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In radiology, several Health Information Systems (HISs) are commonly used: Electronic Health Records (EHR), Radiology Information System (RIS), and Picture Archive and Communication System (PACS). Each HIS records partial and complementary information about the radiologic care process. Depending on the institution, the HISs that touch radiology can be distinct, disparate, and with different formats and meta information. We note no reported research on integrating multiple HISs to allow for an end-to-end tracking of the care patients receive. Therefore, the current Quality of Care (QoC) research is limited as it can only utilize data for partial workflow analysis. A comprehensive assessment of the QoC in radiology requires HISs be integrated such that key metrics can be extracted. We developed a novel technology called Department Data Depot (DDD) that integrates multiple HISs in radiology. We propose nine metrics defined upon DDD data measuring various dimensions of care quality. To demonstrate the...

BACKGROUND AND PURPOSE: Intracerebral hemorrhage accounts for 6.5%–19.6% of all acute strokes. Initial intracerebral hemorrhage volume and expansion are both independent predictors of clinical outcomes and mortality. Therefore, a rapid, unbiased, and precise measurement of intracerebral hemorrhage volume is a key component of clinical management. The most commonly used method, ABC/2, results in overestimation. We developed an interactive segmentation program, SegTool, using a novel graphic processing unit, level set algorithm. Until now, the speed, bias, and precision of SegTool had not been validated. MATERIALS AND METHODS: In a single stroke academic center, 2 vascular neurologists and 2 neuroradiologists independently performed a test-retest experiment that involved repeat measurements of static, unchanging intracerebral hemorrhage volumes on CT from 76 intracerebral hemorrhage cases. Measurements were made with SegTool and ABC/2. True intracerebral hemorrhage volumes were estimated from a consensus of repeat manual tracings by 2 operators. These data allowed us to estimate measurement bias, precision, and speed. RESULTS: The measurements with SegTool were not significantly different from the true intracerebral hemorrhage volumes, while ABC/2 overestimated volume by 45%. The interrater measurement variability with SegTool was 50% less than that with ABC/2. The average measurement times for ABC/2 and SegTool were 35.7 and 44.6 seconds, respectively. CONCLUSIONS: SegTool appears to have attributes superior to ABC/2 in terms of accuracy and interrater reliability with a 9-second delay in measurement time (on average); hence, it could be useful in clinical trials and practice.

BACKGROUND AND PURPOSE: Because sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging–based texture analysis can accurately classify cases of noncoexistent squamous cell carcinoma and inverted papilloma and to compare this classification performance with neuroradiologists9 review. MATERIALS AND METHODS: Adult patients who had inverted papilloma or squamous cell carcinoma resected were eligible (coexistent inverted papilloma and squamous cell carcinoma were excluded). Inclusion required tumor size of >1.5 cm and preoperative MR imaging with axial T1, axial T2, and axial T1 postcontrast sequences. Five well-established texture analysis algorithms were applied to an ROI from the largest tumor cross-section. For a training dataset, machine-learning algorithms were used to identify the most accurate model, and performance was also evaluated in a validation dataset. On the basis of 3 separate blinded reviews of the ROI, isolated tumor, and entire images, 2 neuroradiologists predicted tumor type in consensus. RESULTS: The inverted papilloma (n = 24) and squamous cell carcinoma (n = 22) cohorts were matched for age and sex, while squamous cell carcinoma tumor volume was larger (P = .001). The best classification model achieved similar accuracies for training (17 squamous cell carcinomas, 16 inverted papillomas) and validation (7 squamous cell carcinomas, 6 inverted papillomas) datasets of 90.9% and 84.6%, respectively (P = .537). For the combined training and validation cohorts, the machine-learning accuracy (89.1%) was better than that of the neuroradiologists9 ROI review (56.5%, P = .0004) but not significantly different from the neuroradiologists9 review of the tumors (73.9%, P = .060) or entire images (87.0%, P = .748). CONCLUSIONS: MR imaging–based texture analysis has the potential to differentiate squamous cell carcinoma from inverted papilloma and may, in the future, provide incremental information to the neuroradiologist.

OBJECTIVE To determine whether machine learning can accurately classify human papillomavirus (HPV) status of oropharyngeal squamous cell carcinoma (OPSCC) using computed tomography (CT)-based texture analysis. METHODS Texture analyses were retrospectively applied to regions of interest from OPSCC primary tumors on contrast-enhanced neck CT, and machine learning was used to create a model that classified HPV status with the highest accuracy. Results were compared against the blinded review of 2 neuroradiologists. RESULTS The HPV-positive (n = 92) and -negative (n = 15) cohorts were well matched clinically. Neuroradiologist classification accuracies for HPV status (44.9%, 55.1%) were not significantly different (P = 0.13), and there was a lack of agreement between the 2 neuroradiologists (κ = -0.145). The best machine learning model had an accuracy of 75.7%, which was greater than either neuroradiologist (P < 0.001, P = 0.002). CONCLUSIONS Useful diagnostic information regarding HPV infection can be extracted from the CT appearance of OPSCC beyond what is apparent to the trained human eye.

Neoadjuvant endocrine therapy (NET) is increasingly used for the treatment of estrogen receptor positive, HER2 negative breast cancer. We evaluated whether MRI phenotype and background parenchymal enhancement (BPE) can predict response to NET. Patients with localized breast cancer treated with NET and had a pre-treatment breast MRI were identified. Baseline MRI phenotype and BPE was interpreted by a single radiologist blinded to the results of systemic therapy. Response was defined as stable disease or reduction in tumor size on clinical and/or ultrasound examination. Of the 21 patients identified, 17 were responders; all patients with minimal/mild BPE had a response compared to 5/9 (56%) patients with moderate/marked BPE (P = 0.02). All four nonresponders had moderate/marked BPE as compared to 5/17 (29%) responders (P = 0.02). This pilot study suggests that minimal/mild BPE may be predictive of a positive response to NET. A higher degree of background enhancement was significantly predictive of negative response to NET.

We introduce a new edge-directed interpolator based on locally defined, straight line approximations of image isophotes. Spatial derivatives of image intensity are used to describe the principal behavior of pixel-intersecting isophotes in terms of their slopes. The slopes are determined by inverting a tridiagonal matrix and are forced to vary linearly from pixel-to-pixel within segments. Image resizing is performed by interpolating along the approximated isophotes. The proposed method can accommodate arbitrary scaling factors, provides state-of-the-art results in terms of PSNR as well as other quantitative visual quality metrics, and has the advantage of reduced computational complexity that is directly proportional to the number of pixels.

Although coil embolization is one of the most effective treatments for intracranial aneurysms (ICAs), the procedure is often unsuccessful. For example, an ICA may persist after coil embolization if deployed coils fail to block the flow of blood into the aneurysm. Unfortunately, the specific flow changes that are effected by embolic coiling (and other endovascular therapies) are poorly understood, which creates a barrier to the design and execution of optimal treatments in the clinic. We present an in vitro pulsatile flow study of treated basilar tip aneurysm models that elucidates relationships between controllable treatment parameters and clinically important post-treatment fluid dynamics. We also compare fluid dynamic performance across embolic coils and more recently proposed devices (e.g., the Pipeline Embolization Device) that focus on treating ICAs by diverting rather than blocking blood flow. In agreement with previous steady flow studies, coil embolization-reduced velocity magnitude at the aneurysmal neck by greater percentages for a narrow-neck aneurysm, and reduced flow into aneurysms by greater percentages at lower parent vessel flow rates. However, flow diversion reduced flow into a wide-neck aneurysm more so than coil embolization, regardless of flow conditions. Finally, results also showed that for the endovascular devices we examined, treatment effects were generally less dramatic under physiologic pulsatile flow conditions as compared to steady flow conditions. The fluid dynamic performance data presented in this study represent the first direct in vitro comparison of coils and flow diverters in aneurysm models, and provide a novel, quantitative basis to aid in designing endovascular treatments toward specific fluid dynamic outcomes.

Motion analysis of video data is a powerful tool for studying dynamic behavior and determining sources of failures. In the case of failure analysis, the available video may be of poor quality, such as from surveillance cameras. It is also likely to have been taken from a bad angle, with poor lighting, and occlusions may be present. To address such cases, this paper presents an optical flow-based tracking algorithm incorporating physically-based constraints to extract motion data from video. The technique can accurately track a significant number of data points with a high degree of automation and efficiency. Many traditional methods of video data extraction from poor-quality video have proven tedious and time-consuming due to extensive user-input requirements. With this in mind, the proposed optical flow-based algorithm functions with a minimal degree of user involvement. Points identified at the outset of a video sequence, and within a small subset of frames spaced throughout, can be automatically tracked even when they become occluded or undergo translational, rotational, or deformational motion. The proposed algorithm improves upon previous optical flow-based tracking algorithms by providing greater flexibility and robustness. Example results are presented that show the method tracking machines with flexible components, Segway personal transporters, and athletes pole vaulting.

Background Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. Methods We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs

Background: For health care providers, mobile image viewing increases image accessibility, which could lead to faster interpretation/consultations and improved patient outcomes. Objective: We explored the technical requirements and challenges associated with implementing a commercial mobile image viewer and conducted a small study testing the hypothesis that the mobile image viewer would provide faster image access. Methods: A total of 19 clinicians (9 radiologists, 3 surgeons, 4 neurologists, and 3 physician assistants) evaluated (1) a desktop commercial picture archiving and communication system (PACS) viewer, (2) a desktop viewer developed internally over 20 years and deployed throughout the enterprise (ENTERPRISE viewer) and (3) a commercial Food and Drug Administration class II-cleared mobile viewer compatible with Web browsers, tablets, and mobile phones. Data were collected during two separate 7-day periods, before and after mobile image viewer deployment. Data included image viewer chosen, time to view first image, technical issues, diagnostic confidence, and ease of use. Results: For 565 image-viewing events, ease of use was identical for PACS and mobile viewers (mean 3.6 for all scores of a possible 4.0), and significantly worse for the enterprise viewer (mean 2.9, P =.001). Technical issues were highest with the enterprise viewer (26%, 56/215) compared with the mobile (7%,19/259, P =.001) and PACS (8%, 7/91, P =.003) viewers. Mean time to first image for the mobile viewer (2.4 minutes) was significantly faster than PACS (12.5 minutes, P =.001) and the enterprise viewer (4.5 minutes, P =.001). Diagnostic confidence was similar for PACS and mobile viewers and worst for enterprise viewer. Mobile image viewing increased by sixfold, from 14% (37/269, before the deployment) to 88.9% (263/296, after the deployment). Conclusions: A mobile viewer provided faster time to first image, improved technical performance, ease of use, and diagnostic confidence, compared with desktop image viewers. [JMIR Mhealth Uhealth 2015;3(2):e45]

e15626 Background: FGFR2 fusions are found in 10-15% of intrahepatic cholangiocarcinomas. Individuals with FGFR2 fusions have a better prognosis and more indolent natural history. More recently FGFR2 fusions have been described as a predictive marker for tyrosine kinase inhibitors targeting the FGFR2 pathway. Texture analysis from CT imaging has been used in the past to estimate degree of fibrosis and angiogenesis in various tumors types; however, texture analysis has not been used in the past to detect FGFR2 rearrangements. Methods: This is a retrospective study of 33 patients with intrahepatic cholangiocarcinoma who were previously tested for FGFR2 fusions with either a FISH break apart assay or with clinically available next generation sequencing assays. All patients had a contrast enhanced CT performed. To address physiological and pathological variations, matched-size regions of interest (ROIs) over areas of non-necrotic tumor and unaffected liver during the portal venous phase were drawn. The paired ROIs for each patient were then processed via an imaging analytic pipeline for (1) ROI normalization, (2) texture panel creation, and (3) machine learning predictive model development. Results: Fifteen patients with FGFR2 fusions and 18 patients without FGFR2 fusions were included in the study. Using the developed predictive model, accurate FGFR2 fusion classification in 90% of cases was achieved (sensitivity = 87%, specificity = 94%). Conclusions: Utilizing advanced analytics with machine learning techniques, multi-textural data obtained from conventional CT images has the capability to detect genetic aberrations in intrahepatic cholangiocarcinoma, including FGFR2 gene fusions. Radiogenomic evaluation has several attractive benefits including the cost effective and expeditious detection of actionable mutations such as FGFR2 fusions as well as avoidance of invasive procedures such as percutaneous core biopsies. [Table: see text]

Due to copyright restrictions, the access to the full text of this article is only available via subscription. Exceptional opportunities exist for researchers and practitioners to invest in conducting innovative and transformative research in data mining and health informatics. This IEEE Intelligent Systems "Trends and Controversies" (T&C) department hopes to raise awareness and highlight recent research to move toward such goals. The introduction, "Healthcare Intelligence: Turning Data into Knowledge," is written by Hui Yang and Erhun Kundakcioglu. Next, "Empowering Excellence of Care by Radiology Informatics" is written by Jing Li, Teresa Wu, J. Ross Mitchell, Amy K. Hara, William Pavlicek, Leland S. Hu, Alvin C. Silva, and Christine M. Zwart. Third, "Opportunities for Operations Research in Medical Decision Making" is written by Sait Tunc, Oguzhan Alagoz, and Elizabeth Burnside. Fourth, "Diagnostic Network Modeling of Neural Connectivity Using Functional Magnetic Resonance Imaging" is written by W. Art Chaovalitwongse, Georgiy Presnyakov, Yulian Cao, Sirirat Sujitnapitsatham, Daehan Won, Tara Madhyastha, Kurt E. Weaver, Paul R. Borghesani, and Thomas J. Grabowski. The final article, "Spatial Clustering in Public Health: Advances and Challenges," is written by Lianjie Shu, Man Ho Ling, Shui-Yee Wong, and Kwok-Leung Tsui. Macau Science and Technology Development Fund ; the Food and Health Bureau ; the Research Grants Council

Videos that capture accidents are usually of poor quality: they are likely to be taken from a bad angle, with poor lighting, and contain occluded points. However, the motion data contained in such videos can be very valuable for understanding and preventing accidents. To extract the motion of a body from a video: 1) the points of interest must be identified and 2) point tracking from frame-to-frame must be accomplished. Accordingly, one logical approach is to focus on automated tracking, while allowing a human to identify important points of interest [1].

Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This paper proposes a novel spatio-temporal video deinterlacing technique that adaptively chooses between results from segment adaptive gradient angle interpolation (SAGA), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and PSNR and requires minimal computational overhead. The algorithm performs better than existing approaches on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases.

Artificial displacement (the apparent motion of stationary objects) is one important component of atmospheric turbulence distortion, which has led many researchers to propose motion compensation as a solution. Defining a sufficiently dense set of motion estimates for successful restoration is challenging, particularly for time sensitive applications. We introduce a new, control grid implementation of optical flow that allows for rapid and analytical solutions to the motion estimation problem. Our results demonstrate the effectiveness of using the resulting motion field for removing articial displacements in turbulence distorted videos.

The importance of edge fidelity in image resizing is well established. Isophotes, or connected pixels of equal intensity, are essential to human perception of static images and interpolation methods that disrupt isophote curvature produce distracting artifacts. We introduce a new image resizing algorithm based on the principles of optical flow. The optical flow equation assumes that for every pixel in a given video frame, there exists an isointense pixel in adjacent frames. For video, this assumption implies that subsequent frames are reconfigurations of the same pixels. Here, we apply the optical flow equation to adjacent rows and columns of single images. The physical basis for optical flow in video (objects are moving) does not apply to static images. However, the the use of the optical flow equation amounts to asserting that each pixel is a member of an isophote with curvature that can be approximated locally with a straight line. Our implementation is fully separable and outperforms both traditional and recently proposed interpolators including NEDI and iNEDI.

With the advent of progressive format display and broadcast technologies, video deinterlacing has become an important video-processing technique. Numerous approaches exist in the literature to accomplish deinterlacing. While most earlier methods were simple linear filtering-based approaches, the emergence of faster computing technologies and even dedicated video-processing hardware in display units has allowed higher quality but also more computationally intense deinterlacing algorithms to become practical. Most modern approaches analyze motion and content in video to select different deinterlacing methods for various spatio- temporal regions. We introduce a family of deinterlacers that employs spectral residue to choose between and weight control grid interpolation based spatial and temporal deinterlacing methods. The proposed approaches perform better than the prior state-of-the-art based on peak signal-to-noise ratio, other visual quality metrics, and simple perception-based subjective evaluations conducted by human viewers. We further study the advantages of using soft and hard decision thresholds on the visual performance. © 2015 SPIE and IS&T (DOI: 10.1117/1.JEI.24.2.023022)

Interpolation is an essential and broadly employed function of signal processing. Accordingly, considerable development has focused on advancing interpolation algorithms toward optimal accuracy. Such development has motivated a clear shift in the state-of-the art from classical interpolation to more intelligent and resourceful approaches, registration-based interpolation for example. As a natural result, many of the most accurate current algorithms are highly complex, specific, and computationally demanding. However, the diverse hardware destinations for interpolation algorithms present unique constraints that often preclude use of the most accurate available options. For example, while computationally demanding interpolators may be suitable for highly equipped image processing platforms (e.g., computer workstations and clusters), only more efficient interpolators may be practical for less well equipped platforms (e.g., smartphones and tablet computers). The latter examples of consumer electronics present a design tradeoff in this regard: high accuracy interpolation benefits the consumer experience but computing capabilities are limited. It follows that interpolators with favorable combinations of accuracy and efficiency are of great practical value to the consumer electronics industry. We address multidimensional interpolation-based image processing problems that are common to consumer electronic devices through a decomposition approach. The multidimensional problems are first broken down into multiple, independent, one-dimensional (1-D) interpolation steps that are then executed with a newly modified registration-based one-dimensional control grid interpolator. The proposed approach, decomposed multidimensional control grid interpolation (DMCGI), combines the accuracy of registration-based interpolation with the simplicity, flexibility, and computational efficiency of a 1-D interpolation framework. Results demonstrate that DMCGI provides improved interpolation accuracy (and other benefits) in image resizing, color sample demosaicing, and video deinterlacing applications, at a computational cost that is manageable or reduced in comparison to popular alternatives.