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

Karyna Isaieva, Nicolas Weber, Lena Nohava, Barbara Fischer, Alexandre Megel, Philippe Henrot, Emilien Micard, Elmar Laistler, Jacques Felblinger, Freddy Odille

► To cite this version:

Karyna Isaieva, Nicolas Weber, Lena Nohava, Barbara Fischer, Alexandre Megel, et al.. Optimal Sensor Selection for Motion-Corrected Supine Breast MRI With a Wearable Coil. IEEE Transactions on Biomedical Engineering, 2025, 72 (7), pp.2261-2268. 10.1109/TBME.2025.3539386. hal-05129354

HAL Id: hal-05129354

https://hal.science/hal-05129354v1

Submitted on 25 Jun 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Optimal Sensor Selection for Motion-Corrected Supine Breast MRI with a Wearable Coil

Karyna Isaieva*, Nicolas Weber, Lena Nohava, Barbara Fischer, Alexandre Megel, Philippe Henrot, Emilien Micard, Elmar Laistler, Jacques Felblinger, and Freddy Odille

**Abstract** — **Objective:** Supine breast MRI offers several advantages over conventional prone MRI but requires correction of motion artifacts. This study aimed to identify the optimal combination of motion sensors for achieving the best motion correction quality using a wearable radio frequency coil ("BraCoil"). **Methods:** T2-weighted (T2w) 2D and T1-weighted (T1w) 3D sequences were acquired in 10 volunteers and 17 exams. Images were reconstructed using the GRICS motion correction algorithm, using data from a respiratory belt or MRI-compatible accelerometers. The motion models were evaluated by comparing the predicted motion to dynamic MRI. The resulting images were also assessed using both quantitative metrics and radiological scoring. Additionally, the accuracy of motion models calculated from high-resolution 2D T2w images was compared to those derived from low-resolution 3D T1w images. **Results:** Motion predicted from 2D T2w images matched the dynamic MRI significantly better than that predicted from low-resolution 3D T1w images. Radiological scoring showed that using all accelerometers significantly improved image quality compared to using the belt. A single accelerometer explained only 45-70% of the total sensor data, and combining all sensors led to superior correction overall. No optimal sensor position was identified. **Conclusion:** The motion model calculated from 2D T2w images, using the data from all accelerometers, provides the best motion correction quality for supine breast MRI using a wearable coil. **Significance:** Selecting the appropriate physiological sensors minimizes motion artifacts and can potentially enhance diagnostic accuracy.

This work was supported by the CIC-IT of Nancy. With financial support from ITMO Cancer of Aviesan within the framework of the 2021-2030 Cancer Control Strategy, on funds administered by Inserm. It was also co-funded by the French State-Region contract CPER 2015-2020 (IT2MP), by the European Union through the European Regional Development Fund "FEDER-FSE Lorraine et Massif des Vosges 2014-2020", and France Life Imaging network (grant ANR-11-INBS-0006). The sponsor of this study was the CHRU de Nancy (Department of Methodology, Promotion, Investigation—MPI).

Nicolas Weber and Barbara Fischer are with IADI, Université de Lorraine, U1254 INSERM, Nancy, F-54000, France.

Lena Nohava and Elmar Laistler are with High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.

Alexandre Megel and Philippe Henrot are with Institut de Cancérologie de Lorraine Alexis Vautrin, Vandoeuvre-les-Nancy, F-54500, France.

Emilien Micard is with CIC-IT 1433, INSERM, CHRU de Nancy, Nancy, F-54000, France.

Jacques Felblinger and Freddy Odille are with IADI, Université de Lorraine, U1254 INSERM, Nancy, F-54000, France and CIC-IT 1433, INSERM, CHRU de Nancy, Nancy, F-54000, France.

*Karyna Isaieva is with IADI, Université de Lorraine, U1254 INSERM, Nancy, F-54000, France (correspondence karyna.isaieva@univ-lorraine.fr)

**Index Terms**— Accelerometers, Magnetic resonance imaging, Motion artifacts

## I. INTRODUCTION

MRI is a cornerstone in the diagnosis and monitoring of breast cancer. Standard breast MRI protocols typically involve imaging the patient in the prone position, where the breasts are pendant through openings in the dedicated coil installed on the MRI table. This position helps to minimize the impact of respiratory motion artifacts. However, the prone position is not always comfortable for patients and introduces a discrepancy in breast shape and positioning compared to the supine position commonly used during surgery, ultrasonography, radiotherapy, and other clinical interventions. As a result, there is a strong rationale for developing supine MRI, though it remains sensitive to motion artifacts.

