Faculty Bibliography

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background

Provided methodologic training, more imagers can contribute to the evidence basis on improved health outcomes and value in diagnostic imaging. The Value of Imaging Through Comparative Effectiveness Research Program was developed to provide hands-on, practical training in five core areas for comparative effectiveness and big biomedical data research: decision analysis, cost-effectiveness analysis, evidence synthesis, big data principles, and applications of big data analytics. The program's mixed format consists of web-based modules for asynchronous learning as well as in-person sessions for practical skills and group discussion. Seven diagnostic radiology subspecialties and cardiology are represented in the first group of program participants, showing the collective potential for greater depth of comparative effectiveness research in the imaging community.

Radiologists are facing increasing workplace pressures that can lead to decreased job satisfaction and burnout. The increasing complexity and volumes of cases and increasing numbers of noninterpretive tasks, compounded by decreasing reimbursements and visibility in this digital age, have created a critical need to develop innovations that optimize workflow, increase radiologist engagement, and enhance patient care. During their workday, radiologists often must navigate through multiple software programs, including picture archiving and communication systems, electronic health records, and dictation software. Furthermore, additional noninterpretive duties can interrupt image review. Fragmented data and frequent task switching can create frustration and potentially affect patient care. Despite the current successful technological advancements across industries, radiology software systems often remain nonintegrated and not leveraged to their full potential. Each step of the imaging process can be enhanced with use of information technology (IT). Successful implementation of IT innovations requires a collaborative team of radiologists, IT professionals, and software programmers to develop customized solutions. This article includes a discussion of how IT tools are used to improve many steps of the imaging process, including examination protocoling, image interpretation, reporting, communication, and radiologist feedback. ^©RSNA, 2018.

PURPOSE: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. THEORY AND METHODS: Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. RESULTS: The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. CONCLUSION: Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med, 2017. (c) 2017 International Society for Magnetic Resonance in Medicine.

OBJECTIVES AND RATIONALE/UNASSIGNED:Radiology-pathology correlation is time-consuming and is not feasible in most clinical settings, with the notable exception of breast imaging. The purpose of this study was to determine if an automated radiology-pathology report pairing system could accurately match radiology and pathology reports, thus creating a feedback loop allowing for more frequent and timely radiology-pathology correlation. METHODS:An experienced radiologist created a matching matrix of radiology and pathology reports. These matching rules were then exported to a novel comprehensive radiology-pathology module. All distinct radiology-pathology pairings at our institution from January 1, 2016 to July 1, 2016 were included (n = 8999). The appropriateness of each radiology-pathology report pairing was scored as either "correlative" or "non-correlative." Pathology reports relating to anatomy imaged in the specific imaging study were deemed correlative, whereas pathology reports describing anatomy not imaged with the particular study were denoted non-correlative. RESULTS:Overall, there was 88.3% correlation (accuracy) of the radiology and pathology reports (n = 8999). Subset analysis demonstrated that computed tomography (CT) abdomen/pelvis, CT head/neck/face, CT chest, musculoskeletal CT (excluding spine), mammography, magnetic resonance imaging (MRI) abdomen/pelvis, MRI brain, musculoskeletal MRI (excluding spine), breast MRI, positron emission tomography (PET), breast ultrasound, and head/neck ultrasound all demonstrated greater than 91% correlation. When further stratified by imaging modality, CT, MRI, mammography, and PET demonstrated excellent correlation (greater than 96.3%). Ultrasound and non-PET nuclear medicine studies demonstrated poorer correlation (80%). CONCLUSION/CONCLUSIONS:There is excellent correlation of radiology imaging reports and appropriate pathology reports when matched by organ system. Rapid, appropriate radiology-pathology report pairings provide an excellent opportunity to close feedback loop to the interpreting radiologist.

