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Journal of Medical Imaging

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https://www.readbyqxmd.com/read/28630887/calculation-of-the-entrance-skin-dose-distribution-for-fluoroscopically-guided-interventions-using-a-pencil-beam-backscatter-model
#1
Sarath Vijayan, Zhenyu Xiong, Stephen Rudin, Daniel R Bednarek
Radiation backscattered from the patient can contribute substantially to skin dose in fluoroscopically guided interventions (FGIs). The distribution of backscatter is not spatially uniform, and use of a single backscatter factor cannot provide an accurate determination of skin dose. This study evaluates a method to determine the backscatter spatial distribution through convolution of a backscatter-to-primary (BP) point spread function (PSFn). The PSFn is derived for a pencil beam using EGSnrc Monte Carlo software and is convolved with primary distributions using a dose-tracking system...
July 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28630886/review-of-the-current-status-of-radiation-protection-in-diagnostic-radiology-in-africa
#2
REVIEW
Wilbroad Muhogora, Madan M Rehani
The aim of this paper is to review the available published studies from African countries on patient doses and medical radiation protection and identify strengths, weaknesses, and challenges. Papers on radiation doses to patients published until 2016 pertaining to studies in African countries were reviewed. Radiography, interventional radiology, computed tomography (CT), and mammography modalities were covered. In radiography, the entrance surface air kerma values were below the established diagnostic reference levels (DRLs) provided by the International Atomic Energy Agency, European Commission, and National Council on Radiation Protection and Measurements...
July 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28630885/bayesian-framework-inspired-no-reference-region-of-interest-quality-measure-for-brain-mri-images
#3
Michael Osadebey, Marius Pedersen, Douglas Arnold, Katrina Wendel-Mitoraj
We describe a postacquisition, attribute-based quality assessment method for brain magnetic resonance imaging (MRI) images. It is based on the application of Bayes theory to the relationship between entropy and image quality attributes. The entropy feature image of a slice is segmented into low- and high-entropy regions. For each entropy region, there are three separate observations of contrast, standard deviation, and sharpness quality attributes. A quality index for a quality attribute is the posterior probability of an entropy region given any corresponding region in a feature image where quality attribute is observed...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28630884/evaluation-of-a-high-resolution-patient-specific-model-of-the-electrically-stimulated-cochlea
#4
Ahmet Cakir, Robert T Dwyer, Jack H Noble
Cochlear implants (CIs) are surgically implanted medical devices used to treat individuals with severe-to-profound sensorineural hearing loss. Although these devices have been remarkably successful at restoring audibility, many patients experience poor outcomes. Our group has developed the first image-guided CI programming technique where the electrode positions are found in CT images and used to estimate neural activation patterns, which is unique information that audiologists can use to define patient-specific processor settings...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28630883/detection-of-prostate-cancer-in-multiparametric-mri-using-random-forest-with-instance-weighting
#5
Nathan Lay, Yohannes Tsehay, Matthew D Greer, Baris Turkbey, Jin Tae Kwak, Peter L Choyke, Peter Pinto, Bradford J Wood, Ronald M Summers
A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28612039/erratum-large-field-of-view-optical-elastography-using-digital-image-correlation-for-biological-soft-tissue-investigation-erratum
#6
Daniel Claus, Marijo Mlikota, Jonathan Geibel, Thomas Reichenbach, Giancarlo Pedrini, Johannes Mischinger, Siegfried Schmauder, Wolfgang Osten
[This corrects the article DOI: 10.1117/1.JMI.4.1.014505.].
