Azadeh Tabari

Background: Motion artifacts can degrade prostate MRI quality. PROPELLER read-out (PR), a radial acquisition technique, is an established approach that can reduce motion artifacts but requires longer acquisition times. We explored whether combining PR with accelerated acquisition and reconstruction methods yields image quality gains while reducing scan times. Purpose: To compare the image quality of standard Cartesian (CR) and PROPELLER read-outs (PR) using an accelerated fast spin echo-based technique (VORTEX), combined with deep learning reconstruction (DLR). Materials and Methods: In 80 consecutive patients, VORTEX with CR was performed for the axial, sagittal, and coronal planes with scan time of 84, 50, and 54 s, respectively, while VORTEX with PR was performed in the coronal (71 s) and sagittal planes (92 s), for a total of five imaging datasets. All datasets were reconstructed using a commercially available DLR (AIR ^TM Recon DL). Five radiologists performed image quality assessments. Inter-reader agreement was estimated using Gwet’s AC2 statistics. Results: All VORTEX derived images were assigned a T ₂ w image PI-QUAL score of ≥3, and all radiologists perceived fewer artifacts in the sagittal and coronal PROPELLER images ( p < .001). Four out of five radiologists also observed both superior overall image quality and reduced noise for all PROPELLER derived images ( p < .001). Inter-rater agreement was good to very good for all assessments (AC2 0.77–0.88). Conclusion: VORTEX with PR and DLR can achieve fast high-quality T ₂ w sagittal and coronal imaging of prostate, with all acquisitions completed within 95 s per imaging plane and a total triplanar acquisition time of 4 min 7 s.

Deep learning (DL) reconstruction methods have shown promise in accelerating 2D MRI sequences but have yet to be extensively validated for routine 3D volumetric MRI applications. Our purpose was to assess the diagnostic quality of a novel DL-accelerated 3D T1-MPRAGE compared with a state-of-the-art wave-controlled aliasing in parallel imaging (Wave-CAIPI) accelerated 3D T1-MPRAGE for evaluating intracranial enhancing lesions. This prospective study was approved by the Institutional Review Board. Patients undergoing contrast-enhanced brain MRI in an outpatient setting were scanned on 3T MRI systems. The imaging protocol included a state-of-the-art Wave-CAIPI postcontrast T1-MPRAGE (acceleration factor [R] = 2 × 2, acquisition time [TA] = 2:11 minutes) and a research-based postcontrast DL-T1-MPRAGE (R = 2 × 2, TA = 2:11 minutes). The DL-based reconstruction process involved 2 steps. The first step, inspired by variational networks, involved 6 iterations alternating between data consistency updates and neural network evaluation. The second step applied a super-resolution algorithm for further image enhancement. Two independent neuroradiologists conducted a blinded, randomized head-to-head comparison of the 2 sequences by using a previously published scale across the following criteria: visualization of dural, parenchymal, leptomeningeal, and ependymal enhancement; sharpness; noise; artifacts; and overall diagnostic quality. A third board-certified neuroradiologist adjudicated cases with discrepant ratings. Noninferiority of DL-T1-MPRAGE was tested by using a 15% margin. A total of 115 patients (68 women/47 men, mean age = 54 ± 10 years) were included. The top 3 clinical indications were: neoplasm (52%), vascular lesions (24%), and headache (8%). DL-T1-MPRAGE was noninferior to Wave-CAIPI T1-MPRAGE for delineating enhancing lesions with unanimous agreement in all cases with enhancing pathology. It was also noninferior in terms of noise perception (P < .0001), artifact (P < .0001), sharpness (P = .001), and overall diagnostic quality (P < .0001). DL-T1-MPRAGE provided equivalent visualization of small, subtle enhancing parenchymal, dural, and leptomeningeal lesions. The highly accelerated postcontrast DL-T1-MPRAGE demonstrated noninferior image quality compared with the clinically validated Wave-CAIPI T1-MPRAGE accelerated sequence while offering enhanced visualization of subtle enhancing lesions.