Various strategies have been developed to mitigate respiratory motion artifacts in chest MRI, with techniques falling into three main categories: breath-hold techniques [1], [2], respiratory gating or reordering [3], [4], and retrospective non-rigid motion correction using MRI navigators or motion sensors. The first two approaches have obvious constraints: the breast-hold images must be acquired within 15-20 seconds, which limits their spatial resolution, while gating significantly increases acquisition time.

Navigator-based motion correction is practical in terms of patient installation and has proven effective for rigid motion correction in head MRI [5], [6]. Respiratory motion correction using navigators has also been successfully applied to cardiac MRI [7]. However, the application of this approach to breast MRI is challenging due to the relatively small displacements of breast tissue, which may be difficult to detect with the low-resolution data provided by navigators.

Respiratory motion data used for non-rigid motion correction can also be acquired using various MRI-compatible sensors. A pneumatic belt is routinely used for respiratory gating in clinical cardiac and abdominal MRI [8], [9]. Alternative solutions include optical camera respiration tracking [10], [11], accelerometers [12], ultrasound sensors [13], radiofrequency (beat) pilot tone transmitters [14], [15], and various electromagnetic devices [9].

In our previous study, we demonstrated that online non-rigid motion correction using a respiratory belt significantly
improves the quality of free-breathing supine MRI [16]. However, that study used commercially available radio frequency coil which are suboptimal for supine breast MRI. Standard cardiac or torso coils used for breast imaging do not provide optimal fit to the breast shape or signal-to-noise ratio (SNR) performance. To address these limitations, a wearable supine breast coil, known as the "BraCoil," was recently proposed [17]. Despite this coil providing a notably better SNR and acceleration capabilities, its main drawback is that its close fit prevents the placement of the respiratory belt below the coil. A placement caudally to the coil potentially reduces the effectiveness of motion correction as in this case, the recorded pneumatic signals represent rather abdominal breathing than chest breathing patterns. Thus, an alternative on-coil sensor may be required.

MRI-compatible accelerometers present a cost-effective and compact solution. Prior research has shown that a single MRI-compatible accelerometer (the MARMOT sensor) placed on the superior surface a torso coil allows respiratory motion correction for supine breast MRI [18]. The BraCoil design accommodates the placement of up to seven MARMOT sensors. The aim of this study was to determine the optimal combination of available motion sensors to achieve the highest possible motion correction quality. Another contribution of this work is the comparison of two motion correction approaches: the previously proposed strategy from [16], and a new alternative strategy utilizing a low-resolution 3D calibration sequence.

## II. MATERIALS AND METHODS

### A. Population and data acquisition

10 healthy female volunteers underwent supine MRI on a Siemens Prisma 3 T using the BraCoil [17]. 7 were scanned a second time wearing a textile breast holder, resulting in 17 exams in total. The data was acquired under the approved ethical protocol "EDEN" (ClinicalTrials.gov Identifier: NCT05218460, approval: CPP SUD-EST IV, 26.07.21). This study was performed in line with the principles of the Declaration of Helsinki and written informed consent was obtained from all volunteers.

7 MARMOT sensors [12] were attached to the coil using double-sided tape. To prevent B1 field perturbation, the sensors

<figure><img src="image_2.png" /><figcaption>Fig. 1. The acquisition setup: BraCoil with the attached MARMOT sensors.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. Example of the processed respiratory motion data: normalized pneumatic respiratory belt indications (top row) and jointly normalized x-, y-, z-components of the MARMOT accelerometer (3 bottom rows).</figcaption></figure>

were secured with metallic tape for shielding (Faraday cage), validated by phantom tests. The setup is shown in Fig. 1. Additionally, a pneumatic respiratory belt (Maglife, Schiller Médical, Wissembourg, France) was installed on the volunteers' abdomen inferior to the coil. The physiological data was recorded using the Signal Analyser and Event Controller system [18].

The acquisition protocol included:

1. A low-resolution calibration T1-weighted (T1w) 3D gradient-echo sequence (TE/TR=1.47/4.14 ms, matrix size 166x245, spatial resolution 1.71x1.71x2 mm³, 2 averages);

2. A high-resolution T2-weighted (T2w) 2D TSE sequence (TE/TR=100/4880 ms, matrix size 288x504, in-plane resolution 0.8x0.8 mm², slice thickness 3 mm);

3. A high-resolution T1w 3D gradient-echo sequence (TE/TR=1.84/4.14 ms, matrix size 288x442, spatial resolution 0.88x0.88x1 mm³, acceleration factor R=2).

