Patient-centered radiology: a roadmap for outpatient imaging.

Creating a patient-centered experience is becoming increasingly important for radiology departments around the world. The goal of patient-centered radiology is to ensure that radiology services are sensitive to patients' needs and desires. This article provides a framework for addressing the patient's experience by dividing their imaging journey into three distinct time periods: pre-exam, day of exam, and post-exam. Each time period has aspects that can contribute to patient anxiety. Although there are components of the patient journey that are common in all regions of the world, there are also unique features that vary by location. This paper highlights innovative solutions from different parts of the world that have been introduced in each of these time periods to create a more patient-centered experience. CLINICAL RELEVANCE STATEMENT: Adopting innovative solutions that help patients understand their imaging journey and decrease their anxiety about undergoing an imaging examination are important steps in creating a patient centered imaging experience. KEY POINTS: • Patients often experience anxiety during their imaging journey and decreasing this anxiety is an important component of patient centered imaging. • The patient imaging journey can be divided into three distinct time periods: pre-exam, day of exam, and post-exam. • Although components of the imaging journey are common, there are local differences in different regions of the world that need to be considered when constructing a patient centered experience.

Designing Clinical MRI for Enhanced Workflow and Value.

MRI is an expensive and traditionally time-intensive modality in imaging. With the paradigm shift toward value-based healthcare, radiology departments must examine the entire MRI process cycle to identify opportunities to optimize efficiency and enhance value for patients. Digital tools such as "frictionless scheduling" prioritize patient preference and convenience, thereby delivering patient-centered care. Recent advances in conventional and deep learning-based accelerated image reconstruction methods have reduced image acquisition time to such a degree that so-called nongradient time now constitutes a major percentage of total room time. For this reason, architectural design strategies that reconfigure patient preparation processes and decrease the turnaround time between scans can substantially impact overall throughput while also improving patient comfort and privacy. Real-time informatics tools that provide an enterprise-wide overview of MRI workflow and Picture Archiving and Communication System (PACS)-integrated instant messaging can complement these efforts by offering transparent, situational data and facilitating communication between radiology team members. Finally, long-term investment in training, recruiting, and retaining a highly skilled technologist workforce is essential for building a pipeline and team of technologists committed to excellence. Here, we highlight various opportunities for optimizing MRI workflow and enhancing value by offering many of our own on-the-ground experiences and conclude by anticipating some of the future directions for process improvement and innovation in clinical MR imaging. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 1.

Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI.

BACKGROUND: Detection of rotator cuff tears, a common cause of shoulder disability, can be time-consuming and subject to reader variability. Deep learning (DL) has the potential to increase radiologist accuracy and consistency. PURPOSE: The aim of this study was to develop a prototype DL model for detection and classification of rotator cuff tears on shoulder magnetic resonance imaging into no tear, partial-thickness tear, or full-thickness tear. MATERIALS AND METHODS: This Health Insurance Portability and Accountability Act-compliant, institutional review board-approved study included a total of 11,925 noncontrast shoulder magnetic resonance imaging scans from 2 institutions, with 11,405 for development and 520 dedicated for final testing. A DL ensemble algorithm was developed that used 4 series as input from each examination: fluid-sensitive sequences in 3 planes and a sagittal oblique T1-weighted sequence. Radiology reports served as ground truth for training with categories of no tear, partial tear, or full-thickness tear. A multireader study was conducted for the test set ground truth, which was determined by the majority vote of 3 readers per case. The ensemble comprised 4 parallel 3D ResNet50 convolutional neural network architectures trained via transfer learning and then adapted to the targeted domain. The final tear-type prediction was determined as the class with the highest probability, after averaging the class probabilities of the 4 individual models. RESULTS: The AUC overall for supraspinatus, infraspinatus, and subscapularis tendon tears was 0.93, 0.89, and 0.90, respectively. The model performed best for full-thickness supraspinatus, infraspinatus, and subscapularis tears with AUCs of 0.98, 0.99, and 0.95, respectively. Multisequence input demonstrated higher AUCs than single-sequence input for infraspinatus and subscapularis tendon tears, whereas coronal oblique fluid-sensitive and multisequence input showed similar AUCs for supraspinatus tendon tears. Model accuracy for tear types and overall accuracy were similar to that of the clinical readers. CONCLUSIONS: Deep learning diagnosis of rotator cuff tears is feasible with excellent diagnostic performance, particularly for full-thickness tears, with model accuracy similar to subspecialty-trained musculoskeletal radiologists.

Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI.

Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep learning (DL) methods have provided accelerated high-quality image reconstructions from undersampled data, but it is unclear if DL image reconstruction can be reliably translated to everyday clinical practice. Purpose To determine the diagnostic equivalence of prospectively accelerated DL-reconstructed knee MRI compared with conventional accelerated MRI for evaluating internal derangement of the knee in a clinical setting. Materials and Methods A DL reconstruction model was trained with images from 298 clinical 3-T knee examinations. In a prospective analysis, patients clinically referred for knee MRI underwent a conventional accelerated knee MRI protocol at 3 T followed by an accelerated DL protocol between January 2020 and February 2021. The equivalence of the DL reconstruction of the images relative to the conventional images for the detection of an abnormality was assessed in terms of interchangeability. Each examination was reviewed by six musculoskeletal radiologists. Analyses pertaining to the detection of meniscal or ligament tears and bone marrow or cartilage abnormalities were based on four-point ordinal scores for the likelihood of an abnormality. Additionally, the protocols were compared with use of four-point ordinal scores for each aspect of image quality: overall image quality, presence of artifacts, sharpness, and signal-to-noise ratio. Results A total of 170 participants (mean age ± SD, 45 years ± 16; 76 men) were evaluated. The DL-reconstructed images were determined to be of diagnostic equivalence with the conventional images for detection of abnormalities. The overall image quality score, averaged over six readers, was significantly better (P < .001) for the DL than for the conventional images. Conclusion In a clinical setting, deep learning reconstruction enabled a nearly twofold reduction in scan time for a knee MRI and was diagnostically equivalent with the conventional protocol. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roemer in this issue.

AUR Radiology Resident Core Curriculum Lecture Series - A Model for Multi-Society Collaborative Virtual Education.

To fulfill ACGME requirements, radiology residency programs are required to provide an educational experience that includes a core didactic curriculum for each subspecialty. Although developing and delivering such a core curriculum may not present a problem for large academic programs, it can present a significant challenge for smaller programs with limited faculty in each subspecialty area. Success of the core curriculum lectures series developed for cardiothoracic radiology by the Society of Thoracic Radiology and for musculoskeletal radiology by the International Skeletal Society in collaboration with the Society for Skeletal Radiology prompted the idea of creating a comprehensive core curriculum lecture series encompassing all subspecialties. This paper aims to describe the multi-society collaborative effort entailed in building a curated, on line resident focused core curriculum lecture series detailing the barriers encountered, effects of the COVID-19 pandemic and impact of the finished project.

Work From Home in Academic Radiology Departments: Advantages, Disadvantages and Strategies for the Future.

To achieve necessary social distancing during the Covid-19 pandemic, working from home was introduced at most if not all academic radiology departments. Although initially thought to be a temporary adaptation, the popularity of working from home among faculty has made it likely that it will remain a component of radiology departments for the long term. This paper will review the potential advantages and disadvantages of working from home for an academic radiology department and suggest strategies to try to preserve the advantages and minimize the disadvantages.

Video Radiology Reports: A Valuable Tool to Improve Patient-Centered Radiology.

BACKGROUND. Improved communication between radiologists and patients is a key component of patient-centered radiology. OBJECTIVE. The purpose of this study was to create patient-centered video radiology reports using simple-to-understand language and annotated images and to assess the effect of these reports on patients' experience and understanding of their imaging results. METHODS. During a 4-month study period, faculty radiologists created video radiology reports using a tool integrated within the diagnostic viewer that allows both image and voice capture. To aid patients' understanding of cross-sectional images, cinematically rendered images were automatically created and made immediately available to radiologists at the workstation, allowing their incorporation into video radiology reports. Video radiology reports were made available to patients via the institutional health portal along with the written radiology report and the examination images. Patient views of the video report were recorded, and descriptive analyses were performed on radiologist and examination characteristics as well as patient demographics. A survey was sent to patients to obtain feedback on their experience. RESULTS. During the study period, 105 of 227 faculty radiologists created 3763 video radiology reports (mean number of reports per radiologist, 36 ± 27 [SD] reports). Mean time to create a video report was 238 ± 141 seconds. Patients viewed 864 unique video reports. The mean overall video radiology report experience rating based on 101 patient surveys was 4.7 of 5. The mean rating for how well the video report helped patients understand their findings was also 4.7 of 5. Of the patients who responded to the survey, 91% preferred having both written and video reports together over having written reports alone. CONCLUSION. Patient-centered video radiology reports are a useful tool to help improve patient understanding of imaging results. The mechanism of creating the video reports and delivering them to patients can be integrated into existing informatics infrastructure. CLINICAL IMPACT. Video radiology reports can play an important role in patient-centered radiology, increasing patient understanding of imaging results, and they may improve the visibility of radiologists to patients and highlight the radiologist's important role in patient care.

