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.
Purpose
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
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.
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.
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.
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.
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.
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.
Abstract Burnout is a concern for radiologists. The burnout rate is greater among diagnostic radiologists than the mean for all physicians, while radiation oncologists have a slightly lower burnout ...rate. Burnout can result in unprofessional behavior, thoughts of suicide, premature retirement, and errors in patient care. Strategies to reduce burnout include addressing the sources of job dissatisfaction, instilling lifestyle balance, finding reasons to work other than money, improving money management, developing a support group, and seeking help when needed.
The purpose of this article is to review current and emerging techniques and strategies that can be used to accelerate acquisition times in routine knee MRI.
Specific techniques reviewed include 3D ...fast spin-echo imaging as well as new approaches to rapid image acquisition techniques (parallel imaging, compressed sensing, simultaneous multislice, and neural network reconstruction techniques) and their potential application to knee MRI.
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
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