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Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology

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https://www.readbyqxmd.com/read/30242780/3d-brain-imaging-in-vascular-segmentation-of-cerebral-venous-sinuses
#1
Asli Beril Karakas, Figen Govsa, Mehmet Asım Ozer, Cenk Eraslan
The three-dimensional (3D) visualization of dural venous sinuses (DVS) networks is desired by surgical trainers to create a clear mental picture of the neuroanatomical orientation of the complex cerebral anatomy. Our purpose is to document those identified during routine 3D venography created through 3D models using two-dimensional axial images for teaching and learning neuroanatomy. Anatomical data were segmented and extracted from imaging of the DVS of healthy people. The digital data of the extracted anatomical surfaces was then edited and smoothed, resulting in a set of digital 3D models of the superior sagittal, inferior sagittal, transverse, and sigmoid, rectus sinuses, and internal jugular veins...
September 21, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30238345/restoration-of-full-data-from-sparse-data-in-low-dose-chest-digital-tomosynthesis-using-deep-convolutional-neural-networks
#2
Donghoon Lee, Hee-Joung Kim
Chest digital tomosynthesis (CDT) provides more limited image information required for diagnosis when compared to computed tomography. Moreover, the radiation dose received by patients is higher in CDT than in chest radiography. Thus, CDT has not been actively used in clinical practice. To increase the usefulness of CDT, the radiation dose should reduce to the level used in chest radiography. Given the trade-off between image quality and radiation dose in medical imaging, a strategy to generating high-quality images from limited data is need...
September 20, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30238344/a-compressed-sensing-based-blind-deconvolution-method-for-image-deblurring-in-dental-cone-beam-computed-tomography
#3
K S Kim, S Y Kang, C K Park, G A Kim, S Y Park, Hyosung Cho, C W Seo, D Y Lee, H W Lim, H W Lee, J E Park, T H Woo, J E Oh
In cone-beam computed tomography (CBCT), reconstructed images are inherently degraded, restricting its image performance, due mainly to imperfections in the imaging process resulting from detector resolution, noise, X-ray tube's focal spot, and reconstruction procedure as well. Thus, the recovery of CBCT images from their degraded version is essential for improving image quality. In this study, we investigated a compressed-sensing (CS)-based blind deconvolution method to solve the blurring problem in CBCT where both the image to be recovered and the blur kernel (or point-spread function) of the imaging system are simultaneously recursively identified...
September 20, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30225824/limitations-in-and-solutions-for-improving-the-functionality-of-picture-archiving-and-communication-system-an-exploratory-study-of-pacs-professionals-perspectives
#4
Mona Alhajeri, Syed Ghulam Sarwar Shah
Picture Archiving and Communication System (PACS) technology is evolving leading to improvements in the PACS functionality. However, the needs and expectations of PACS users are increasing to cope with the rising demands for improving the workflow and enhancing efficiency in healthcare. The aim was to study the limitations in the current generation of PACS and solutions for improving PACS functionality. This was a longitudinal online observational study of the perspectives of PACS professionals accessed through four online discussion groups on PACS using the LinkedIn network...
September 17, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30215180/applying-modern-virtual-and-augmented-reality-technologies-to-medical-images-and-models
#5
REVIEW
Justin Sutherland, Jason Belec, Adnan Sheikh, Leonid Chepelev, Waleed Althobaity, Benjamin J W Chow, Dimitrios Mitsouras, Andy Christensen, Frank J Rybicki, Daniel J La Russa
Recent technological innovations have created new opportunities for the increased adoption of virtual reality (VR) and augmented reality (AR) applications in medicine. While medical applications of VR have historically seen greater adoption from patient-as-user applications, the new era of VR/AR technology has created the conditions for wider adoption of clinician-as-user applications. Historically, adoption to clinical use has been limited in part by the ability of the technology to achieve a sufficient quality of experience...
September 13, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30187316/oipav-an-integrated-software-system-for-ophthalmic-image-processing-analysis-and-visualization
#6
REVIEW
Lichun Zhang, Dehui Xiang, Chao Jin, Fei Shi, Kai Yu, Xinjian Chen
Ophthalmic medical images, such as optical coherence tomography (OCT) images and color photo of fundus, provide valuable information for clinical diagnosis and treatment of ophthalmic diseases. In this paper, we introduce a software system specially oriented to ophthalmic images processing, analysis, and visualization (OIPAV) to assist users. OIPAV is a cross-platform system built on a set of powerful and widely used toolkit libraries. Based on the plugin mechanism, the system has an extensible framework. It provides rich functionalities including data I/O, image processing, interaction, ophthalmic diseases detection, data analysis, and visualization...
September 5, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30187315/automatic-mapping-of-ct-scan-locations-on-computational-human-phantoms-for-organ-dose-estimation
#7
Choonsik Lee, Gleb A Kuzmin, Jinyong Bae, Jianhua Yao, Elizabeth Mosher, Les R Folio
To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center...
September 5, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30187314/using-an-existing-dicom-infrastructure-to-enhance-the-availability-quality-and-efficiency-of-imaging-throughout-the-healthcare-enterprise
#8
Dan Kayhart
Managing the capture, label, and storage of medical imaging performed by nonradiology departments at the point of care has become a core issue for many health systems. In contrast to the well-organized and controlled workflows enjoyed by radiology, nonradiology imaging and its associated workflows are often chaotic. Left on their own, many nonradiology departments simply fend for themselves, finding ways to capture and store their imaging that meets the critical need, while falling far short of the ideal. The focus of this study was to build and implement a software solution for nonradiology image management for a large, multi-specialty health system...
September 4, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30128778/large-scale-semi-automated-labeling-of-routine-free-text-clinical-records-for-deep-learning
#9
Hari M Trivedi, Maryam Panahiazar, April Liang, Dmytro Lituiev, Peter Chang, Jae Ho Sohn, Yunn-Yi Chen, Benjamin L Franc, Bonnie Joe, Dexter Hadley
Breast cancer is a leading cause of cancer death among women in the USA. Screening mammography is effective in reducing mortality, but has a high rate of unnecessary recalls and biopsies. While deep learning can be applied to mammography, large-scale labeled datasets, which are difficult to obtain, are required. We aim to remove many barriers of dataset development by automatically harvesting data from existing clinical records using a hybrid framework combining traditional NLP and IBM Watson. An expert reviewer manually annotated 3521 breast pathology reports with one of four outcomes: left positive, right positive, bilateral positive, negative...
August 20, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30109521/synchronization-and-alignment-of-follow-up-examinations-a-practical-and-educational-approach-using-the-dicom-reference-coordinate-system
#10
Sebastian Nowak, Alois M Sprinkart
This work presents an approach for synchronization and alignment of Digital Imaging and Communications in Medicine (DICOM) series from different studies that allows, e.g., easier reading of follow-up examinations. The proposed concept developed within the DICOM's patient-based reference coordinate system allows to synchronize all image data of two different studies/examinations based on a single registration. The most suitable DICOM series for registration could be set as default per protocol. Necessary basics regarding the DICOM standard and the used mathematical transformations are presented in an educative way to allow straightforward implementation in Picture Archiving And Communications Systems (PACS) and other DICOM tools...
August 14, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30097747/automatic-detection-of-negated-findings-in-radiological-reports-for-spanish-language-methodology-based-on-lexicon-grammatical-information-processing
#11
Walter Koza, Darío Filippo, Viviana Cotik, Vanesa Stricker, Mirian Muñoz, Ninoska Godoy, Natalia Rivas, Ricardo Martínez-Gamboa
We present a methodology for the automatic recognition of negated findings in radiological reports considering morphological, syntactic, and semantic information. In order to achieve this goal, a series of rules for processing lexical and syntactic information was elaborated. This required development of an electronic dictionary of medical terminology and informatics grammars. Pertinent information for the assembly of the specialized dictionary was extracted from the ontology SNOMED CT and a medical dictionary (RANM, 2012)...
August 10, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30091112/a-new-optimized-thresholding-method-using-ant-colony-algorithm-for-mr-brain-image-segmentation
#12
Bahar Khorram, Mehran Yazdi
Image segmentation is considered as one of the most fundamental tasks in image processing applications. Segmentation of magnetic resonance (MR) brain images is also an important pre-processing step, since many neural disorders are associated with brain's volume changes. As a result, brain image segmentation can be considered as an essential measure toward automated diagnosis or interpretation of regions of interest, which can help surgical planning, analyzing changes of brain's volume in different tissue types, and identifying neural disorders...
August 8, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30088157/an-automatic-parameter-decision-system-of-bilateral-filtering-with-gpu-based-acceleration-for-brain-mr-images
#13
Herng-Hua Chang, Yu-Ju Lin, Audrey Haihong Zhuang
Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks. In our approach, parallel computing with the GPU architecture is first developed to accelerate the computation of the conventional bilateral filter...