4. Four real-time FLASH sequences [19] (TE/TR=1.44/2.22 ms, matrix size 200 x 200, in-plane resolution 1.95x1.95 mm², slice thickness 6 mm, temporal resolution 33 ms). Two axial slices were positioned centrally within the breast, spaced apart, while two sagittal slices traversed the fibroglandular regions of the left and right breast.

### B. Physiological data processing

The respiratory belt signal was post-processed using low-pass filtering and quadratic drift correction [16]. The accelerometer signals of the MARMOT sensors were band-pass filtered with cutoff frequencies at 0.03 Hz and 0.3 Hz. An example of the post-processed respiratory belt and MARMOT signals is provided in Fig. 2. Only the highest-amplitude track of each accelerometer was selected (e.g., the y-track in Fig. 2).
Additionally, undesirable MARMOT sensor displacement, due to imperfect fastening, was detected by comparing the normalized difference between band-pass-filtered and only low-pass-filtered (<0.3 Hz) signals in the following way:

$$ \Delta = \frac{\|S_{HP} - S_{BP}\|}{\sigma_{BP}N}, $$

where $S_{HP}$ is the high-pass-filtered signal, $S_{BP}$ is the band-pass-filtered signal, $\sigma_{BP}$ is the band-pass-filtered signal's standard deviation, and $N$ is the number of samples in each signal. A threshold value of $\Delta_0 = 0.001$ was empirically selected, based on the visual evaluation of the quality of the images reconstructed using the retained signals. All sensors demonstrating a substantial displacement $\Delta > \Delta_0$ were excluded.

The following combinations of MARMOT sensors were analyzed: 1) all MARMOT sensors that met the condition $\Delta < \Delta_0$ (referred to as “All MARMOTs” throughout the text); 2) the MARMOT sensor with the highest standard deviation (referred to as “1 MARMOT”); and 3) the first principal component of all MARMOT sensors (referred to as “MARMOT PCA”). To assess the correlation between the signals from the MARMOT sensors, singular value decomposition (SVD) was performed on the matrix containing all sensor signals. The normalized root-mean-square error (NRMSE) between the original matrix and the matrix reconstructed using only the first singular value was chosen as the metric to quantify the level of independence between the sensors.

### C. Image reconstruction

The images were reconstructed using the GRICS algorithm [20]. The algorithm iteratively solves for a motion-corrected image $\rho$ and the parameters of a non-rigid motion model $\alpha$:

$$ \left\{ \begin{array}{l} \rho, \alpha = \underset{\rho, \alpha}{\operatorname{argmin}} \|E(u)\rho - m\|^2 + \lambda \|\rho/\rho_0\|^2 + \mu \|\nabla u\|^2, \quad (1a) \\ u(\vec{r}, t) = \sum_{i}^{N_s} \alpha_i(\vec{r}) s_i(t). \quad (1b) \end{array} \right. $$

Here $m$ is the acquired MRI k-space data, $E$ is an encoding operator that includes k-space sampling, Fourier transformation, coil sensitivity weightings, and spatial displacement by $u(\vec{r}, t)$. The displacements are supposed to be a linear combination of physiological signal $s_i$ with motion model components $\alpha_i$ as the coefficients. The coil sensitivity maps and the prior image $\rho_0$ were calculated from the central lines of k-space data with spline smoothing ($\lambda_{smooth} = 1000$).

As described in [16], the motion model derived from T2w 2D images (sequence 2) was applied to correct the motion in T1w 3D images (sequence 3). The reconstruction and regularization parameters were the same as in [16]. This technique is referred to as the “2D motion model” throughout the text. In this study, we also explored an alternative strategy (referred as to “3D motion model”): calculating the motion model from low-resolution 3D images (sequence 1) and applying it to high-resolution 3D images (sequence 3). Some reconstruction parameters were modified to increase the motion model quality while keeping the reconstruction time the same. The parameters for the calibration 3D sequence which differ from those for the T2w 2D sequence, were the following: 16 motion clusters

(instead of 12) and maximum 20 iterations (instead of 10) to solve the motion model.

The images were retrospectively reconstructed using the Siemens ICE simulator and the pipeline presented in [16], modified to enable the application of a 3D motion model. Each of the T1w and T2w sequences was reconstructed using belt correction and three combinations of MARMOT sensors: 1 MARMOT, All MARMOTs, and MARMOT PCA. Additionally, uncorrected images (GRICS with a trivial motion model: $\forall \vec{r}, \alpha_i(\vec{r}) = 0$) were also reconstructed.

