MRI Advancements in Musculoskeletal Clinical and Research Practice

Managing hardware-related metal artifacts in MRI: current and evolving techniques

Magnetic resonance imaging (MRI) around metal implants has been challenging due to magnetic susceptibility differences between metal implants and adjacent tissues, resulting in image signal loss, geometric distortion, and loss of fat suppression. These artifacts can compromise the diagnostic accuracy and the evaluation of surrounding anatomical structures. As the prevalence of total joint replacements continues to increase in our aging society, there is a need for proper radiological assessment of tissues around metal implants to aid clinical decision-making in the management of post-operative complaints and complications. Various techniques for reducing metal artifacts in musculoskeletal imaging have been explored in recent years. One approach focuses on improving hardware components. High-density multi-channel radiofrequency (RF) coils, parallel imaging techniques, and gradient warping correction enable signal enhancement, image acquisition acceleration, and geometric distortion minimization. In addition, the use of susceptibility-matched implants and low-field MRI helps to reduce magnetic susceptibility differences. The second approach focuses on metal artifact reduction sequences such as view-angle tilting (VAT) and slice-encoding for metal artifact correction (SEMAC). Iterative reconstruction algorithms, deep learning approaches, and post-processing techniques are used to estimate and correct artifact-related errors in reconstructed images. This article reviews recent developments in clinically applicable metal artifact reduction techniques as well as advances in MR hardware. The review provides a better understanding of the basic principles and techniques, as well as an awareness of their limitations, allowing for a more reasoned application of these methods in clinical settings.

Present and future of whole-body MRI in metastatic disease and myeloma: how and why you will do it

Metastatic disease and myeloma present unique diagnostic challenges due to their multifocal nature. Accurate detection and staging are critical for determining appropriate treatment. Bone scintigraphy, skeletal radiographs and CT have long been the mainstay for the assessment of these diseases, but have limitations, including reduced sensitivity and radiation exposure. Whole-body MRI has emerged as a highly sensitive and radiation-free alternative imaging modality. Initially developed for skeletal screening, it has extended tumor screening to all organs, providing morphological and physiological information on tumor tissue. Along with PET/CT, whole-body MRI is now accepted for staging and response assessment in many malignancies. It is the first choice in an ever increasing number of cancers (such as myeloma, lobular breast cancer, advanced prostate cancer, myxoid liposarcoma, bone sarcoma, …). It has also been validated as the method of choice for cancer screening in patients with a predisposition to cancer and for staging cancers observed during pregnancy. The current and future challenges for WB-MRI are its availability facing this number of indications, and its acceptance by patients, radiologists and health authorities. Guidelines have been developed to optimize image acquisition and reading, assessment of lesion response to treatment, and to adapt examination designs to specific cancers. The implementation of 3D acquisition, Dixon method, and deep learning-based image optimization further improve the diagnostic performance of the technique and reduce examination durations. Whole-body MRI screening is feasible in less than 30 min. This article reviews validated indications, recent developments, growing acceptance, and future perspectives of whole-body MRI.

Modern low-field MRI

This narrative review explores recent advancements and applications of modern low-field (≤ 1 Tesla) magnetic resonance imaging (MRI) in musculoskeletal radiology. Historically, high-field MRI systems (1.5 T and 3 T) have been the standard in clinical practice due to superior image resolution and signal-to-noise ratio. However, recent technological advancements in low-field MRI offer promising avenues for musculoskeletal imaging. General principles of low-field MRI systems are being introduced, highlighting their strengths and limitations compared to high-field counterparts. Emphasis is placed on advancements in hardware design, including novel magnet configurations, gradient systems, and radiofrequency coils, which have improved image quality and reduced susceptibility artifacts particularly in musculoskeletal imaging. Different clinical applications of modern low-field MRI in musculoskeletal radiology are being discussed. The diagnostic performance of low-field MRI in diagnosing various musculoskeletal pathologies, such as ligament and tendon injuries, osteoarthritis, and cartilage lesions, is being presented. Moreover, the discussion encompasses the cost-effectiveness and accessibility of low-field MRI systems, making them viable options for imaging centers with limited resources or specific patient populations. From a scientific standpoint, the amount of available data regarding musculoskeletal imaging at low-field strengths is limited and often several decades old. This review will give an insight to the existing literature and summarize our own experiences with a modern low-field MRI system over the last 3 years. In conclusion, the narrative review highlights the potential clinical utility, challenges, and future directions of modern low-field MRI, offering valuable insights for radiologists and healthcare professionals seeking to leverage these advancements in their practice.

MRI and single-cell RNA sequence results reveal the influence of anterior talofibular ligament injury on osteochondral lesions of the talus

Anterior talofibular ligament injuries and osteochondral lesions of the talus present unique challenges to orthopedic surgeons. This study aimed to investigate the relevant relationship between them by analyzing the Magnetic resonance imaging (MRI) results of clinical patients and single-cell RNA sequence (scRNA seq) results of healthy talus cartilage to discuss the risk factors. Data from 164 patients from 2018 to 2023 was retrospectively analyzed. The correlation analysis between ATFL injury grade and the Hepple stage of OLT determined by MRI was performed. Publicly available single-cell RNA datasets were collected. Single-cell RNA datasets from five volunteers of healthy talus cartilage were analyzed. ATFL injury grade was relevant with the Hepple stage of OLT (P < 0.05). The results of multivariate logistic regression analysis showed that injured area was the independent influencing factor of the incidence rate and the severity of OLT (P < 0.05). The Hepple stage of OLT was relevant with AOFAS and VAS (P < 0.05). Single-cell RNA sequence results showed that among the 9 subtypes of chondrocytes, the interaction strength between HTC-A and HTC-B is the highest. Their physical interactions are mainly achieved through the CD99 signaling pathway, and factor interactions are mainly achieved through the ANGPTL signaling pathway. Anterior talofibular ligament injury may lead to osteochondral lesions of the talus. Early medical intervention should be carried out for ligament injuries to restore joint stability and avoid cartilage damage.

Inflammatory Knee Synovitis: Evaluation of an Accelerated FLAIR Sequence Compared With Standard Contrast-Enhanced Imaging

OBJECTIVES

The aim of this study was to assess the diagnostic value and accuracy of a deep learning (DL)-accelerated fluid attenuated inversion recovery (FLAIR) sequence with fat saturation (FS) in patients with inflammatory synovitis of the knee.

MATERIALS AND METHODS

Patients with suspected knee synovitis were retrospectively included between January and September 2023. All patients underwent a 3 T knee magnetic resonance imaging including a DL-accelerated noncontrast FLAIR FS sequence (acquisition time: 1 minute 38 seconds) and a contrast-enhanced (CE) T1-weighted FS sequence (acquisition time: 4 minutes 50 seconds), which served as reference standard. All knees were scored by 2 radiologists using the semiquantitative modified knee synovitis score, effusion synovitis score, and Hoffa inflammation score. Diagnostic confidence, image quality, and image artifacts were rated on separate Likert scales. Wilcoxon signed rank test was used to compare the semiquantitative scores. Interreader and intrareader reproducibility were calculated using Cohen κ.

RESULTS

Fifty-five patients (mean age, 52 ± 17 years; 28 females) were included in the study. Twenty-seven patients (49%) had mild to moderate synovitis (synovitis score 6-13), and 17 patients (31%) had severe synovitis (synovitis score >14). No signs of synovitis were detected in 11 patients (20%) (synovitis score <5). Semiquantitative assessment of the whole knee synovitis score showed no significant difference between the DL-accelerated FLAIR sequence and the CE T1-weighted sequence (mean FLAIR score: 10.69 ± 8.83, T1 turbo spin-echo FS: 10.74 ± 10.32; P = 0.521). Both interreader and intrareader reproducibility were excellent (range Cohen κ [0.82-0.96]).

CONCLUSIONS

Assessment of inflammatory knee synovitis using a DL-accelerated noncontrast FLAIR FS sequence was feasible and equivalent to CE T1-weighted FS imaging.

Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T

OBJECTIVES

The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms.

MATERIALS AND METHODS

This was a prospective single-center study. Twenty-three healthy volunteers underwent 7 T knee magnetic resonance imaging. Two-, 3-, and 4-fold accelerated high-resolution fat-signal-suppressing proton density (PD-fs) and T1-weighted coronal 2D TSE acquisitions with an encoded voxel volume of 0.31 × 0.31 × 1.5 mm3 were acquired. Each set of raw data was reconstructed with a DL-based and a conventional Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm. Three readers rated image contrast, sharpness, artifacts, noise, and overall quality. Friedman analysis of variance and the Wilcoxon signed rank test were used for comparison of image quality criteria.

RESULTS

The mean age of the participants was 32.0 ± 8.1 years (15 male, 8 female). Acquisition times at 4-fold acceleration were 4 minutes 15 seconds (PD-fs, Supplemental Video is available at http://links.lww.com/RLI/A938) and 3 minutes 9 seconds (T1, Supplemental Video available at http://links.lww.com/RLI/A939). At 4-fold acceleration, image contrast, sharpness, noise, and overall quality of images reconstructed with the DL-based algorithm were significantly better rated than the corresponding GRAPPA reconstructions (P < 0.001). Four-fold accelerated DL-reconstructed images scored significantly better than 2- to 3-fold GRAPPA-reconstructed images with regards to image contrast, sharpness, noise, and overall quality (P ≤ 0.031). Image contrast of PD-fs images at 2-fold acceleration (P = 0.087), image noise of T1-weighted images at 2-fold acceleration (P = 0.180), and image artifacts for both sequences at 2- and 3-fold acceleration (P ≥ 0.102) of GRAPPA reconstructions were not rated differently than those of 4-fold accelerated DL-reconstructed images. Furthermore, no significant difference was observed for all image quality measures among 2-fold, 3-fold, and 4-fold accelerated DL reconstructions (P ≥ 0.082).

CONCLUSIONS

This study explored the technical potential of DL-based image reconstruction in accelerated 2D TSE acquisitions of the knee at 7 T. DL reconstruction significantly improved a variety of image quality measures of high-resolution TSE images acquired with a 4-fold parallel-imaging acceleration compared with a conventional reconstruction algorithm.

Updates in diagnostic tools for diagnosing nerve injury and compressions

Predicting prognosis after nerve injury and compression can be challenging, even for the experienced clinician. Although thorough clinical assessment can aid diagnosis, we cannot always be precise about long-term functional recovery of either motor or sensory nerves. To evaluate the severity of nerve injury, surgical exploration remains the gold standard, particularly after iatrogenic injury and major nerve injury from trauma, such as brachial plexus injury. Recently, advances in imaging techniques (ultrasound, magnetic resonance imaging [MRI] and MR neurography) along with multimodality assessment, including electrodiagnostic testing, have allowed us to have a better preoperative understanding of nerve continuity and prediction of nerve health and possible recovery. This article outlines the current and potential roles for clinical assessment, exploratory surgery, electrodiagnostic testing ultrasound and MRI in entrapment neuropathies, inflammatory neuritis and trauma. Emphasis is placed on those modalities that are improving in diagnostic accuracy of nerve assessment before any surgical intervention.

Artificial intelligence powered advancements in upper extremity joint MRI: A review

Magnetic resonance imaging (MRI) is an indispensable medical imaging examination technique in musculoskeletal medicine. Modern MRI techniques achieve superior high-quality multiplanar imaging of soft tissue and skeletal pathologies without the harmful effects of ionizing radiation. Some current limitations of MRI include long acquisition times, artifacts, and noise. In addition, it is often challenging to distinguish abutting or closely applied soft tissue structures with similar signal characteristics. In the past decade, Artificial Intelligence (AI) has been widely employed in musculoskeletal MRI to help reduce the image acquisition time and improve image quality. Apart from being able to reduce medical costs, AI can assist clinicians in diagnosing diseases more accurately. This will effectively help formulate appropriate treatment plans and ultimately improve patient care. This review article intends to summarize AI's current research and application in musculoskeletal MRI, particularly the advancement of DL in identifying the structure and lesions of upper extremity joints in MRI images.