Feasibility and Implementation of a Deep Learning MR Reconstruction for TSE Sequences in Musculoskeletal Imaging

Abstract

Magnetic Resonance Imaging (MRI) of the musculoskeletal system is one of the most common examinations in clinical routine. The application of Deep Learning (DL) reconstruction for MRI is increasingly gaining attention due to its potential to improve the image quality and reduce the acquisition time simultaneously. However, the technology has not yet been implemented in clinical routine for turbo spin echo (TSE) sequences in musculoskeletal imaging. The aim of this study was therefore to assess the technical feasibility and evaluate the image quality. Sixty examinations of knee, hip, ankle, shoulder, hand, and lumbar spine in healthy volunteers at 3 T were included in this prospective, internal-review-board-approved study. Conventional (TSE_S) and DL-based TSE sequences (TSE_DL) were compared regarding image quality, anatomical structures, and diagnostic confidence. Overall image quality was rated to be excellent, with a significant improvement in edge sharpness and reduced noise compared to TSE_S (p < 0.001). No difference was found concerning the extent of artifacts, the delineation of anatomical structures, and the diagnostic confidence comparing TSE_S and TSE_DL (p > 0.05). Therefore, DL image reconstruction for TSE sequences in MSK imaging is feasible, enabling a remarkable time saving (up to 75%), whilst maintaining excellent image quality and diagnostic confidence.

Keywords: deep learning reconstruction; image processing; magnetic resonance imaging; musculoskeletal imaging.

Figure 1

Exemplary sampling pattern used for the k-space data acquisition (red arrows point to the acquired date; black arrows point to the missing data). Along the phase encoding direction, data are under sampled by an acceleration factor R. An autocalibration region covering the center of k-space is acquired, either along with the imaging acquisition or separately with a different image contrast. A fraction of the periphery may be skipped, usually referred to as phase resolution.

Figure 2

Architecture of the fixed iterative reconstruction scheme. Pre-cascades address data consistency and generate an image similar to conventional parallel imaging, cascades use an alternating scheme between data consistency and regularization, post-cascades reemphasize consistency of a reconstructed image and acquired data. Note that only the regularization is a Convolutional Neural Network (CNN). Nevertheless, the whole architecture can be presented by a general network.

Figure 3

Example of a Deep Learning and standard PD-weighted turbo spin echo (TSE) image of the knee in sagittal orientation. Note that the extent of noise in TSE_DL (left) is distinctly reduced compared to TSE_S (right), which comes with the fact that, at the same time, very small structures such as small bone channels disappear. The visibility of anatomic relevant structures is not impaired.

Figure 4

Example of a Deep Learning (left) and standard (right) PD-weighted turbo spin echo image of the shoulder in coronal orientation.

Figure 5

Example of a Deep Learning and standard T1- (right) and T2-weighted (left) turbo spin echo image of the lumbar spine in sagittal orientation. Note that TSE_DL show lower extents of noise both in T1- and T2-weighted imaging. Nonetheless, some small structures, such as small bone canals, disappear; there is no impact on the delineation and assessment of relevant anatomical structures in both TSE_S and TSE_DL.

Figure 6

Example of a Deep Learning (left) and standard (right) PD-weighted turbo spin echo image of the hip in axial orientation. Note that although the assessment of the bone was rated to be lower, the assessment of anatomical structures and articular cartilage, as well as the delineation of ligaments and tendons, are comparable between TSE_DL and TSE_S.

Figure 7

Example of a Deep Learning (left) and standard (right) PD-weighted turbo spin echo image of the ankle in axial orientation.

Figure 8

Example of a Deep Learning (left) and standard (right) PD-weighted turbo spin echo image of the hand in coronal orientation.

Figure 9

Comparison of TSE_S (A), TSE_DL raw data reconstructed with a standard (GRAPPA) method (B) and TSE_DL (C) of a knee in sagittal orientation. Note that the extent of noise in TSE_DL with standard reconstruction (B) is increased compared to TSE_S (A) and TSE_DL.

Figure 10

Comparison of TSE_S (A), TSE_DL raw data reconstructed with a standard (GRAPPA) method (B) and TSE_DL (C) of a knee in coronal orientation. Note that the image quality in TSE_DL (C) is increased compared to TSE_S (A) and TSE_DL with standard reconstruction (B).

Figure 11

Exemplary visualization of signal-to-noise ratio (SNR) as SNR-maps. On the (left), results of the TSE_DL dataset, and on the (right), of a TSE_S dataset of knee in sagittal orientation. The TSE_DL dataset shows lower noise levels and an increase of SNR with a more homogeneous distribution compared to the TSE_S dataset. In addition, SNR levels throughout the whole image are more homogeneous.