### D. Quantitative image evaluation

The sharpness index (SI) [21] was selected as the quantitative metric. The images were manually segmented using the ITK Snap software [22] and multiplied by smoothed segmentation masks prior to the metrics calculation. This step allowed evaluation only of the region of interest sharpness. The sharpness index depends on numerous image characteristics (e.g. intensity distribution). Thus, similarly to [16], we introduce sharpness enhancement as a normalized metric indicating the enhancement compared to the non-corrected image:

$$ SE = \frac{SI_{GRICS} - SI_{noncorr.}}{0.5 \cdot (SI_{GRICS} + SI_{noncorr.})} \times 100\%. $$

For the 2D TSE T2w sequence, the images reconstructed with all sensor conditions were evaluated using this metric. For the 3D GRE T1w sequence, quality was assessed based on motion models calculated from the 2D TSE sequence and the low-resolution 3D GRE sequence. To optimize comparisons and avoid an excessive number of parameters, only the two most representative combinations – the respiratory belt and all MARMOT sensors – were retained for the sharpness enhancement evaluation of the 3D GRE T1w sequence.

The methods were compared using the paired Wilcoxon test ($p < 0.05$ considered significant).

### E. Motion model evaluation

The motion model was evaluated by comparing the predicted displacements, using equation (1b), with actual displacements obtained from the real-time sequence. The motion model $\alpha$ was calculated for each axial and sagittal slice acquired with the real-time sequence by interpolating either a stack of 2D motion maps calculated from the T2w 2D sequence, or 3D motion maps calculated from the T1w 3D sequence.

For each sequence and motion correction method, a reference image was generated as follows: first, the image corresponding to the minimum of the physiological signal was identified. Next, using the calculated motion model, this image was warped to the motion state corresponding to zero values of the physiological signal. The reference image was warped to match the motion states of the real-time images across the entire series.

Given that the real-time sequence was affected by intensity normalization changes due to heartbeats, a direct comparison between real-time images and those generated by the GRICS model was not feasible. Image registration-based metrics also failed to provide reliable results. Therefore, a metric based on the comparison of the outer edges of the breast was used. To extract these edges, an algorithm based on Canny edge
detection was applied. The Mean Sum of Distances (MSD) metric [23] was then calculated between the contours of the GRICS and real-time images as follows:

$$MSD(U,V) = \frac{1}{n_1 + n_2} \left( \sum_{i=1}^{n_1} \min_j \|u_i - v_j\| + \sum_{i=1}^{n_2} \min_j \|u_j - v_i\| \right),$$

where $u_i \in U$ and $v_i \in V$ represent the curves, with $n_1$ and $n_2$ being the numbers of points in the corresponding curves. To quantify the actual motion, MSD was also calculated between the contours of the real-time and reference images. Fig. 3 provides an illustration of the MSD metric calculation: one can imagine *MSD(GRICS,RT)*, characterizing the discrepancy between the actual and the predicted edges, as the average

<figure><img src="image_4.png" /><figcaption>Fig. 3. Zoomed view of spline-interpolated outer breast edge in the axial plane in the expiration phase. The curves denote the edges extracted from the corresponding images, and the lines depict the minimal distances between the real-time image edge to the reference edge (black lines) and to the GRICS edge (red lines). The MSD metric represents the average of these minimal distances: *MSD(Ref,RT)* for black lines and *MSD(GRICS,RT)* for red lines.</figcaption></figure>

length of all red lines, and *MSD(Ref,RT)*, representing the physiological displacement, as the average length of all black lines.

For further analysis, the quality of the motion model was evaluated as the ratio of *MSD(GRICS,RT)* to *MSD(Ref,RT)*. These values were calculated for an integer number of respiration cycles, from the first minimum and to the last minimum of the belt signal. The mean ratio was computed separately for the axial and sagittal slices by averaging the values from two slices per orientation, resulting in a single value for each orientation.

As for the quantitative metric, the statistical analysis was based on the paired Wilcoxon test (with $p < 0.05$ considered significant).

### F. Qualitative image evaluation

First, the images used for the radiological scoring were preselected. The methods which demonstrated a significantly worse image or motion model quality metric were excluded from the radiological evaluation.

