Faculty Bibliography

A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.

OBJECTIVE:To augment the educational resources available to training programs and trainees in musculoskeletal (MSK) radiology by creating a comprehensive series of Web-based open-access core curriculum lectures. MATERIALS AND METHODS/METHODS:Speakers with recognized content and lecturing expertise in MSK radiology were invited to create digitally recorded lecture presentations across a series of 42 core curriculum topics in MSK imaging. Resultant presentation recordings, organized under curriculum subject headings, were archived as open-access video file recordings for online viewing on a dedicated Web page ( http://radiologycorelectures.org/msk/ ). Information regarding the online core curriculum lecture series was distributed to members of the International Skeletal Society, Society of Skeletal Radiology, Society of Chairs of Academic Radiology Departments, and the Association of Program Directors in Radiology. Web page and online lecture utilization data were collected using Google Analytics (Alphabet, Mountain View, CA, USA). RESULTS:Forty-two lectures, by 38 speakers, were recorded, edited and hosted online. Lectures spanned ACGME curriculum categories of musculoskeletal trauma, arthritis, metabolic diseases, marrow, infection, tumors, imaging of internal derangement of joints, congenital disorders, and orthopedic imaging. Online access to the core curriculum lectures was opened on March 4, 2018. As of January 20, 2019, the core curriculum lectures have had 77,573 page views from 34,977 sessions. CONCLUSIONS:To date, the MSK core curriculum lecture series lectures have been widely accessed and viewed. It is envisioned that the initial success of the project will serve to promote ongoing content renewal and expansion to the lecture materials over time.

INTRODUCTION/BACKGROUND:Communication and physician burn out are major issues within Radiology. This study is designed to determine the utilization and cost benefit of a hybrid computer/human communication tool to aid in relay of clinically important imaging findings. MATERIAL AND METHODS/METHODS:Analysis of the total number of tickets, (requests for assistance) placed, the type of ticket and the turn-around time was performed. Cost analysis of a hybrid computer/human communication tool over a one-year period was based on human costs as a multiple of the time to close the ticket. Additionally, we surveyed a cohort of radiologists to determine their use of and satisfaction with this system. RESULTS:14,911 tickets were placed in the 6-month period, of which 11,401 (76.4%) were requests to "Get the Referring clinician on the phone." The mean time to resolution (TTR) of these tickets was 35.3 (±17.4) minutes. Ninety percent (72/80) of radiologists reported being able to interpret a new imaging study instead of waiting to communicate results for the earlier study, compared to 50% previously. 87.5% of radiologists reported being able to read more cases after this system was introduced. The cost analysis showed a cost savings of up to $101.12 per ticket based on the length of time that the ticket took to close and the total number of placed tickets. CONCLUSIONS:A computer/human communication tool can be translated to significant time savings and potentially increasing productivity of radiologists. Additionally, the system may have a cost savings by freeing the radiologist from tracking down referring clinicians prior to communicating findings.

PURPOSE/OBJECTIVE:To determine the utility of an automated radiology-pathology feedback tool. METHODS:We previously developed a tool that automatically provides radiologists with pathology results related to imaging examinations they interpreted. The tool also allows radiologists to mark the results as concordant or discordant. Five abdominal radiologists prospectively scored their own discordant results related to their previously interpreted abdominal ultrasound, CT, and MR interpretations between August 2017 and June 2018. Radiologists recorded whether they would have followed up on the case if there was no automated alert, reason for the discordance, whether the result required further action, prompted imaging rereview, influenced future interpretations, enhanced teaching files, or inspired a research idea. RESULTS:There were 234 total discordances (range 30-66 per radiologist), and 70.5% (165 of 234) of discordances would not have been manually followed up in the absence of the automated tool. Reasons for discordances included missed findings (10.7%; 25 of 234), misinterpreted findings (29.1%; 68 of 234), possible biopsy sampling error (13.3%; 31 of 234), and limitations of imaging techniques (32.1%; 75/234). In addition, 4.7% (11 of 234) required further radiologist action, including report addenda or discussion with referrer or pathologist, and 93.2% (218 of 234) prompted radiologists to rereview the images. Radiologists reported that they learned from 88% (206 of 234) of discordances, 38.6% (90 of 233) of discordances probably or definitely influenced future interpretations, 55.6% (130 of 234) of discordances prompted the radiologist to add the case to his or her teaching files, and 13.7% (32 of 233) inspired a research idea. CONCLUSION/CONCLUSIONS:Automated pathology feedback provides a valuable opportunity for radiologists across experience levels to learn, increase their skill, and improve patient care.

OBJECTIVE. The objective of this article is to show how artificial intelligence (AI) has impacted different components of the imaging value chain thus far as well as to describe its potential future uses. CONCLUSION. The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.

OBJECTIVE:The purpose of this study is to increase the value of MRI by reengineering the MRI workflow at a new imaging center to shorten the interval (i.e., turnaround time) between each patient examination by at least 5 minutes. MATERIALS AND METHODS/METHODS:The elements of the MRI workflow that were optimized included the use of dockable tables, the location of patient preparation rooms, the number of doors per scanning room, and the storage location and duplication of coils. Turnaround times at the new center and at two existing centers were measured both for all patients and for situations when the next patient was ready to be brought into the scanner room after the previous patient's examination was completed. RESULTS:Workflow optimizations included the use of dockable tables, dedicated patient preparation rooms, two doors in each MRI room, positioning the scanner to provide the most direct path to the scanner, and coil storage in the preparation rooms, with duplication of the most frequently used coils. At the new imaging center, the median and mean (± SD) turnaround times for situations in which patients were ready for scanning were 115 seconds (95% CI, 113-117 seconds) and 132 ± 72 seconds (95% CI, 129-135 seconds), respectively, and the median and mean turnaround times for all situations were 141 seconds (95% CI, 137-146 seconds) and 272 ± 270 seconds (95% CI, 263-282 seconds), respectively. For existing imaging centers, the median and mean turnaround times for situations in which patients were ready for scanning were 430 seconds (95% CI, 424-434 seconds) and 460 ± 156 seconds (95% CI, 455-465 seconds), respectively, and the median and mean turnaround times for all situations were 481 seconds (95% CI, 474-486 seconds) and 537 ± 219 seconds (95% CI, 532-543 seconds), respectively. CONCLUSION/CONCLUSIONS:The optimized MRI workflow resulted in a mean time savings of 5 minutes 28 seconds per patient.

PURPOSE/OBJECTIVE:Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning. METHODS:Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning was evaluated by fine-tuning baseline trainings from synthetic data with a small subset of in vivo MR training data. RESULTS:Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non-MR image data showed residual aliasing artifacts, which could be removed by transfer learning-inspired fine-tuning. CONCLUSION/CONCLUSIONS:This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases.