Magnetic Resonance Imaging (MRI) examinations are frequently associated with significant anxiety and phobias in patients, negatively impacting imaging quality and patient compliance. In this study, we explore the use of Virtual Reality Exposure Therapy (VRET) to reduce MRI-related anxiety by examining physiological and subjective responses across three virtual scenarios: a 2D video, a 360° video, and a fully immersive VR environment. The study aimed to determine how different levels of immersion and the order in which scenarios are experienced impact anxiety. Thirteen participants engaged in all three scenarios, with heart rate (HR), skin temperature (SKT), and electrodermal activity (EDA) monitored, and self-reported anxiety assessments collected before, during, and after the study. Results showed no significant differences in average or maximum heart rates between the three scenarios. However, the fully immersive VR environment generally elicited higher HR peaks, higher EDA, and lower SKT, suggesting stronger physiological responses in some participants. Self-reported anxiety decreased after the VR experience, particularly for participants with moderate to high anxiety levels prior to the sessions, independent of the scenario order. These findings suggest that individual responses to VRET vary, emphasizing the need for personalized approaches rather than a one-size-fits-all solution. While larger studies are necessary to validate these outcomes, the results suggest that incorporating real-time biofeedback monitoring in VRET could allow for dynamic adjustments to exposure levels based on participants’ physiological responses, creating a more adaptive and therapeutic environment.

OBJECTIVES: Breast diffusion-weighted imaging (DWI) has shown potential as a standalone imaging technique for certain indications, eg, supplemental screening of women with dense breasts. This study evaluates an artificial intelligence (AI)-powered computer-aided diagnosis (CAD) system for clinical interpretation and workload reduction in breast DWI. MATERIALS AND METHODS: This retrospective IRB-approved study included: n = 824 examinations for model development (2017-2020) and n = 235 for evaluation (01/2021-06/2021). Readings were performed by three readers using either the AI-CAD or manual readings. BI-RADS-like (Breast Imaging Reporting and Data System) classification was based on DWI. Histopathology served as ground truth. The model was nnDetection-based, trained using 5-fold cross-validation and ensembling. Statistical significance was determined using McNemar's test. Inter-rater agreement was calculated using Cohen's kappa. Model performance was calculated using the area under the receiver operating curve (AUC). RESULTS: The AI-augmented approach significantly reduced BI-RADS-like 3 calls in breast DWI by 29% (P =.019) and increased interrater agreement (0.57 ± 0.10 vs 0.49 ± 0.11), while preserving diagnostic accuracy. Two of the three readers detected more malignant lesions (63/69 vs 59/69 and 64/69 vs 62/69) with the AI-CAD. The AI model achieved an AUC of 0.78 (95% CI: [0.72, 0.85]; P <.001), which increased for women at screening age to 0.82 (95% CI: [0.73, 0.90]; P <.001), indicating a potential for workload reduction of 20.9% at 96% sensitivity. DISCUSSION AND CONCLUSION: Breast DWI might benefit from AI support. In our study, AI showed potential for reduction of BI-RADS-like 3 calls and increase of inter-rater agreement. However, given the limited study size, further research is needed.