The remaining images were ranked (from 1 to 5, 1 = best) and image quality was evaluated (on a scale from 1 to 5, 5 = best) by two radiologists (25 and 4 years of experience) by comparing all simultaneously displayed images. While the rank was unambiguously assigned to an image, the quality score could be the same for differently reconstructed images. The average of two radiologists' ranks and quality scores were used for the analysis. The order of the image display was randomized and different for each sequence. Radiologists were blind to the motion correction condition used per reconstruction.

The paired Wilcoxon test was used to determine if the rank and the quality scores differed between reconstruction conditions ($p < 0.05$ considered significant). The Cohen's kappa coefficient was used to evaluate the agreement between the radiologists.

## III. RESULTS

### A. Physiological data analysis

The analysis of sensor drift rates showed that the sensors demonstrated some mechanical instability, with some of them

<figure><img src="image_5.png" /><figcaption>Fig. 4. (a) Number of exams in which MARMOT accelerometers were retained (out of a total of 17 exams) for each position. (b) Number of exams in which MARMOT accelerometer were selected as the best one (out of a total of 17 exams) for each position. The color code indicates the percentage of sensors for both subfigures.</figcaption></figure>

experiencing movement during the experiment. Certain placements were more effective at maintaining stable sensor positions (see Fig. 4a).

Sensors with the highest amplitude were predominantly located on the left side but covered all positions (Fig. 4b).

The NRMSE between the original sensor signals and those reconstructed using only the first singular value ranged from 0.3 to 0.55 across subjects, indicating a significant degree of

<figure><img src="image_6.png" /><figcaption>Fig. 5. Summary and results of the statistical analysis on the sharpness enhancement calculated for T2w and T1w images. The $p$-values correspond to the results of the paired Wilcoxon test. For the better visibility the scales of the bow plots are not the same.</figcaption></figure>

independence between the sensors.

### B. Quantitative image evaluation

The sharpness enhancement analysis did not lead to a definitive conclusion regarding the superiority of any single motion correction method. A summary of the comparison, along with the corresponding statistical results, is shown in Fig. 5.

For the 2D TSE T2w sequence, using all MARMOT sensors resulted in the highest sharpness enhancement, significantly
<figure><img src="image_7.png" /></figure>

Fig. 6. (a) and (b): Typical MSD curves for the belt and all MARMOT sensors (with MSD ratios of 72% and 57%, correspondingly) calculated from the same real-time series. (c) and (d): The best examples of the motion model matching to the real-time sequence for the belt and all MARMOT sensors (with MSD ratios of 28% and 19%, correspondingly). In all subplots: The solid blue curve corresponds to MSD(Ref, RT) and the dashed blue curve to MSD(GRICS, RT) (as defined in Figure 3)Smaller amplitude of the blue dashed line can be interpreted as better motion correction quality. The orange curves depict the normalized values of the leads of respiratory sensors.

outperforming the single MARMOT sensor, and showing a slight improvement over the respiratory belt.

In the case of the 3D GRE T1w sequence, the combination of all MARMOT sensors also yielded the greatest sharpness, but the difference was not statistically significant compared to other correction methods. The correction based on the 3D motion model utilization resulted in a greater variation in sharpness enhancement compared to the 2D model. The 2D model was slightly better in terms of sharpness enhancement for both sensor combinations (with no significant difference).

### C. Motion model evaluation

The motion amplitude had an order of 1 mm and was never greater than 2 mm, and the average MSD between the real-time sequence and the reference image edges varied from 0.4 mm to 1 mm with an average of 0.6 mm. The average MSD between the corrected and real-time images was lower and varied

typical curves, as well as the examples of the best match demonstrated with the metrics MSD(GRICS, RT) and MSD(Ref, RT) are shown in Fig. 6.

A dephasing of the MSD with respect to the belt signal was observed for most subjects. An example of such dephasing can be noticed in Fig. 6a.

The ratio of the average MSD(GRICS, RT) to the average MSD(Ref, RT) varied across different motion models, as summarized in Fig. 7. The 2D motion model incorporating all MARMOT sensors showed the best overall match to the real-time images, with MSD ratios ranging between 30% and 60% for both orientations (excluding outliers). However, this result was not significantly greater than that of the 2D motion model using only the belt sensor, which outperformed the MARMOT sensors in some subjects.

A significant difference was observed between the 2D and 3D motion models for all sensors and orientations, with the 2D model consistently providing predictions closer to the real-time images. Therefore, the 3D motion models were excluded from further analysis.

