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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