Optimal Sensor Selection for Motion-Corrected Supine Breast MRI with a Wearable Coil

Life Sciences Bioengineering Medical Imaging Medicine
supine breast MRI motion correction wearable coil respiratory belt accelerometers

Motion-Corrected Supine Breast MRI with a Wearable Coil

Study on Motion Correction for Supine Breast MRI Based on Wearable Coils

Academic Background

Magnetic Resonance Imaging (MRI) is a crucial tool in the diagnosis and monitoring of breast cancer. Currently, standard breast MRI is typically performed in the prone position, which helps to minimize motion artifacts caused by respiratory movement. However, the prone position is not always comfortable for patients and introduces discrepancies in breast shape and positioning compared to the supine position commonly used during surgery, ultrasonography, radiotherapy, and other clinical interventions. Therefore, developing supine breast MRI has significant clinical importance, although it remains sensitive to motion artifacts.

To reduce respiratory motion artifacts in supine breast MRI, researchers have proposed various strategies, including breath-hold techniques, respiratory gating or reordering, and retrospective non-rigid motion correction based on navigators or motion sensors. Among these, using motion sensors for correction is an effective method. However, the application of traditional respiratory belts in supine breast MRI has limitations, especially when using wearable coils (such as the “Bracoil”), where the belt cannot be placed directly below the coil, leading to inaccurate signal acquisition. Therefore, researchers have explored the use of MRI-compatible accelerometers as an alternative solution to optimize motion correction.

Paper Origin

This paper was authored by Karyna Isaieva, Nicolas Weber, Lena Nohava, et al., from institutions such as Université de Lorraine (France) and the Medical University of Vienna (Austria). The research received support from the French National Institute of Health and Medical Research (INSERM) and the European Regional Development Fund. It was published in IEEE Transactions on Biomedical Engineering in 2025.

Research Workflow

Study Population and Data Acquisition

The study included 10 healthy female volunteers who underwent 17 supine breast MRI scans. Scans were performed using a Siemens Prisma 3T MRI device equipped with the wearable coil “Bracoil.” During scanning, seven MRI-compatible accelerometers (Marmot sensors) were attached to the Bracoil, while a respiratory belt recorded breathing signals. Additionally, low-resolution T1-weighted 3D sequences, high-resolution T2-weighted 2D sequences, and high-resolution T1-weighted 3D image data were acquired.

Physiological Data Processing

Respiratory belt signals were processed using low-pass filtering and quadratic drift correction, whereas accelerometer signals were processed using band-pass filtering (cutoff frequencies at 0.03 Hz and 0.3 Hz). Researchers also designed an algorithm to detect sensor displacement and excluded data from sensors showing significant displacement. To assess sensor independence, singular value decomposition (SVD) was performed on the sensor signals, and normalized root-mean-square error (NRMSE) was calculated.

Image Reconstruction

Image reconstruction utilized the GRICS (Generalized Reconstruction by Inversion of Coupled Systems) algorithm, which iteratively solves for motion-corrected images and motion model parameters. The study compared two motion correction strategies: one based on a high-resolution T2-weighted 2D sequence (2D motion model) and another based on a low-resolution T1-weighted 3D sequence (3D motion model).

Quantitative and Qualitative Evaluation

Image quality was assessed using the Sharpness Index (SI), and Sharpness Enhancement (SE) was calculated to compare the effectiveness of different correction methods. Additionally, the accuracy of the motion model was evaluated by comparing predicted displacements with actual displacements observed in real-time MRI images, using Mean Sum of Distances (MSD) as the evaluation metric. Finally, two radiologists qualitatively scored the images to evaluate the image quality of different correction methods.

Research Results

Physiological Data Analysis

The study found that the placement of accelerometers significantly affected signal quality, with some positions more stable than others. Moreover, SVD analysis indicated that sensor signals exhibited high independence, with a single sensor explaining only 45%-70% of total motion data.

Quantitative Assessment

In T2-weighted 2D sequences, the correction method using all accelerometers significantly outperformed single sensors and the respiratory belt. In T1-weighted 3D sequences, the method using all accelerometers also performed best, though the difference was not statistically significant. Furthermore, correction based on the 2D motion model outperformed that of the 3D motion model.

Motion Model Evaluation

The 2D motion model performed better in predicting displacements, showing a significantly higher match with real-time images compared to the 3D motion model. MSD analysis showed that the average error of the 2D motion model ranged between 30%-60%, while the 3D motion model had larger errors.

Qualitative Assessment

Radiologist scoring revealed that the correction method using all accelerometers significantly outperformed the respiratory belt and ranked higher in image quality. Typical images showed that using all accelerometers effectively reduced artifacts, particularly around the breast edges.

Conclusion and Significance

The study demonstrated that the motion model based on high-resolution T2-weighted 2D sequences provided higher accuracy in supine breast MRI. Additionally, the correction method using multiple MRI-compatible accelerometers significantly outperformed traditional respiratory belts, especially when using wearable coils. This finding offers important technical references for future clinical applications of supine breast MRI.

Research Highlights

  1. Innovative Correction Method: This study was the first to systematically evaluate the effects of using multiple accelerometers for motion correction in wearable coils, providing new solutions for supine breast MRI.
  2. Advantages of Multi-Sensor Use: The study found that using multiple accelerometers significantly improved the precision of motion correction, offering critical insights for future sensor optimization.
  3. Clinical Value: This research lays a technical foundation for supine MRI scans in breast cancer patients, potentially improving diagnostic accuracy and patient experience.

Future Prospects

Researchers plan to further optimize sensor setups to enhance motion signal quality and will apply this technology to breast cancer patients in future studies. This research establishes a solid foundation for the broader application of supine breast MRI.