### D. Radiological image quality evaluation

Due to the exclusion of the 3D motion model, five reconstructions of each of T2w and T1w sequences were generated. They included the reconstructions using 1) the belt indications, 2) the highest-amplitude MARMOT sensor, 3) all MARMOT sensors, 4) the first principal component of the MARMOT signals PCA decomposition, and 5) the non-corrected reconstruction. The T1w 3D images were reconstructed using the motion model from T2w 2D images reconstruction.

The quality of the corrected images was visibly improved overall; however, in many cases, the differences between corrections using different sensor inputs were subtle.

between the subjects and the motion correction methods. Some

<figure><img src="image_8.png" /></figure>

Fig. 7. Summary and results of the statistical analysis on the of the average MSD(GRICS, RT) to the average MSD(Ref, RT). The p-values correspond to the results of the paired Wilcoxon test.
<figure><img src="image_9.png" /><figcaption>Fig. 8. Example of T2w and T1w images reconstructed using the GRICS algorithm supplemented with no motion sensor data, a pneumatic respiratory belt, and all MARMOT accelerometers. One can observe a difference in the level of correction of the ghost artifact (indicated by the white triangle) when the belt and when all MARMOT accelerometers were used.</figcaption></figure>

Nevertheless, certain images showed a significant improvement when all MARMOT sensors were used (refer to Fig. 8 for an example).

Cohen's kappa coefficients, characterizing the radiologists' agreement, were 0.37 for the quality evaluation (fair agreement [24]) and 0.54 for the image ranking (moderate agreement).

A summary of the radiological scoring results, along with the corresponding statistical analyses, is presented in Fig. 9.

The images corrected using data from all MARMOT sensors were significantly superior in quality compared to those corrected with the belt sensor data, across both T2w and T1w sequences, as well as the combined dataset ($p = 0.001$). While images corrected using a single MARMOT sensor did not show a statistically significant difference compared to those corrected with all sensors, they tended to be of lower quality on average.

These findings were supported by the ranking results. Images reconstructed using data from all MARMOT sensors were consistently ranked higher than those corrected with the belt data ($p = 0.002$). Also, they were generally ranked higher than

images reconstructed using data from a single MARMOT sensor.

Some typical examples of motion correction using the best sensor combination (all MARMOT sensors) are presented in Fig. 10.

<figure><img src="image_10.png" /><figcaption>Fig. 10. Zoomed regions of breast tissues demonstrating typical examples of motion correction results.</figcaption></figure>

## IV. DISCUSSION

In this study, we selected a motion correction approach that provided the highest correction quality for supine breast MRI with the wearable BraCoil. We demonstrated that using MRI-compatible MARMOT accelerometer sensors, attached to the external surface of the BraCoil, resulted in better motion

<figure><img src="image_11.png" /><figcaption>Fig. 9. Results of the statistical analysis of image quality and ranking scoring averaged across both radiologists. The p-values correspond to the results of paired Wilcoxon test. It should be noted that using all MARMOT accelerometers always resulted in significantly better results compared to the respiratory belt.</figcaption></figure>
correction than using the belt. This result can be explained by better positioning of the sensors: the belt cannot be placed beneath or directly on the BraCoil, resulting in a dephasing of the respiratory signal to the actual motion. This dephasing invalidates the key assumption of the GRICS algorithm – the linear relationship between the displacement field and the respiratory signal (equation (1b)). Thus, this result is probably generalizable to any flexible coil used for supine breast MRI.

This improvement was observed despite some mechanical instability of the sensors detected during data analysis. Visual inspection following each acquisition revealed that certain MARMOT sensors had indeed partially detached. The sensor positioning resulting in optical fibers (used for motion signal transfer) being less bent demonstrated superior sensor stability. Efforts to improve the setup are ongoing, and for future studies the use of double-sided tape has already been replaced by plastic caps that reliably fasten the sensors to the BraCoil modules.

It was also observed that each sensor position was selected at least once when using a single MARMOT sensor. Although a nearly symmetric distribution of sensor selections was expected, the chosen sensor was typically located on the left side. Interestingly, in [25], where accelerometers were placed directly on the skin, it was found that the right side produced more accurate respiratory waveforms. We believe our results are likely due to coincidence and can be explained by the small sample size and the potential damping of respiratory motion by the coil. If fewer sensors need to be used, excluding the two lower central sensors may be the most reasonable approach.

The use of all MARMOT sensors has been shown to result in more effective motion correction than using just one. This can be explained by the complementary nature of the data from each sensor. SVD analysis indicates that the highest-amplitude sensors account for only 45% to 70% of the motion data, meaning that the additional sensors provide information that enhances the performance of the motion correction algorithm.

