Evaluation of Shimming Techniques on MRI Breast Image Quality at 1.5T

Abstract

Objective The quality of all clinical MRI is dependent on B0 homogeneity, which is optimized during the shimming part of a prescan or preparatory phase before image acquisition. The purpose of this study was to assess shimming techniques clinically employed for breast MRI across our practice, and to determine factors that correlate with higher image quality for contrast-enhanced breast MRI at 1.5T. Methods One hundred consecutive female patients were retrospectively collected with Institutional Review Board approval. Shimming-related parameters, including shim-box placement and shimming gradient offsets were extracted from prior contrast-enhanced 3D fat-suppressed T1-weighted gradient echo image acquisitions. Three breast radiologists evaluated these images for fat saturation, breast density, overall image quality, and artifacts. Technologist experience was also evaluated for variability of shimming. Generalized linear mixed models were used to compare acquisition parameters between fat saturation. P < 0.05 was considered as statistical significance. Results The percentage of soft tissue inside the field of view (FOV) (ie, Tissue/FOV) in the good fat-saturation group (0.37 ± 0.06) was significantly lower (P < 0.01) than that in the poor fat-saturation group (0.39 ± 0.06). Other shimming-related parameters were found not significantly affecting the fat-saturation outcomes. Technologists with more experience tended to have less variable shimming performance than junior technologists did. Conclusions The quality of clinical MRI and especially breast MRI is highly dependent on shimming. Decreasing Tissue/FOV was associated with good image quality (good fat saturation). Optimization of shimming may require manual shimming or higher-order field-correction strategies.

Hybrid active and passive local shimming (HAPLS) for two‐region MRI

“…Some scanners provide one additional shimming FOV for breast imaging; however, the variability of the parameter adjustment by the technologists is experience-dependent and can affect the shimming output and image quality significantly. 2 It is even more challenging to shim the two-voxel MRS for two breasts simultaneously. 42 The HAPLS approach can improve bilateral breast MRI/MRS significantly, although it will require different designs and optimizations for different applications and FOVs.…”

“…A typical scan routine images the breasts sequentially, which is time‐consuming. Some scanners provide one additional shimming FOV for breast imaging; however, the variability of the parameter adjustment by the technologists is experience‐dependent and can affect the shimming output and image quality significantly 2 . It is even more challenging to shim the two‐voxel MRS for two breasts simultaneously 42 .…”

“…However, in some clinical and functional MRI applications, simultaneously shimming two separate regions can be significantly more efficient, and in some cases even essential. For example, acquiring images of two knees 1 or two breasts, 2,3 or two shoulders at once, can reduce scan time by half if bilateral exams are required. Another example is studying two interacting human brains in one scanner simultaneously, [4][5][6] where B 0 shimming of two side-by-side brains becomes imperative for acquiring EPI images, which are very sensitive to off-resonance effects.…”

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network

“…Consequences of poor system settings can include incomplete water excitation, flip angle inaccuracy, image distortion, or fat suppression failure. For example, recent work by Zhou et al found a statistically significant relationship between the x, y, and z linear shims and the quality of fat suppression in breast MRI [ 4 ]. While their work focused on the linear shims, fat suppression quality relies on the totality of scan-specific system settings.…”

“…Manually aligning these volumes to the individual patient anatomy in all three dimensions is prone to error and intra- and inter-reader variability. The work by Zhou et al also highlighted the variability in pre-scan placement by clinical technologists and how that variability could impact scan-specific system settings and image quality [ 4 ].…”