Fast MRI of bones in the knee -- An AI-driven reconstruction approach for adiabatic inversion recovery prepared ultra-short echo time sequences

TL;DR

This study introduces an AI-driven reconstruction method for ultra-short echo time MRI of the knee bones, significantly reducing scan times while maintaining image quality for clinical use.

Contribution

A novel deep learning-based reconstruction approach that accelerates IR-UTE MRI of the knee bones, enabling faster imaging without compromising detail.

Findings

High-quality images at 5-minute acquisition time

Effective noise suppression and contrast preservation

Potential for clinical application in rapid bone assessment

Abstract

Purpose: Inversion recovery prepared ultra-short echo time (IR-UTE)-based MRI enables radiation-free visualization of osseous tissue. However, sufficient signal-to-noise ratio (SNR) can only be obtained with long acquisition times. This study proposes a data-driven approach to reconstruct undersampled IR-UTE knee data, thereby accelerating MR-based 3D imaging of bones. Methods: Data were acquired with a 3D radial IR-UTE pulse sequence, implemented using the open-source framework Pulseq. A denoising convolutional neural network (DnCNN) was trained in a supervised fashion using data from eight healthy subjects. Conjugate gradient sensitivity encoding (CG-SENSE) reconstructions of different retrospectively undersampled subsets (corresponding to 2.5-min, 5-min and 10-min acquisition times) were paired with the respective reference dataset reconstruction (30-min acquisition time). The DnCNN was then integrated into a Landweber-based reconstruction algorithm, enabling physics-based iterative reconstruction. Quantitative evaluations of the approach were performed using one prospectively accelerated scan as well as retrospectively undersampled datasets from four additional healthy subjects, by assessing the structural similarity index measure (SSIM), the peak signal-to-noise ratio (PSNR), the normalized root mean squared error (NRMSE), and the perceptual sharpness index (PSI). Results: Both the reconstructions of prospective and retrospective acquisitions showed good agreement with the reference dataset, indicating high image quality, particularly for an acquisition time of 5 min. The proposed method effectively preserves contrast and structural details while suppressing noise, albeit with a slight reduction in sharpness. Conclusion: The proposed method is poised to enable MR-based bone assessment in the knee within clinically feasible scan times.