Improved tissue sodium concentration quantification in breast cancer by reducing partial volume effects: a preliminary study

Introduction: In sodium (23Na) MRI, partial volume effects (PVE) are one of the most common causes of errors in the quantification of tissue sodium concentration (TSC) in vivo. Advanced image reconstruction algorithms, such as compressed sensing (CS), have been shown to potentially reduce PVE. Therefore, we investigated the feasibility of CS-based methods for image quality and TSC quantification accuracy improvement in patients with breast cancer (BC). Subjects and Methods: Three healthy participants and 12 female participants with BC were examined on a 7T MRI scanner in this study. We reconstructed 23Na-MRI images using the weighted total variation (wTV) and directional total variation (dTV), anatomically guided total variation (AG-TV), and adaptive combine (ADC) reconstruction and performed image quality assessment. We evaluated agreement in tumor volumes delineated on sodium data using the Dice score and performed TSC quantification for different image reconstruction approaches. Results: All methods provided sodium images of the breast with good quality. The mean Dice scores for wTV, dTV, and AG-TV were 65%, 72%, and 75%, respectively. In the breast tumors, average TSC values were 83.0, 72.0, 80.0, and 84.0 mmol/L, respectively. There was a significant difference between dTV and wTV (p<0.001), as well as between dTV and AG-TV (p<0.001) and dTV and ADC algorithm (p<0.001). Conclusion: The results of this study showed that there are differences in tumor appearance and TSC estimations that might be depending on the type of image reconstruction and parameters used, most likely due to differences in their robustness in reducing PVE.

Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths

Background: Multiparametric breast MRI data might improve tumor diagnostics, characterization, and treatment planning. Accurate alignment and delineation of images acquired at different field strengths such as 3T and 7T, remain challenging research tasks. Purpose: To address alignment challenges and enable consistent tumor segmentation across different MRI field strengths. Study type: Retrospective. Subjects: Nine female subjects with breast tumors were involved: six histologically proven invasive ductal carcinomas (IDC) and three fibroadenomas. Field strength/sequence: Imaging was performed at 3T and 7T scanners using post-contrast T1-weighted three-dimensional time-resolved angiography with stochastic trajectories (TWIST) sequence. Assessments: The method's performance for joint image registration and tumor segmentation was evaluated using several quantitative metrics, including signal-to-noise ratio (PSNR), structural similarity index (SSIM), normalized cross-correlation (NCC), Dice coefficient, F1 score, and relative sum of squared differences (rel SSD). Statistical tests: The Pearson correlation coefficient was used to test the relationship between the registration and segmentation metrics. Results: When calculated for each subject individually, the PSNR was in a range from 27.5 to 34.5 dB, and the SSIM was from 82.6 to 92.8%. The model achieved an NCC from 96.4 to 99.3% and a Dice coefficient of 62.9 to 95.3%. The F1 score was between 55.4 and 93.2% and the rel SSD was in the range of 2.0 and 7.5%. The segmentation metrics Dice and F1 Score are highly correlated (0.995), while a moderate correlation between NCC and SSIM (0.681) was found for registration. Data conclusion: Initial results demonstrate that the proposed method may be feasible in providing joint tumor segmentation and registration of MRI data acquired at different field strengths.