Artificial intelligence in musculoskeletal imaging: a perspective on value propositions, clinical use, and obstacles.

Artificial intelligence and deep learning (DL) offer musculoskeletal radiology exciting possibilities in multiple areas, including image reconstruction and transformation, tissue segmentation, workflow support, and disease detection. Novel DL-based image reconstruction algorithms correcting aliasing artifacts, signal loss, and noise amplification with previously unobtainable effectiveness are prime examples of how DL algorithms deliver promised value propositions in musculoskeletal radiology. The speed of DL-based tissue segmentation promises great efficiency gains that may permit the inclusion of tissue compositional-based information routinely into radiology reports. Similarly, DL algorithms give rise to a myriad of opportunities for workflow improvements, including intelligent and adaptive hanging protocols, speech recognition, report generation, scheduling, precertification, and billing. The value propositions of disease-detecting DL algorithms include reduced error rates and increased productivity. However, more studies using authentic clinical workflow settings are necessary to fully understand the value of DL algorithms for disease detection in clinical practice. Successful workflow integration and management of multiple algorithms are critical for translating the value propositions of DL algorithms into clinical practice but represent a major roadblock for which solutions are critically needed. While there is no consensus about the most sustainable business model, radiology departments will need to carefully weigh the benefits and disadvantages of each commercially available DL algorithm. Although more studies are needed to understand the value and impact of DL algorithms on clinical practice, DL technology will likely play an important role in the future of musculoskeletal imaging.

Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.

OBJECTIVE. Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive examination of the knee without compromising image quality or diagnostic accuracy. MATERIALS AND METHODS. A DL model for image reconstruction using a variational network was optimized. The model was trained using dedicated multisequence training, in which a single reconstruction model was trained with data from multiple sequences with different contrast and orientations. After training, data from 108 patients were retrospectively undersampled in a manner that would correspond with a net 3.49-fold acceleration of fully sampled data acquisition and a 1.88-fold acceleration compared with our standard twofold accelerated parallel acquisition. An interchangeability study was performed, in which the ability of six readers to detect internal derangement of the knee was compared for clinical and DL-accelerated images. RESULTS. We found a high degree of interchangeability between standard and DL-accelerated images. In particular, results showed that interchanging the sequences would produce discordant clinical opinions no more than 4% of the time for any feature evaluated. Moreover, the accelerated sequence was judged by all six readers to have better quality than the clinical sequence. CONCLUSION. An optimized DL model allowed acceleration of knee images that performed interchangeably with standard images for detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images.

National Trends in Oncologic Diagnostic Imaging.

OBJECTIVE: To characterize national trends in oncologic imaging (OI) utilization. METHODS: This retrospective cross-sectional study used 2004 and 2016 CMS 5% Carrier Claims Research Identifiable Files. Radiologist-performed, primary noninvasive diagnostic imaging examinations were identified from billed Current Procedural Terminology codes; CT, MRI, and PET/CT examinations were categorized as "advanced" imaging. OI examinations were identified from imaging claims' primary International Classification of Diseases-9 and International Classification of Diseases-10 codes. Imaging services were stratified by academic practice status and place of service. State-level correlations of oncologic advanced imaging utilization (examinations per 1,000 beneficiaries) with cancer prevalence and radiologist supply were assessed by Spearman correlation coefficient. RESULTS: The national Medicare sample included 5,051,095 diagnostic imaging examinations (1,220,224 of them advanced) in 2004 and 5,023,115 diagnostic imaging examinations (1,504,608 of them advanced) in 2016. In 2004 and 2016, OI represented 4.3% and 3.9%, respectively, of all imaging versus 10.8% and 9.5%, respectively, of advanced imaging. The percentage of advanced OI done in academic practices rose from 18.8% in 2004 to 34.1% in 2016, leaving 65.9% outside academia. In 2016, 58.0% of advanced OI was performed in the hospital outpatient setting and 23.9% in the physician office setting. In 2016, state-level oncologic advanced imaging utilization correlated with state-level radiologist supply (r = +0.489, P < .001) but not with state-level cancer prevalence (r = -0.139, P = .329). DISCUSSION: OI usage varied between practice settings. Although the percentage of advanced OI done in academic settings nearly doubled from 2004 to 2016, the majority remained in nonacademic practices. State-level oncologic advanced imaging utilization correlated with radiologist supply but not cancer prevalence.

Early-Stage Radiology Volume Effects and Considerations with the Coronavirus Disease 2019 (COVID-19) Pandemic: Adaptations, Risks, and Lessons Learned.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic resulted in significant loss of radiologic volume as a result of shelter-at-home mandates and delay of non-time-sensitive imaging studies to preserve capacity for the pandemic. We analyze the volume-related impact of the COVID-19 pandemic on six academic medical systems (AMSs), three in high COVID-19 surge (high-surge) and three in low COVID-19 surge (low-surge) regions, and a large national private practice coalition. We sought to assess adaptations, risks of actions, and lessons learned. METHODS: Percent change of 2020 volume per week was compared with the corresponding 2019 volume calculated for each of the 14 imaging modalities and overall total, outpatient, emergency, and inpatient studies in high-surge AMSs and low-surge AMSs and the practice coalition. RESULTS: Steep examination volume drops occurred during week 11, with slow recovery starting week 17. The lowest total AMS volume drop was 40% compared with the same period the previous year, and the largest was 70%. The greatest decreases were seen with screening mammography and dual-energy x-ray absorptiometry scans, and the smallest decreases were seen with PET/CT, x-ray, and interventional radiology. Inpatient volume was least impacted compared with outpatient or emergency imaging. CONCLUSION: Large percentage drops in volume were seen from weeks 11 through 17, were seen with screening studies, and were larger for the high-surge AMSs than for the low-surge AMSs. The lowest drops in volume were seen with modalities in which delays in imaging had greater perceived adverse consequences.

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge.

PURPOSE: To advance research in the field of machine learning for MR image reconstruction with an open challenge. METHODS: We provided participants with a dataset of raw k-space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi-coil and single-coil data. We performed a two-stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019. RESULTS: We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches. CONCLUSIONS: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.

Preserving Radiology Resident Education During the COVID-19 Pandemic: The Simulated Daily Readout.

RATIONALE AND OBJECTIVES: The educational value of the daily resident readout, a vital component of resident training, has been markedly diminished due to a significant decrease in imaging volume and case mix diversity. The goal of this study was to create a "simulated" daily readout (SDR) to restore the educational value of the daily readout. MATERIALS AND METHODS: To create the SDR the following tasks were performed; selection of cases for a daily worklist for each resident rotation, comprising a combination of normal and abnormal cases; determination of the correct number of cases and the appropriate mix of imaging modalities for each worklist; development of an "educational" environment consisting of separate "instances" of both our Picture Archive Communication System and reporting systems; and the anonymization of all of the cases on the worklists. Surveys of both residents and faculty involved in the SDR were performed to assess its effectiveness. RESULTS: Thirty-two residents participated in the SDR. The daily worklists for the first 20 days of the SDR included 3682 cases. An average of 480 cases per day was dictated by the residents. Surveys of the residents and the faculty involved in the SDR demonstrated that both agreed that the SDR effectively mimics a resident's daily work on rotations and preserves resident education during the Coronavirus Disease 2019 crisis. CONCLUSION: The development of the SDR provided an effective method of preserving the educational value of the daily readout experience of radiology residents, despite severe decreases in imaging exam volume and case mix diversity during the Coronavirus Disease 2019 pandemic.

ACR Statement on Safe Resumption of Routine Radiology Care During the Coronavirus Disease 2019 (COVID-19) Pandemic.