Finally, in our work, we also evaluated the efficiency of replacing 2D motion model derived from 2D T2w images with a 3D motion model calculated from a calibration low-resolution 3D T1w sequence. The latter approach could potentially lead to a shorter acquisition protocol, considering the fully sampled T2w sequence might be replaced by an accelerated one in this case. Our results demonstrated that the 2D motion model better matched the actual motion captured in real-time images. Although this finding may seem counterintuitive, we attribute it to two key factors. First, the low-resolution 3D sequence was acquired prior to the 2D T2w sequence in the imaging protocol. Any shift or rotation of the subject between these sequences could result in model misalignment, reducing its accuracy when applied to the high-resolution 3D sequence. Second, which seems to be more important, the resolution of the 3D calibration sequence (1.7 mm) may have been insufficient, as it was similar to the estimated motion range (1-2 mm) observed in real-time sequences.

## V. CONCLUSION

We identified the optimal method for correcting motion

artifacts in supine breast MRI images acquired with the dedicated wearable BraCoil. Our findings demonstrate that the motion model derived from a high-resolution 2D sequence is more accurate than that obtained from a low-resolution 3D sequence.

We conclude that using a combination of seven MRI-compatible MARMOT accelerometer sensors (excluding the displaced sensors) results in superior motion correction quality compared to a respiratory belt. On average, the performance of the motion correction using all MARMOT sensors exceeded that of a single sensor.

We expect that ongoing improvements to the setup will result in better motion signal quality, thereby improving motion correction quality. The protocol established in this study will be used in future investigations involving breast cancer patients.

## ACKNOWLEDGMENT

This work was supported by the CIC-IT of Nancy. With financial support from ITMO Cancer of Aviesan within the framework of the 2021-2030 Cancer Control Strategy, on funds administered by Inserm. It was also co-funded by the French State-Region contract CPER 2015-2020 (IT2MP), by the European Union through the European Regional Development Fund "FEDER-FSE Lorraine et Massif des Vosges 2014-2020", and France Life Imaging network (grant ANR-11-INBS-0006). The sponsor of this study was the CHRU de Nancy (Department of Methodology, Promotion, Investigation—MPI).

## REFERENCES

[1] S. Joukainen et al., "Feasibility of mapping breast cancer with supine breast MRI in patients scheduled for oncoplastic surgery," *Eur. Radiol.*, vol. 29, no. 3, pp. 1435-1443, Mar. 2019, doi: 10.1007/s00330-018-5681-y.

[2] S. L. Perkins, B. A. Hargreaves, and B. L. Daniel, "Preoperative Supine Breast MRI for Surgical Planning: Comparison of Dynamic Contrast Enhancement Curves to Prone Diagnostic MRIs," presented at the Proceedings of the 27th Annual Meeting of the ISMRM. Montreal, QC, Canada, Accessed: Sep. 18, 2024. [Online]. Available: https://archive.ismrm.org/2019/1867.html

[3] N. Janssen et al., "Supine Breast MRI Using Respiratory Triggering," *Acad. Radiol.*, vol. 24, Feb. 2017, doi: 10.1016/j.acra.2017.01.003.

[4] P. Siegler, C. M. B. Holloway, P. Causer, G. Thevathasan, and D. B. Plewes, "Supine breast MRI," *J. Magn. Reson. Imaging*, vol. 34, no. 5, pp. 1212-1217, 2011, doi: https://doi.org/10.1002/jmri.22605.

[5] E. Marchetto, K. Murphy, S. L. Glimberg, and D. Gallichan, "Robust retrospective motion correction of head motion using navigator-based and markerless motion tracking techniques," *Magn. Reson. Med.*, vol. 90, no. 4, pp. 1297-1315, 2023, doi: 10.1002/mrm.29705.

[6] M. Zaitsev, J. Maclaren, and M. Herbst, "Motion artifacts in MRI: A complex problem with many partial solutions," *J. Magn. Reson. Imaging*, vol. 42, no. 4, pp. 887-901, 2015, doi: 10.1002/jmri.24850.
[7] F. Odille, S. Uribe, P. G. Batchelor, C. Prieto, T. Schaeffter, and D. Atkinson, "Model-based reconstruction for cardiac cine MRI without ECG or breath holding," *Magn. Reson. Med.*, vol. 63, no. 5, pp. 1247-1257, 2010.

[8] C. Santelli et al., "Respiratory bellows revisited for motion compensation: Preliminary experience for cardiovascular MR," *Magn. Reson. Med.*, vol. 65, no. 4, pp. 1097-1102, 2011, doi: 10.1002/mrm.22687.

