Denoising and Multiple Tissue Compartment Visualization of Multi-b-Valued Breast Diffusion MRI

Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRI

PURPOSE

This study aimed to assess the technical feasibility, the impact on image quality, and the acquisition time (TA) of a new deep-learning-based reconstruction algorithm in diffusion weighted imaging (DWI) of breast magnetic resonance imaging (MRI).

METHODS

Retrospective analysis of 55 female patients who underwent breast DWI at 1.5 T. Raw data were reconstructed using a deep-learning (DL) reconstruction algorithm on a subset of the acquired averages, therefore a reduction of TA. Clinically used standard DWI sequence (DWIStd) and the DL-reconstructed images (DWIDL) were compared. Two radiologists rated the image quality of b800 and ADC images, using a Likert-scale from 1 to 5 with 5 being considered perfect image quality. Signal intensities were measured by placing a region of interest (ROI) at the same position in both sequences.

RESULTS

TA was reduced by 40 % in DWIDL, compared to DWIStd, DWIDL improved noise and sharpness while maintaining contrast, the level of artifacts, and diagnostic confidence. There were no differences regarding the signal intensity values of the apparent diffusion coefficient (ADC), (p = 0.955), b50-values (p = 0.070) and b800-values (p = 0.415) comparing standard and DL-imaging. Lesion assessment showed no differences regarding the number of lesions in ADC and DWI (both p = 1.000) and regarding the lesion diameter in DWI (p = 0.961;0.972) and ADC (p = 0.961;0.972).

CONCLUSIONS

The novel deep-learning-based reconstruction algorithm significantly reduces TA in breast DWI, while improving sharpness, reducing noise, and maintaining a comparable level of image quality, artifacts, contrast, and diagnostic confidence. DWIDL does not influence the quantifiable parameters.

Diffusion MRI fiber diameter for muscle denervation assessment

BACKGROUND

To develop and evaluate a diffusion MRI-based apparent muscle fiber diameter (AFD) method in patients with muscle denervation. It was hypothesized that AFD differences between denervated, non-denervated and control muscles would be greater than those from standard diffusion metrics.

METHODS

A spin-echo diffusion acquisition with multi-b-valued diffusion sampling was used. An orientation-invariant dictionary approach utilized a cylinder-based forward model and multi-compartment model for obtaining restricted and free fractions. Simulations were performed to determine precision, bias, and optimize dictionary parameters. In all, 18 exams of patients with muscle denervation and 8 exams of healthy subjects were performed at 3T. Six regions of interests (ROIs) within separate shoulder muscles were selected, yielding three groups consisting 47 control (healthy), 36 non-denervated (patients), and 68 denervated (patients) muscle ROIs. Two-sample t-tests (α=0.05) between groups were performed with Holm-Bonferroni correction. T2- and fat fraction (FF)-mapping were acquired for comparison.

RESULTS

Mean AFD was 89.7±13.6 µm in control, 71.6±15.3 µm in non-denervated, and 60.7±15.9 µm in denervated muscles and were significantly different (P<0.001) in paired comparisons and in 10/12 individual muscle region comparisons. Correlation between AFD and FF (-0.331, P<0.001) was low, but correlation between FA and FF was negligible (0.197, P=0.016). Correlation was low between AFD and T2 (-0.395, P<0.001) and between FA and T2 (0.359, P<0.001).

CONCLUSIONS

Diffusion MRI-based AFD complements T2- and FF-mapping techniques to non-invasively assess muscle denervation.

Intravoxel Incoherent Motion Magnetic Resonance Imaging in Skeletal Muscle: Review and Future Directions

Throughout the body, muscle structure and function can be interrogated using a variety of noninvasive magnetic resonance imaging (MRI) methods. Recently, intravoxel incoherent motion (IVIM) MRI has gained momentum as a method to evaluate components of blood flow and tissue diffusion simultaneously. Much of the prior research has focused on highly vascularized organs, including the brain, kidney, and liver. Unique aspects of skeletal muscle, including the relatively low perfusion at rest and large dynamic range of perfusion between resting and maximal hyperemic states, may influence the acquisition, postprocessing, and interpretation of IVIM data. Here, we introduce several of those unique features of skeletal muscle; review existing studies of IVIM in skeletal muscle at rest, in response to exercise, and in disease states; and consider possible confounds that should be addressed for muscle-specific evaluations. Most studies used segmented nonlinear least squares fitting with a b-value threshold of 200 sec/mm² to obtain IVIM parameters of perfusion fraction (f), pseudo-diffusion coefficient (D*), and diffusion coefficient (D). In healthy individuals, across all muscles, the average ± standard deviation of D was 1.46 ± 0.30 × 10^-3  mm² /sec, D* was 29.7 ± 38.1 × 10^-3  mm² /sec, and f was 11.1 ± 6.7%. Comparisons of reported IVIM parameters in muscles of the back, thigh, and leg of healthy individuals showed no significant difference between anatomic locations. Throughout the body, exercise elicited a positive change of all IVIM parameters. Future directions including advanced postprocessing models and potential sequence modifications are discussed. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.