arXiv subjects

Olgica Zaric

Publications and source records attributed to Olgica Zaric.

3 recordsLinked to original sources

arXiv2025

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

Introduction: In sodium (23Na) magnetic resonance imaging (MRI), partial volume effects (PVE) are one of the most common causes of errors in the in vivo quantification of tissue sodium concentration (TSC). Advanced image reconstruction algorithms, such as compressed sensing (CS), have the potential to reduce PVE. Therefore, we investigated the feasibility of using CS-based methods to improve image quality and TSC quantification accuracy in patients with breast cancer. Subjects and methods: In this study, three healthy participants and 12 female participants with breast cancer were examined on a 7T MRI scanner. 23Na-MRI images were reconstructed using weighted total variation (wTV), directional total variation (dTV), anatomically guided total variation (AG-TV) and adaptive combine (ADC) methods. The consistency of tumor volume delineations based on sodium data was assessed using the Dice score, and TSC quantification was performed for various image reconstruction methods. Pearsons correlation coefficients were calculated to assess the relationships between wTV, dTV, AG-TV, and ADC values. Results: All methods provided breast MRI images with well-preserved sodium signal and tissue structures. The mean Dice scores for wTV, dTV, and AG-TV were 65%, 72%, and 75%, respectively. Average TSC values in breast tumors were 61.0, 72.0, 73.0, and 88.0 mmol/L for wTV, dTV, AG-TV, and ADC, respectively. A strong negative correlation was observed between wTV and dTV (r = -0.78, 95% CI [-0.94, -0.31], p = 0.0076) and a strong positive correlation between dTV and AG-TV (r = 0.71, 95% CI [0.16, 0.92], p = 0.0207) was found. Conclusion: The results of this study showed that differences in tumor appearance and TSC estimations may depend on the type of image reconstruction and the parameters used. This is most likely due to differences in their ability to reduce PVE.

cs.CV↗

arXiv2025

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.

eess.IV↗

arXiv2024

Intensity Normalization Techniques and Their Effect on the Robustness and Predictive Power of Breast MRI Radiomics

Radiomics analysis has emerged as a promising approach for extracting quantitative features from medical images to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features can be highly dependent on the acquisition and pre-processing techniques used. In this study, we aim to investigate the effect of various intensity normalization techniques on the robustness of radiomics features extracted from MRI scans of breast cancer patients. The images used are from the publicly available I-SPY TRIAL dataset, which contains MRI scans of stage 2 or 3 breast cancer patients and from the Platinum and PARP inhibitor for Neoadjuvant treatment of Triple Negative and / or BRCA positive breast cancer (PARTNER) trial. We compared the effect of commonly used intensity normalization techniques on the robustness of radiomics features using Intraclass Correlation Coefficient (ICC) between multiple combinations of normalization approaches, identified categories that are robust and therefore could be compared between studies regardless of the pre-processing used. We were able to show that while systematic differences between MRI scanners can significantly affect many radiomics features, a combination of Bias Field correction with piecewise linear histogram normalization can mitigate some of the effects compared to other normalization methods investigated in this paper. We were able to demonstrate the importance of carefully selecting and standardizing normalization methods for accurate and reliable radiomics analysis in breast MRI scans.

physics.med-ph↗