Simulated and experimental phantom data for multi-center quality assurance of quantitative susceptibility maps at 3 T, 7 T and 9.4 T.

PURPOSE: To develop methods for quality assurance of quantitative susceptibility mapping (QSM) using MRI at different magnetic field strengths, and scanners, using different MR-sequence protocols, and post-processing pipelines. METHODS: We built a custom phantom based on iron in two forms: homogeneous susceptibility ('free iron') and with fine-scaled variations in susceptibility ('clustered iron') at different iron concentrations. The phantom was measured at 3.0 T (two scanners), 7.0 T and 9.4 T using multi-echo, gradient echo acquisition sequences. A digital phantom analogue to the iron-phantom, tailored to obtain similar results as in experimentation was developed, with similar geometry and susceptibility values. Morphology enabled dipole inversion was applied to the phase images to obtain QSM for experimental and simulated data using the MEDI + 0 approach for background regularization. RESULTS: Across all scanners, QSM-values showed a linear increase with iron concentrations. The QSM-relaxivity was 0.231 ± 0.047 ppm/mM for free and 0.054 ± 0.013 ppm/mM for clustered iron, with adjusted determination coefficients (DoC) ≥ 0.87. Similarly, the simulations yielded linear increases (DoC ≥ 0.99). In both the experimental and digital phantoms, the estimated molar susceptibility was lower with clustered iron, because clustering led to highly localized field effects. CONCLUSION: Our iron phantom can be used to evaluate the capability of QSM to detect local variations in susceptibility across different field strengths, when using different MR-sequence protocols. The devised simulation method captures the effect of iron clustering in QSM as seen experimentally and could be used in the future to optimize QSM processing pipelines and achieve higher accuracy for local field effects, as also seen in Alzheimer's beta-amyloid plaques.

Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.

OBJECTIVE: Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines. Therefore, the main objective of this work was to develop a robust and reproducible CNN-based automatic prostate multi-regional segmentation model using an intercontinental cohort of prostate MRI. METHODS: A heterogeneous database of 243 T2-weighted prostate studies from 7 countries and 10 machines of 3 different vendors, with the CZ-TZ, PZ, and SV regions manually delineated by two experienced radiologists (ground truth), was used to train (n = 123) and test (n = 120) a U-Net-based model with deep supervision using a cyclical learning rate. The performance of the model was evaluated by means of dice similarity coefficient (DSC), among others. Segmentation results with a DSC above 0.7 were considered accurate. RESULTS: The proposed method obtained a DSC of 0.88 ± 0.01, 0.85 ± 0.02, 0.72 ± 0.02, and 0.72 ± 0.02 for the prostate gland, CZ-TZ, PZ, and SV respectively in the 120 studies of the test set when comparing the predicted segmentations with the ground truth. No statistically significant differences were found in the results obtained between manufacturers or continents. CONCLUSION: Prostate multi-regional T2-weighted MR images automatic segmentation can be accurately achieved by U-Net like CNN, generalizable in a highly variable clinical environment with different equipment, acquisition configurations, and population. KEY POINTS: • Deep learning techniques allows the accurate segmentation of the prostate in three different regions on MR T2w images. • Multi-centric database proved the generalization of the CNN model on different institutions across different continents. • CNN models can be used to aid on the diagnosis and follow-up of patients with prostate cancer.

68Ga-PSMA and 11C-Choline comparison using a tri-modality PET/CT-MRI (3.0 T) system with a dedicated shuttle.

BACKGROUND: The aim of this study was to prospectively compare the detection rate of 68Ga-PSMA versus 11C-Choline in men with prostate cancer with biochemical recurrence and to demonstrate the added value of a tri-modality PET/CT-MRI system. METHODS: We analysed 36 patients who underwent both 11C-Choline PET/CT and 68Ga-PSMA PET/CT scanning within a time window of 1-2 weeks. Additionally, for the 68Ga-PSMA scan, we used a PET/CT-MRI (3.0 T) system with a dedicated shuttle, acquiring MRI images of the pelvis. RESULTS: Both scans were positive in 18 patients (50%) and negative in 8 patients (22%). Nine patients were positive with 68Ga-PSMA alone (25%) and one with 11C-Choline only (3%). The median detected lesion per patient was 2 for 68Ga-PSMA (range 0-93) and 1 for 11C-Choline (range 0-57). Tumour to background ratios in all concordant lesions (n = 96) were higher for 68Ga-PSMA than for 11C-Choline (110.3 ± 107.8 and 27.5 ± 17.1, mean ± S.D., for each tracer, respectively P = 0.0001). The number of detected lesions per patient was higher for 11C-Choline in those with PSA ≥ 3.3 ng/mL, while the number of detected lesions was independent of PSA levels for 68Ga-PSMA using the same PSA cut-off value. Metastatic pelvic lesions were found in 25 patients (69%) with 68Ga-PSMA PET/CT, in 18 (50%) with 11C-Choline PET/CT and in 21 (58%) with MRI (3.0 T). MRI was very useful in detecting recurrence in cases classified as indeterminate by means of PET/CT alone at prostate bed. CONCLUSIONS: In patients with prostate cancer with biochemical recurrence 68Ga-PSMA detected more lesions per patient than 11C-Choline, regardless of PSA levels. PET/CT-MRI (3.0 T) system is a feasible imaging modality that potentially adds useful relevant information with increased accuracy of diagnosis.