Shih, Robert

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

PurposeWe investigate the importance of high gradient‐amplitude and high slew‐rate on oscillating gradient spin echo (OGSE) diffusion imaging for human brain imaging and evaluate human brain imaging with OGSE on the MAGNUS head‐gradient insert (200 mT/m amplitude and 500 T/m/s slew rate).MethodsSimulations with cosine‐modulated and trapezoidal‐cosine OGSE at various gradient amplitudes and slew rates were performed. Six healthy subjects were imaged with the MAGNUS gradient at 3T with OGSE at frequencies up to 100 Hz and b = 450 s/mm2. Comparisons were made against standard pulsed gradient spin echo (PGSE) diffusion in vivo and in an isotropic diffusion phantom.ResultsSimulations show that to achieve high frequency and b‐value simultaneously for OGSE, high gradient amplitude, high slew rates, and high peripheral nerve stimulation limits are required. A strong linear trend for increased diffusivity (mean: 8–19%, radial: 9–27%, parallel: 8–15%) was observed in normal white matter with OGSE (20 Hz to 100 Hz) as compared to PGSE. Linear fitting to frequency provided excellent correlation, and using a short‐range disorder model provided radial long‐term diffusivities of D∞,MD = 911 ± 72 µm2/s, D∞,PD = 1519 ± 164 µm2/s, and D∞,RD = 640 ± 111 µm2/s and correlation lengths of lc,MD = 0.802 ± 0.156 µm, lc,PD = 0.837 ± 0.172 µm, and lc,RD = 0.780 ± 0.174 µm. Diffusivity changes with OGSE frequency were negligible in the phantom, as expected.ConclusionThe high gradient amplitude, high slew rate, and high peripheral nerve stimulation thresholds of the MAGNUS head‐gradient enables OGSE acquisition for in vivo human brain imaging.

BackgroundCarotid plaque imaging with MRI is becoming more commonplace, but practical challenges exist in performing plaque imaging with surface coils.PurposeTo compare the diagnostic performance of a carotid plaque MRI protocol using a standard neurovascular coil (Neurovascular Coil Protocol) to a higher‐resolution carotid plaque MRI using carotid surface coils (Surface Coil Protocol) in characterizing carotid plaque.Study TypeProspective study comparing two MR techniques in plaque characterization.PopulationThirty‐eight consecutive carotid artery disease patients.Field Strength/SequencePatients underwent 3T MRI using 1) a Neurovascular Coil Protocol including the following sequences: 3D‐FSE T1 pre/postcontrast and precontrast 3D IR‐FSPGR, and 2) a Surface Coil Protocol using standard multicontrast MRI sequences.AssessmentPlaque characteristics analyzed by two independent neuroradiologists included intraplaque hemorrhage (IPH), lipid‐rich necrotic‐core (LRNC), and thin/ruptured fibrous cap (TRFC).Statistical TestsDiagnostic performance of the Neurovascular Coil Protocol was compared to the Surface Coil Protocol reference standard using receiver‐operating curves.ResultsFor IPH, sensitivity, specificity, and area under the curve (AUC) of the Neurovascular Coil Protocol were 91.1% (95% confidence interval [CI] = 78.8–97.5%), 87.0% (95% CI = 66.4–97.2%), and 0.92, respectively. For LRNC without IPH sensitivity, specificity, and AUC were 73.3% (95% CI = 44.9–92.2%), 85.7% (95% CI = 67.3–96.0%), and 0.84, respectively. For TRFC, sensitivity, specificity, and AUC were 35.3% (95% CI = 14.2–61.7%), 97.6% (95% CI = 87.4–99.9%), and 0.66 respectively. Interobserver agreement for IPH, LRNC, and TRFC using the Neurovascular Coil Protocol were k = 0.87 (95% CI = 0.75–0.99), k = 0.54 (95% CI = 0.29–0.80), and k = 0.41 (95% CI = 0.08–0.74), respectively.Data ConclusionOur Neurovascular Coil Protocol has high sensitivity, specificity, and accuracy in identifying IPH and LRNC but is limited in assessment of TRFC.Level of Evidence: 1Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2018;47:1264–1272.