Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)–based reconstruction: prospective, multi-reader, multi-vendor study

Scientific Reports (2023)

Abstract

Objectives

In this prospective, multi-reader, multi-vendor study, we evaluated the performance of a commercially available deep neural network (DNN)–based MR image reconstruction in enabling accelerated 2D fast spin-echo (FSE) knee imaging. Forty-five subjects were prospectively enrolled and randomly divided into three 3T MRIs. Conventional 2D FSE and accelerated 2D FSE sequences were acquired for each subject, and the accelerated FSE images were reconstructed and enhanced with DNN–based reconstruction software (FSE-DNN). Quantitative assessments and diagnostic performances were independently evaluated by three musculoskeletal radiologists. For statistical analyses, paired t-tests, and Pearson’s correlation were used for image quality comparison and inter-reader agreements. Accelerated FSE-DNN reduced scan times by 41.0% on average. FSE-DNN showed better SNR and CNR (p < 0.001). Overall image quality of FSE-DNN was comparable (p > 0.05), and diagnostic performances of FSE-DNN showed comparable lesion detection. Two of cartilage lesions were under-graded or over-graded (n = 2) while there was no significant difference in other image sets (n = 43). Overall inter-reader agreement between FSE-conventional and FSE-DNN showed good agreement (R2 = 0.76; p < 0.001). In conclusion, DNN-based reconstruction can be applied to accelerated knee imaging in multi-vendor MRI scanners, with reduced scan time and comparable image quality. This study suggests the potential for DNN-accelerated knee MRI in clinical practice.

Introduction

It is reported that about 25% of adults experience knee pain, resulting in limitations to their functional capabilities and mobility, and causing a negative impact on their quality of life, and the prevalence of knee pain has shown an upward trend over time, regardless of age1. Magnetic resonance imaging (MRI) plays an important role in evaluating internal derangements in patients with knee pain2. Common indications of knee MRI are trauma, overuse, degeneration, and knee pain. Commonly used knee MRI protocols are parallel image based MR sequences with multi-channel phased array coil3,4. Typical sequences are triplane fat-suppressed fluid-sensitive, sagittal PD-weighted, and coronal or axial T1-weighted image with 15–25 min scan time. 3D sequence or deep learning reconstructions may be added5. Common MRI protocols consisting of four to five separately acquired 2D fast spin-echo (FSE) or 2D turbo spin-echo (TSE) pulse sequences are commonly used as a standard in clinical practice, providing excellent tissue contrast and high spatial resolution, enabling good assessment of meniscal, ligamentous, and cartilaginous injuries6. Accurate and noninvasive imaging evaluation requires three planes of axial, coronal, and sagittal fat-saturated proton density-weighted or intermediate-weighted images, which require repetitive scans with relatively long scan times. Accelerated MRI is essential in knee imaging because patients with knee pain tend to move, causing motion artifacts, especially when scan time is prolonged. Long scan time of MRI scans can result in reduced productivity per MRI scanner and elevated MRI cost7. Implementing accelerated MRI techniques can alleviate patient discomfort and enhance the cost-effectiveness of the process.

Recent advances in various accelerated imaging methods have shown the feasibility of accelerated knee MRI, in some cases enabling a 5-min knee imaging protocol8,9,10. Parallel imaging (PI) is one approach to accelerate MRI data acquisition, and it is based on the principle of acquiring spatial encoding data from overlapping phased-array coil elements that sample the MR signal in parallel11. Although disadvantages of PI include reduced signal-to-noise ratio (SNR), aliasing, and reconstruction-related artifacts, acceleration with PI allows for rapid imaging due to the advancement of multi-channel phased-array coil technology12. In the knee, 2D FSE with PI has been widely utilized for routine 2D FSE protocols9. However, PI acceleration factors higher than 2 cannot be reliably achieved in clinical settings without compromising image quality13. Compressed sensing (CS) was developed on the premise of reconstructing an image from an under-sampled k-space, since the number of data segments in the k-space is a direct determinant of image acquisition time14. The combination of CS and PI allows even faster imaging, with the resultant image quality deemed acceptable15. However, they require a high computational burden during the image reconstruction process with long iteration times, limiting their use in routine clinical practice.

Recently, deep neural network (DNN)–based MRI reconstructions have been proposed, showing great potential to reduce MRI acquisition time16,17. Deep learning-based MRI reconstruction techniques have been approved and are being evaluated in clinical practice18. Currently, the software requires to be evaluated and monitored from its premarket development to post-market performance in real-world radiology19. However, to date, there has been only a few multi-vendor studies20,21,22 that evaluated the image quality and performances with commercially available DNN–based magnetic resonance imaging (MRI) reconstruction.

The purpose of this prospective, multi-reader, multi-vendor study was to evaluate the performance of commercially available DNN-based MR image reconstruction software in enabling accelerated 2D FSE knee imaging in a clinical environment. We hypothesized that highly accelerated 2D FSE knee imaging combined with DNN–based reconstruction would allow a decrease in scan time while yielding comparable image quality and diagnostic performance for ligamentous, meniscal, and cartilaginous lesions against conventional 2D FSE knee MRI.