Purpose: To evaluate a physics-based deep-learning-accelerated super-resolution T1-weighted MPRAGE sequence (DL-MPRAGE) against standard 3-dimensional T1-weighted MPRAGE (STD-MPRAGE) for quantitative and qualitative regional cortical volume assessment. Materials and Methods: This prospective single-center study included patients undergoing evaluation for memory loss on 3T MRI scanners (MAGNETOM Vida, Siemens Healthineers, Forchheim, Germany) from October 2023 to January 2024. The absolute symmetrized percent change in cortical volume and thickness was assessed on DL- and STD-MPRAGE images using the FreeSurfer brain segmentation algorithm. Bland-Altman analysis evaluated the agreement in volumetrics for each anatomical region. Additionally, 2 blinded radiologists independently qualitatively rated image quality metrics and cortical volume loss for anatomical regions based on standardized scales. Results: A total of 64 participants (29 women [45%], mean age 62 years ±16 [SD]) were evaluated. DL-MPRAGE increased spatial resolution from 1 mm to 0.5 mm while reducing scan time by more than half (2:11 vs. 5:21). Mean regional volumes for DL-MPRAGE were systematically lower than for STD-MPRAGE (eg, 17 226 ± 2011 vs. 17 923 ± 2185 mm3, corresponding to an absolute difference between the means of 697 mm3, for the cingulate gyrus, P < .004). Corresponding absolute symmetrized percent change values averaged 2.8% across brain regions, with the largest mean value being 5.08% for the cingulate gyrus. Bland-Altman analysis demonstrated high agreement in quantitative measurements for both volume and thickness. On reader assessment, DL-MPRAGE was noninferior to STD-MPRAGE across image quality metrics (P < .01) and equivalent in assessing volume loss. Conclusions: DL-MPRAGE offers quantitatively and qualitatively equivalent volumetric estimation compared to STD-MPRAGE while improving spatial resolution and acquisition speed for patients undergoing evaluation for memory loss.

Background The scout accelerated motion estimation and reduction (SAMER) method has been developed to achieve rapid retrospective motion correction and may be advantageous in pediatric imaging to mitigate motion artifacts. Objective To evaluate the efficacy of SAMER for motion correction of brain imaging in awake pediatric patients. Methods In this institutional review board (IRB) approved and Health Insurance Portability and Accountability Act (HIPAA) compliant prospective study, 33 pediatric patients were scanned on a 3-T MRI system in the outpatient setting. The imaging protocol included an accelerated 3D T1-weighted SAMER MPRAGE research sequence acquired in 2 min 37 s. Retrospective reconstructions of the T1-weighted MPRAGE data were performed with and without SAMER motion correction. Reconstructed image pairs were reviewed side-by-side by two board-certified neuroradiologists in a blinded manner and graded for motion artifacts using a 5-point scale. The improvement in motion score was calculated by subtracting the post-correction score from the pre-correction score. Results Without SAMER motion correction, 24 out of 33 cases (73%) showed motion artifact (21% (7/33) minimal, 27%(9/33) mild, 9% (3/33) moderate, and 15% (5/33) severe). SAMER motion correction improved motion artifacts in 79% (19/24) of cases, improving the motion score by median of 3 vs. 2 for motion-corrected examinations using SAMER. After applying SAMER motion correction, 50% (4/8) of cases considered nondiagnostic due to motion were reclassified as diagnostic. Conclusion SAMER demonstrates potential for reducing motion artifacts in 3D T1-weighted MPRAGE brain MR images, with the most notable improvements occurring in cases of moderate to severe motion.

This study shows that MRI acceleration techniques reduce energy use, carbon emissions, and costs, while increasing imaging capacity and practice revenue, offering a sustainable and efficient strategy for health care operations.

Background: The identification of the optimal management for blunt splenic trauma—angioembolization (AE), splenectomy, or observation—remains a challenge. This study applies Optimal Policy Trees (OPT), an artificial intelligence (AI) model, to prescribe appropriate management and improve in-hospital mortality. Methods: OPTs were trained on patients with blunt splenic injuries in the ACS-TQIP 2013–2019 to prescribe one of the three interventions: splenectomy, angioembolization (AE), or observation. Prescriptive trees were derived in two separate patient cohorts: those who presented with a systolic blood pressure (SBP) < 70 mmHg and those with an SBP ≥ 70 mmHg. Splenic injury severity was graded using the American Association of Surgical Trauma (AAST) grading scale. Counterfactual estimation was used to predict the effects of interventions on overall in-hospital mortality. Results: Among 54,345 patients, 3.1% underwent splenic AE, 13.1% splenectomy, and 83.8% were managed with observation. In patients with SBP < 70 mmHg, AE was recommended for shock index (SI) < 1.5 or without transfusion, while splenectomy was indicated for SI ≥ 1.5 with transfusion. For patients with SBP ≥ 70 mmHg, AE was recommended for AAST grades 4–5, or grades 1–3 with SI ≥ 1.2; observation was recommended for grades 1–3 with SI < 1.2. Predicted mortality using OPT-prescribed treatments was 18.4% for SBP < 70 mmHg and 4.97% for SBP ≥ 70 mmHg, compared to observed rates of 36.46% and 7.60%, respectively. Conclusions: Interpretable AI models may serve as a decision aid to improve mortality in patients presenting with a blunt splenic injury. Our data-driven prescriptive OPT models may aid in prescribing the appropriate management in this patient cohort based on their characteristics.

Aim: To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). Methods: From 2007–2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves. Results: Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5–103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88. Conclusions: MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM.