal of Medical Imaging
https://read.qxmd.com/read/30397631/dynamic-fluence-field-modulation-for-miscentered-patients-in-computed-tomography
#20
Andrew Mao, Grace J Gang, William Shyr, Reuven Levinson, Jeffrey H Siewerdsen, Satomi Kawamoto, J Webster Stayman
Traditional CT image acquisition uses bowtie filters to reduce dose, x-ray scatter, and detector dynamic range requirements. However, accurate patient centering within the bore of the CT scanner takes time and is often difficult to achieve precisely. Patient miscentering combined with a static bowtie filter can result in significant increases in dose, reconstruction noise, and CT number variations, and consequently raise overall exposure requirements. Approaches to estimate the patient position from scout scans and perform dynamic spatial beam filtration during acquisition are developed and applied in physical experiments on a CT test bench using different beam filtration strategies...
October 2018: Journal of Medical Imaging
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