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28612038/resection-planning-for-robotic-acoustic-neuroma-surgery
#7
Kepra L McBrayer, George B Wanna, Benoit M Dawant, Ramya Balachandran, Robert F Labadie, Jack H Noble
Acoustic neuroma surgery is a procedure in which a benign mass is removed from the internal auditory canal (IAC). Currently, this surgical procedure requires manual drilling of the temporal bone followed by exposure and removal of the acoustic neuroma. This procedure is physically and mentally taxing to the surgeon. Our group is working on the development of an acoustic neuroma surgery robot (ANSR) to perform the initial drilling procedure. Planning the ANSR's drilling region using preoperative CT requires expertise and takes about 35 min...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28612037/large-scale-image-region-documentation-for-fully-automated-image-biomarker-algorithm-development-and-evaluation
#8
Anthony P Reeves, Yiting Xie, Shuang Liu
With the advent of fully automated image analysis and modern machine learning methods, there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. This paper presents a method and implementation for facilitating such datasets that addresses the critical issue of size scaling for algorithm validation and evaluation; current evaluation methods that are usually used in academic studies do not scale to large datasets. This method includes protocols for the documentation of many regions in very large image datasets; the documentation may be incrementally updated by new image data and by improved algorithm outcomes...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28612036/mixed-spine-metastasis-detection-through-positron-emission-tomography-computed-tomography-synthesis-and-multiclassifier
#9
Jianhua Yao, Joseph E Burns, Vic Sanoria, Ronald M Summers
Bone metastases are a frequent occurrence with cancer, and early detection can guide the patient's treatment regimen. Metastatic bone disease can present in density extremes as sclerotic (high density) and lytic (low density) or in a continuum with an admixture of both sclerotic and lytic components. We design a framework to detect and characterize the varying spectrum of presentation of spine metastasis on positron emission tomography/computed tomography (PET/CT) data. A technique is proposed to synthesize CT and PET images to enhance the lesion appearance for computer detection...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28612035/method-for-detecting-voxelwise-changes-in-fluorodeoxyglucose-positron-emission-tomography-brain-images-via-background-adjustment-in-cancer-clinical-trials
#10
Lei Qin, Armin Schwartzman, Keisha McCall, Nezamoddin N Kachouie, Jeffrey T Yap
An important challenge to using fluorodeoxyglucose-positron emission tomography (FDG-PET) in clinical trials of brain tumor patients is to identify malignant regions whose metabolic activity shows significant changes between pretreatment and a posttreatment scans in the presence of high normal brain background metabolism. This paper describes a semiautomated processing and analysis pipeline that is able to detect such changes objectively with a given false detection rate. Image registration and voxelwise comparison of the pre- and posttreatment images were performed...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28560245/assistive-lesion-emphasis-system-an-assistive-system-for-fundus-image-readers
#11
Samrudhdhi B Rangrej, Jayanthi Sivaswamy
Computer-assisted diagnostic (CAD) tools are of interest as they enable efficient decision-making in clinics and the screening of diseases. The traditional approach to CAD algorithm design focuses on the automated detection of abnormalities independent of the end-user, who can be an image reader or an expert. We propose a reader-centric system design wherein a reader's attention is drawn to abnormal regions in a least-obtrusive yet effective manner, using saliency-based emphasis of abnormalities and without altering the appearance of the background tissues...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28560244/toward-real-time-quantification-of-fluorescence-molecular-probes-using-target-background-ratio-for-guiding-biopsy-and-endoscopic-therapy-of-esophageal-neoplasia
#12
Yang Jiang, Yuanzheng Gong, Joel H Rubenstein, Thomas D Wang, Eric J Seibel
Multimodal endoscopy using fluorescence molecular probes is a promising method of surveying the entire esophagus to detect cancer progression. Using the fluorescence ratio of a target compared to a surrounding background, a quantitative value is diagnostic for progression from Barrett's esophagus to high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC). However, current quantification of fluorescent images is done only after the endoscopic procedure. We developed a Chan-Vese-based algorithm to segment fluorescence targets, and subsequent morphological operations to generate background, thus calculating target/background (T/B) ratios, potentially to provide real-time guidance for biopsy and endoscopic therapy...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28560243/semiautomated-biventricular-segmentation-in-three-dimensional-echocardiography-by-coupled-deformable-surfaces
#13
Jørn Bersvendsen, Fredrik Orderud, Øyvind Lie, Richard John Massey, Kristian Fosså, Raúl San José Estépar, Stig Urheim, Eigil Samset
With the advancement of three-dimensional (3-D) real-time echocardiography in recent years, automatic creation of patient specific geometric models is becoming feasible and important in clinical decision making. However, the vast majority of echocardiographic segmentation methods presented in the literature focus on the left ventricle (LV) endocardial border, leaving segmentation of the right ventricle (RV) a largely unexplored problem, despite the increasing recognition of the RV's role in cardiovascular disease...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28560242/segmented-targeted-least-squares-estimator-for-material-decomposition-in-multibin-photon-counting-detectors
#14
Paurakh L Rajbhandary, Scott S Hsieh, Norbert J Pelc
We present a fast, noise-efficient, and accurate estimator for material separation using photon-counting x-ray detectors (PCXDs) with multiple energy bin capability. The proposed targeted least squares estimator (TLSE) is an improvement of a previously described A-table method by incorporating dynamic weighting that allows the variance to be closer to the Cramér-Rao lower bound (CRLB) throughout the operating range. We explore Cartesian and average-energy segmentation of the basis material space for TLSE and show that, compared with Cartesian segmentation, the average-energy method requires fewer segments to achieve similar performance...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28523284/enhanced-axial-and-lateral-resolution-using-stabilized-pulses
#15
Shujie Chen, Kevin J Parker
Ultrasound B-scan imaging systems operate under some well-known resolution limits. To improve resolution, the concept of stable pulses, having bounded inverse filters, was previously utilized for the lateral deconvolution. This framework has been extended to the axial direction, enabling a two-dimensional deconvolution. The modeling of the two-way response in the axial direction is discussed, and the deconvolution is performed in the in-phase quadrature data domain. Stable inverse filters are generated and applied for the deconvolution of the image data from Field II simulation, a tissue-mimicking phantom, and in vivo imaging of a carotid artery, where resolution enhancement is observed...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28523283/variable-temporal-sampling-and-tube-current-modulation-for-myocardial-blood-flow-estimation-from-dose-reduced-dynamic-computed-tomography
#16
Dimple Modgil, Michael D Bindschadler, Adam M Alessio, Patrick J La Rivière
Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease. However, there are no widely accepted clinical methods for estimating MBF. Dynamic cardiac perfusion computed tomography (CT) holds the promise of providing a quick and easy method to measure MBF quantitatively. However, the need for repeated scans can potentially result in a high patient radiation dose, limiting the clinical acceptance of this approach. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28523282/comparative-study-of-computational-visual-attention-models-on-two-dimensional-medical-images
#17
Gezheng Wen, Brenda Rodriguez-Niño, Furkan Y Pecen, David J Vining, Naveen Garg, Mia K Markey
Computational modeling of visual attention is an active area of research. These models have been successfully employed in applications such as robotics. However, most computational models of visual attention are developed in the context of natural scenes, and their role with medical images is not well investigated. As radiologists interpret a large number of clinical images in a limited time, an efficient strategy to deploy their visual attention is necessary. Visual saliency maps, highlighting image regions that differ dramatically from their surroundings, are expected to be predictive of where radiologists fixate their gaze...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28491908/lack-of-agreement-between-radiologists-implications-for-image-based-model-observers
#18
Juhun Lee, Robert M Nishikawa, Ingrid Reiser, Margarita L Zuley, John M Boone
We tested the agreement of radiologists' rankings of different reconstructions of breast computed tomography images based on their diagnostic (classification) performance and on their subjective image quality assessments. We used 102 pathology proven cases (62 malignant, 40 benign), and an iterative image reconstruction (IIR) algorithm to obtain 24 reconstructions per case with different image appearances. Using image feature analysis, we selected 3 IIRs and 1 clinical reconstruction and 50 lesions. The reconstructions produced a range of image quality from smooth/low-noise to sharp/high-noise, which had a range in classifier performance corresponding to AUCs of 0...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28491907/comprehensive-assessment-of-patient-image-quality-and-radiation-dose-in-latest-generation-cardiac-x-ray-equipment-for-percutaneous-coronary-interventions
#19
Amber J Gislason-Lee, Claire Keeble, Daniel Egleston, Josephine Bexon, Stephen M Kengyelics, Andrew G Davies
This study aimed to determine whether a reduction in radiation dose was found for percutaneous coronary interventional (PCI) patients using a cardiac interventional x-ray system with state-of-the-art image enhancement and x-ray optimization, compared to the current generation x-ray system, and to determine the corresponding impact on clinical image quality. Patient procedure dose area product (DAP) and fluoroscopy duration of 131 PCI patient cases from each x-ray system were compared using a Wilcoxon test on median values...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28466029/global-detection-approach-for-clustered-microcalcifications-in-mammograms-using-a-deep-learning-network
#20
Juan Wang, Robert M Nishikawa, Yongyi Yang
In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size)...
April 2017: Journal of Medical Imaging
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