J Mag Res Imaging

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

Tan ET, Wilmes LJ, Joe BN, Onishi N, Arasu VA, Hylton NM, Marinelli L, Newitt DC

Background: Multi-b-valued/multi-shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal-to-noise ratio and has potentially long scan times.

Purpose: To investigate the effects of model-based denoising with no loss of spatial resolution on multi-shell breast diffusion MRI; to determine the effects of downsampling on multi-shell diffusion; and to quantify these effects in multi-b-valued (three directions per b-value) acquisitions.

Study Type: Prospective (“fully-sampled” multi-shell) and retrospective longitudinal (multi-b).

Subjects: One normal subject (multi-shell) and 10 breast cancer subjects imaging at four timepoints (multi-b).

Field Strength/Sequence: 3T multi-shell acquisition and 1.5T multi-b acquisition.

Assessment: The “fully-sampled” multi-shell acquisition was retrospectively downsampled to determine the bias and error from downsampling. Mean, axial/parallel, radial diffusivity, and fractional anisotropy (FA) were analyzed. Denoising was applied retrospectively to the multi-b-valued breast cancer subject dataset and assessed subjectively for image noise level and tumor conspicuity.

Statistical Tests: Parametric paired t-test (P < 0.05 considered statistically significant) on mean and coefficient of variation of each metric—the apparent diffusion coefficient (ADC) from all b-values, fast ADC, slow ADC, and perfusion fraction. Paired and two-sample t-tests for each metric comparing normal and tumor tissue.

Results: In the multi-shell data, denoising effectively suppressed FA (–45% to –78%), with small biases in mean diffusivity (–5% in normal, +23% in tumor, and –4% in vascular compartments). In the multi-b data, denoising resulted in small biases to the ADC metrics in tumor and normal contralateral tissue (by –3% to +11%), but greatly reduced the coefficient of variation for every metric (by –1% to –24%). Denoising improved differentiation of tumor and normal tissue regions in most metrics and timepoints; subjectively, image noise level and tumor conspicuity were improved in the fast ADC maps.

Data Conclusion: Model-based denoising effectively suppressed erroneously high FA and improved the accuracy of diffusivity metrics.

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