Deep Learning Reconstruction for ¹²⁹Xe Diffusion-Weighted MRI Enables Use of Natural Abundant Xenon and Improved Image Acceleration.

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Abstract

(i) To assess whether ¹²⁹Xe apparent diffusion coefficient (ADC) and diffusive length scale (Lm_D) metrics are quantitatively preserved with deep learning (DL) accelerated acquisition and reconstruction and (ii) to evaluate the feasibility of ¹²⁹Xe diffusion weighted imaging with natural-abundance xenon at increased acceleration factors. Twenty three-dimensional compressed sensing (CS) accelerated ¹²⁹Xe DW MRI datasets were gathered from a cohort of patients with asthma, chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF). Images were retrospectively reconstructed with DL based CS, denoising and de-ringing reconstruction, and compared to conventional CS. ADC and diffusive length scales (Lm_D) were assessed and compared between conventional CS and DL reconstructions. Prospectively acquired DL reconstruction was then assessed in three healthy volunteers who underwent ¹²⁹Xe DW MRI with both natural-abundance and enriched xenon mixes. DL reconstruction qualitatively improved the sharpness, SNR and image quality of ¹²⁹Xe DW images. In the retrospective study, DL reconstruction produced a slight bias in ADC (5.4%) and Lm_D (0.8%) values when compared with conventional CS reconstruction. In the prospective study, DL reconstruction significantly improved the SNR of natural-abundance xenon images and produced ADC and Lm_D values comparable to those achieved with 129-enriched xenon. DL-based CS, denoising and de-ringing significantly improves SNR and image sharpness in 3D ¹²⁹Xe diffusion-weighted MRI while exhibiting a slight bias in ADC and Lm_D. This approach enables the use of natural-abundance xenon and higher acceleration factors, offering substantial cost reduction and improved clinical feasibility for hyperpolarized ¹²⁹Xe lung morphometry.

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