Automated Protocoling for MRI Exams-Challenges and Solutions

Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation's contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan's healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan's national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan's extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI-AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.

Current Advances in Viral Nanoparticles for Biomedicine

Viral nanoparticles (VNPs) have emerged as crucial tools in the field of biomedicine. Leveraging their biological and physicochemical properties, VNPs exhibit significant advantages in the prevention, diagnosis, and treatment of human diseases. Through techniques such as chemical bioconjugation, infusion, genetic engineering, and encapsulation, these VNPs have been endowed with multifunctional capabilities, including the display of functional peptides or proteins, encapsulation of therapeutic drugs or inorganic particles, integration with imaging agents, and conjugation with bioactive molecules. This review provides an in-depth analysis of VNPs in biomedicine, elucidating their diverse types, distinctive features, production methods, and complex design principles behind multifunctional VNPs. It highlights recent innovative research and various applications, covering their roles in imaging, drug delivery, therapeutics, gene delivery, vaccines, immunotherapy, and tissue regeneration. Additionally, the review provides an assessment of their safety and biocompatibility and discusses challenges and future opportunities in the field, underscoring the vast potential and evolving nature of VNP research.

The role of artificial intelligence in cardiovascular magnetic resonance imaging

Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.

Automatic Protocolling of Non-contrast Musculoskeletal MRIs Does Not Result in Increase in Patient Recall Rates for Contrast-Enhanced Studies

RATIONALE AND OBJECTIVES

Physicians spend large amounts of time on protocolling imaging studies, limiting their time spent on other essential clinical tasks. Most musculoskeletal (MSK) MRI studies are performed for the evaluation of joint pain and internal derangements and usually require no intravenous contrast. Contrast-enhanced MRI studies are performed for the evaluation of infection, suspected or established tumor, and rheumatological conditions. Protocolling all MSK MRI studies takes time away from other important tasks during the workday. Routine joint MRI scans have established set of sequences, and thus, could be scheduled and performed without special protocols by the radiologists. In a large tertiary care center like ours with multiple MRI magnets, we set up a process of automated protocoling and scheduling of non-contrast joint MRI scans ordered by referring doctors. This project's purpose was to assess the effect of this newly established process of 'automatic protocoling and scheduling' of MSK MRI scans on the rate of overlooked MRI exams that may have required contrast examinations, and on the patient recall-rate to obtain follow-up post-contrast sequences for further diagnostic characterization.

METHODS

All MSK reports of MRIs during the last two months of the years before and after the implementation of automatic protocolling (intervention) were searched for the presence of indications related to neoplasms, infections, and rheumatological conditions. For each of the three disease categories, we determined the number of MRIs obtained with and without contrast before and after the intervention. For each matching study obtained without contrast, the patient chart was reviewed for contraindications to contrast, positive final diagnosis, whether interpreting radiologist mentioned the exam being limited by lack of contrast, and recommendations for a follow-up contrast enhanced study.

RESULTS

A total of 846 MSK MRIs were performed prior to intervention and 822 MRIs were performed afterwards. Overall, 25% of the studies were performed without contrast prior to the intervention, and 31% of studies were performed without contrast afterwards (Chi square 0.07, p-value 0.79). No report contained a recommendation for contrast enhanced follow-up study before or after the intervention.

CONCLUSION

Automatic protocolling of routine MSK non-contrast MRI studies resulted in statistically insignificant, minimal increase in the overall number of non-contrast enhanced studies obtained for work up of neoplasms, infections, and rheumatological conditions. There was no increase in patient recall rate for additional post contrast sequences and the new process resulted in time savings to fellows and other physicians, being not distracted from other important tasks.

Implementation of an Institution-Wide Rules-Based Automated CT Protocoling System

BACKGROUND. Examination protocoling is a noninterpretive task that increases radiologists' workload and can cause workflow inefficiencies. OBJECTIVE. The purpose of this study was to evaluate effects of an automated CT protocoling system on examination process times and protocol error rates. METHODS. This retrospective study included 317,597 CT examinations (mean age, 61.8 ± 18.1 [SD] years; male, 161,125; female, 156,447; unspecified sex, 25) from July 2020 to June 2022. A rules-based automated protocoling system was implemented institution-wide; the system evaluated all CT orders in the EHR and assigned a protocol or directed the order for manual radiologist protocoling. The study period comprised pilot (July 2020 to December 2020), implementation (January 2021 to December 2021), and postimplementation (January 2022 to June 2022) phases. Proportions of automatically protocoled examinations were summarized. Process times were recorded. Protocol error rates were assessed by counts of quality improvement (QI) reports and examination recalls and comparison with retrospectively assigned protocols in 450 randomly selected examinations. RESULTS. Frequency of automatic protocoling was 19,366/70,780 (27.4%), 68,875/163,068 (42.2%), and 54,045/83,749 (64.5%) in pilot, implementation, and postimplementation phases, respectively (p < .001). Mean (± SD) times from order entry to protocol assignment for automatically and manually protocoled examinations for emergency department examinations were 0.2 ± 18.2 and 2.1 ± 69.7 hours, respectively; mean inpatient examination times were 0.5 ± 50.0 and 3.5 ± 105.5 hours; and mean outpatient examination times were 361.7 ± 1165.5 and 1289.9 ± 2050.9 hours (all p < .001). Mean (± SD) times from order entry to examination completion for automatically and manually protocoled examinations for emergency department examinations were 2.6 ± 38.6 and 4.2 ± 73.0 hours, respectively (p < .001); for inpatient examinations were 6.3 ± 74.6 and 8.7 ± 109.3 hours (p = .001); and for outpatient examinations were 1367.2 ± 1795.8 and 1471.8 ± 2118.3 hours (p < .001). In the three phases, there were three, 19, and 25 QI reports and zero, one, and three recalls, respectively, for automatically protocoled examinations, versus nine, 19, and five QI reports and one, seven, and zero recalls for manually protocoled examinations. Retrospectively assigned protocols were concordant with 212/214 (99.1%) of automatically protocoled versus 233/236 (98.7%) of manually protocoled examinations. CONCLUSION. The automated protocoling system substantially reduced radiologists' protocoling workload and decreased times from order entry to protocol assignment and examination completion; protocol errors and recalls were infrequent. CLINICAL IMPACT. The system represents a solution for reducing radiologists' time spent performing noninterpretive tasks and improving care efficiency.

Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks

Purpose

To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols.

Approach

The approach simulates "acquisition shift derivatives" of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI).

Results

The differences between real and simulated images range up to 19% in gray and white matter for extreme parameter settings. For the segmentation networks under test, the F1 score dependency on TE and TI can be well described by quadratic model functions (R 2 > 0.9 ). The coefficients of the model functions indicate that changes of TE have more influence on the model performance than TI.

Conclusions

We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.