[9] B. Madore et al., "External Hardware and Sensors, for Improved MRI," *J. Magn. Reson. Imaging*, vol. 57, no. 3, pp. 690-705, 2023, doi: 10.1002/jmri.28472.

[10] L. M. Gottwald et al., "Retrospective Camera-Based Respiratory Gating in Clinical Whole-Heart 4D Flow MRI," *J. Magn. Reson. Imaging*, vol. 54, no. 2, pp. 440-451, 2021, doi: 10.1002/jmri.27564.

[11] J. Maclaren, M. Aksoy, and R. Bammer, "Contact-free physiological monitoring using a markerless optical system," *Magn. Reson. Med.*, vol. 74, no. 2, pp. 571-577, 2015, doi: 10.1002/mrm.25781.

[12] B. Chen et al., "Design and validation of a novel mr-compatible sensor for respiratory motion modeling and correction," *IEEE Trans. Biomed. Eng.*, vol. 64, no. 1, pp. 123-133, 2016.

[13] B. Madore, F. Preiswerk, J. S. Bredfeldt, S. Zong, and C.-C. Cheng, "Ultrasound-based sensors to monitor physiological motion," *Med. Phys.*, vol. 48, no. 7, pp. 3614-3622, 2021, doi: 10.1002/mp.14949.

[14] C. Chen et al., "Cardiac and respiratory motion extraction for MRI using pilot tone-a patient study," *Int. J. Cardiovasc. Imaging*, vol. 40, no. 1, pp. 93-105, Jan. 2024, doi: 10.1007/s10554-023-02966-z.

[15] S. Anand and M. Lustig, "Beat Pilot Tone (BPT): Simultaneous MRI and RF motion sensing at arbitrary frequencies," *Magn. Reson. Med.*, vol. 92, no. 4, pp. 1768-1787, 2024, doi: 10.1002/mrm.30150.

[16] K. Isaieva et al., "Feasibility of online non-rigid motion correction for high-resolution supine breast MRI," *Magn. Reson. Med.*, vol. 90, no. 5, pp. 2130-2143, 2023, doi: 10.1002/mrm.29768.

[17] M. Obermann et al., "Panoramic Magnetic Resonance Imaging of the Breast With a Wearable Coil Vest," *Invest. Radiol.*, vol. 58, no. 11, pp. 799-810, Nov. 2023, doi: 10.1097/RLI.0000000000000991.

[18] K. Isaieva et al., "A hardware and software system for MRI applications requiring external device data," *Magn. Reson. Med.*, vol. 88, no. 3, pp. 1406-1418, 2022, doi: 10.1002/mrm.29280.

[19] M. Uecker, S. Zhang, D. Voit, A. Karaus, K.-D. Merboldt, and J. Frahm, "Real-time MRI at a resolution of 20 ms," *NMR Biomed.*, vol. 23, no. 8, pp. 986-994, Oct. 2010, doi: 10.1002/nbm.1585.

[20] F. Odille, P.-A. Vuissoz, P.-Y. Marie, and J. Felblinger, "Generalized reconstruction by inversion of coupled systems (GRICS) applied to free-breathing MRI," *Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med.*, vol. 60, no. 1, pp. 146-157, 2008.

[21] A. Leclaire and L. Moisan, "No-Reference Image Quality Assessment and Blind Deblurring with Sharpness Metrics Exploiting Fourier Phase Information," *J. Math. Imaging*

Vis., vol. 52, no. 1, pp. 145-172, May 2015, doi: 10.1007/s10851-015-0560-5.

[22] P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability," *NeuroImage*, vol. 31, no. 3, pp. 1116-1128, Jul. 2006, doi: 10.1016/j.neuroimage.2006.01.015.

[23] M. Li, C. Kambhamettu, and M. Stone, "Automatic contour tracking in ultrasound images," *Clin. Linguist. Phon.*, vol. 19, no. 6-7, pp. 545-554, Jan. 2005, doi: 10.1080/02699200500113616.

[24] "Interrater reliability: the kappa statistic - PMC." Accessed: Jun. 03, 2022. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900052/

[25] A. Siqueira, A. F. Spirandeli, R. Moraes, and V. Zarzoso, "Respiratory Waveform Estimation From Multiple Accelerometers: An Optimal Sensor Number and Placement Analysis," *IEEE J. Biomed. Health Inform.*, vol. 23, no. 4, pp. 1507-1515, Jul. 2019, doi: 10.1109/JBHI.2018.2867727.