August 7, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30076490/automatic-normalization-of-anatomical-phrases-in-radiology-reports-using-unsupervised-learning
#14
Amir M Tahmasebi, Henghui Zhu, Gabriel Mankovich, Peter Prinsen, Prescott Klassen, Sam Pilato, Rob van Ommering, Pritesh Patel, Martin L Gunn, Paul Chang
In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology reports of a patient to recall critical information for new diagnosis, which is quite tedious, time consuming, and prone to human error. Automatic structuring of report content is desired to facilitate such inquiry of information. In this work, we propose an unsupervised machine learning approach to automatically structure radiology reports by detecting and normalizing anatomical phrases based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) ontology...
August 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30076489/fully-automated-convolutional-neural-network-method-for-quantification-of-breast-mri-fibroglandular-tissue-and-background-parenchymal-enhancement
#15
Richard Ha, Peter Chang, Eralda Mema, Simukayi Mutasa, Jenika Karcich, Ralph T Wynn, Michael Z Liu, Sachin Jambawalikar
The aim of this study is to develop a fully automated convolutional neural network (CNN) method for quantification of breast MRI fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). An institutional review board-approved retrospective study evaluated 1114 breast volumes in 137 patients using T1 precontrast, T1 postcontrast, and T1 subtraction images. First, using our previously published method of quantification, we manually segmented and calculated the amount of FGT and BPE to establish ground truth parameters...
August 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30066123/tumor-identification-in-colorectal-histology-images-using-a-convolutional-neural-network
#16
Hongjun Yoon, Joohyung Lee, Ji Eun Oh, Hong Rae Kim, Seonhye Lee, Hee Jin Chang, Dae Kyung Sohn
Colorectal cancer (CRC) is a major global health concern. Its early diagnosis is extremely important, as it determines treatment options and strongly influences the length of survival. Histologic diagnosis can be made by pathologists based on images of tissues obtained from a colonoscopic biopsy. Convolutional neural networks (CNNs)-i.e., deep neural networks (DNNs) specifically adapted to image data-have been employed to effectively classify or locate tumors in many types of cancer. Colorectal histology images of 28 normal and 29 tumor samples were obtained from the National Cancer Center, South Korea, and cropped into 6806 normal and 3474 tumor images...
July 31, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30066122/ontology-based-approach-for-liver-cancer-diagnosis-and-treatment
#17
Rim Messaoudi, Faouzi Jaziri, Achraf Mtibaa, Manuel Grand-Brochier, Hawa Mohamed Ali, Ali Amouri, Hela Fourati, Pascal Chabrot, Faiez Gargouri, Antoine Vacavant
Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC...
July 31, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30039425/adaptahead-optimization-algorithm-for-learning-deep-cnn-applied-to-mri-segmentation
#18
Farnaz Hoseini, Asadollah Shahbahrami, Peyman Bayat
Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features. Previously, in optimization algorithms, the accuracy achieved for network training was less and high-cost function...
July 23, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30030766/using-virtual-reality-to-improve-performance-and-user-experience-in-manual-correction-of-mri-segmentation-errors-by-non-experts
#19
Dominique Duncan, Rachael Garner, Ivan Zrantchev, Tyler Ard, Bradley Newman, Adam Saslow, Emily Wanserski, Arthur W Toga
Segmentation of MRI scans is a critical part of the workflow process before we can further analyze neuroimaging data. Although there are several automatic tools for segmentation, no segmentation software is perfectly accurate, and manual correction by visually inspecting the segmentation errors is required. The process of correcting these errors is tedious and time-consuming, so we present a novel method of performing this task in a head-mounted virtual reality interactive system with a new software, Virtual Brain Segmenter (VBS)...
July 20, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/30030765/who-pacs-a-punch-the-role-of-the-picture-archiving-and-communication-system-radiology-information-system-pacs-ris-in-quantifying-experiential-learning-in-radiology-residency
#20
Abraham Gerhardus Wilhelmus Greyling, Richard Denys Pitcher
The clinical logbook is the currently accepted tool for evaluating experiential learning (EL) in postgraduate radiology training programs internationally. The role of the picture archiving and communication system/radiology information system (PACS/RIS) in defining the complete EL portfolio of radiology residents has not been explored. To conduct a PACS/RIS-based analysis of the comprehensive clinical outputs of radiology residents, and to correlate outputs with residency recruitment criteria and exit examination performance...
July 20, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
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