The ACR recognizes that radiology practices are grappling with when and how to safely resume routine radiology care during the coronavirus disease 2019 (COVID-19) pandemic. Although it is unclear how long the pandemic will last, it may persist for many months. Throughout this time, it will be important to perform safe, comprehensive, and effective care for patients with and patients without COVID-19, recognizing that asymptomatic transmission is common with this disease. Local idiosyncrasies prevent a single prescriptive strategy. However, general considerations can be applied to most practice environments. A comprehensive strategy will include consideration of local COVID-19 statistics; availability of personal protective equipment; local, state, and federal government mandates; institutional regulatory guidance; local safety measures; health care worker availability; patient and health care worker risk factors; factors specific to the indication(s) for radiology care; and examination or procedure acuity. An accurate risk-benefit analysis of postponing versus performing a given routine radiology examination or procedure often is not possible because of many unknown and complex factors. However, this is the overriding principle: If the risk of illness or death to a health care worker or patient from health care-acquired COVID-19 is greater than the risk of illness or death from delaying radiology care, the care should be delayed; however, if the opposite is true, the radiology care should proceed in a timely fashion.

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.

Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles for the clearance of AI algorithms, and the development of AI educational resources for both practicing radiologists and radiology trainees. This paper details these issues and presents possible solutions based on discussions held at the 2019 meeting of the International Society for Strategic Studies in Radiology. KEY POINTS: • Radiologists should be aware of the different types of bias commonly encountered in AI studies, and understand their possible effects. • Methods for effective data sharing to train, validate, and test AI algorithms need to be developed. • It is essential for all radiologists to gain an understanding of the basic principles, potentials, and limits of AI.

fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.

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.

Perceptions of Radiologists and Emergency Medicine Providers Regarding the Quality, Value, and Challenges of Outside Image Sharing in the Emergency Department Setting.

OBJECTIVE. The purpose of this study is to assess the perceptions of radiologists and emergency medicine (EM) providers regarding the quality, value, and challenges associated with using outside imaging (i.e., images obtained at facilities other than their own institution). MATERIALS AND METHODS. We surveyed radiologists and EM providers at a large academic medical center regarding their perceptions of the availability and utility of outside imaging. RESULTS. Thirty-four of 101 radiologists (33.6%) and 38 of 197 EM providers (19.3%) responded. A total of 32.4% of radiologists and 55.3% of EM providers had confidence in the quality of images from outside community facilities; 20.6% and 44.7%, respectively, had confidence in the interpretations of radiologists from these outside facilities. Only 23.5% of radiologists and 5.3% of EM physicians were confident in their ability to efficiently access reports (for outside images, 47.1% and 5.3%). Very few radiologists and EM providers had accessed imaging reports from outside facilities through an available stand-alone portal. A total of 40.6% of radiologists thought that outside reports always or frequently reduced additional imaging recommendations (62.5% for outside images); 15.6% thought that reports changed interpretations of new examinations (37.5% for outside images); and 43.8% thought that reports increased confidence in interpretations of new examinations (75.0% for outside images). A total of 29.4% of EM providers thought that access to reports from outside facilities reduced repeat imaging (64.7% for outside images), 41.2% thought that they changed diagnostic or management plans (50.0% for outside images), and 50.0% thought they increased clinical confidence (67.6% for outside images). CONCLUSION. Radiologists and EM providers perceive high value in sharing images from outside facilities, despite quality concerns. Substantial challenges exist in accessing these images and reports from outside facilities, and providers are unlikely to do so using separate systems. However, even if information technology solutions for seamless image integration are adopted, providers' lack of confidence in outside studies may remain an important barrier.

Improving the Speed of MRI with Artificial Intelligence.

Magnetic resonance imaging (MRI) is a leading image modality for the assessment of musculoskeletal (MSK) injuries and disorders. A significant drawback, however, is the lengthy data acquisition. This issue has motivated the development of methods to improve the speed of MRI. The field of artificial intelligence (AI) for accelerated MRI, although in its infancy, has seen tremendous progress over the past 3 years. Promising approaches include deep learning methods for reconstructing undersampled MRI data and generating high-resolution from low-resolution data. Preliminary studies show the promise of the variational network, a state-of-the-art technique, to generalize to many different anatomical regions and achieve comparable diagnostic accuracy as conventional methods. This article discusses the state-of-the-art methods, considerations for clinical applicability, followed by future perspectives for the field.