RATIONALE AND OBJECTIVES: Correlation of imaging studies and reference standard outcomes is a significant challenge in radiology. This study evaluates the effectiveness of a new communication tool by assessing the ability of this system to correctly match the imaging studies to arthroscopy reports and qualitatively assessing radiologist behavior before and after the implementation of this system. MATERIALS AND METHODS: Using a commercially available communication or educational tool and applying a novel matching rule algorithm, radiology and arthroscopy reports were matched from January 17, 2017 to March 1, 2017 based on anatomy. The interpreting radiologist was presented with email notifications containing the impression of the imaging report and the entire arthroscopy report. Total correlation rate of appropriate report pairings, modality-specific correlation rate, and the anatomy-specific correlation rate were calculated. Radiologists using the system were given a survey. RESULTS: Overall correlation rate for all musculoskeletal imaging was 83.1% (433 or 508). Low correlation was found in fluoroscopic procedures at 74.4%, and the highest correlation was found with ultrasound at 88.4%. Anatomic location varied from 51.6% for spine to 98.8% for hips and pelvis studies. Survey results revealed 87.5% of the respondents reporting being either satisfied or very satisfied with the new communication tool. The survey also revealed that some radiologists reviewed more cases than before. CONCLUSIONS: Matching of radiology and arthroscopy reports by anatomy allows for excellent report correlation (83.1%). Automated correlation improves the quality and efficiency of feedback to radiologists, providing important opportunities for learning and improved accuracy.

Radiology has historically been at the forefront of innovation and the advancement of technology for the benefit of patient care. However, challenges to early implementation prevented most radiologists from adopting and integrating certified electronic health record technology (CEHRT) into their daily workflow despite the early and potential advantages it offered. This circumstance places radiology at a disadvantage in the two payment pathways of the Medicare Access and CHIP Reauthorization Act of 2015: the Merit-Based Incentive Payment System (MIPS) and advanced alternative payment models (APMs). Specifically, not integrating CEHRT hampers radiology's ability to receive bonus points in the quality performance category of the MIPS and in parallel threatens certain threshold requirements for advanced APMs under the new Quality Payment Program. Radiology must expand the availability and use of CEHRT to satisfy existing performance measures while creating new performance measures that create value for the health care system. In addition, radiology IT vendors will need to ensure their products (eg, radiology information systems, PACS, and radiology reporting systems) are CEHRT compliant and approved. Such collective efforts will increase radiologists' quality of patient care, contribution to value driven activities, and overall health care relevance.

OBJECTIVE:Radiologic technologists may repeat images within a radiographic examination because of perceived suboptimal image quality, excluding these original images from submission to a PACS. This study assesses the appropriateness of technologists' decisions to repeat musculoskeletal and chest radiographs as well as the utility of repeat radiographs in addressing examinations' clinical indication. MATERIALS AND METHODS/METHODS:We included 95 musculoskeletal and 87 chest radiographic examinations in which the technologist repeated one or more images because of perceived image quality issues, rejecting original images from PACS submission. Rejected images were retrieved from the radiograph unit and uploaded for viewing on a dedicated server. Musculoskeletal and chest radiologists reviewed rejected and repeat images in their timed sequence, in addition to the studies' remaining images. Radiologists answered questions regarding the added value of repeat images. RESULTS:The reviewing radiologist agreed with the reason for rejection for 64.2% of musculoskeletal and 60.9% of chest radiographs. For 77.9% and 93.1% of rejected radiographs, the clinical inquiry could have been satisfied without repeating the image. For 75.8% and 64.4%, the repeated images showed improved image quality. Only 28.4% and 3.4% of repeated images were considered to provide additional information that was helpful in addressing the clinical question. CONCLUSION/CONCLUSIONS:Most repeated radiographs (chest more so than musculoskeletal radiographs) did not add significant clinical information or alter diagnosis, although they did increase radiation exposure. The decision to repeat images should be made after viewing the questionable image in context with all images in a study and might best be made by a radiologist rather than the performing technologist.

The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction.