New-Generation Low-Field Magnetic Resonance Imaging of Hip Arthroplasty Implants Using Slice Encoding for Metal Artifact Correction: First In Vitro Experience at 0.55 T and Comparison With 1.5 T
OBJECTIVES/OBJECTIVE:Despite significant progress, artifact-free visualization of the bone and soft tissues around hip arthroplasty implants remains an unmet clinical need. New-generation low-field magnetic resonance imaging (MRI) systems now include slice encoding for metal artifact correction (SEMAC), which may result in smaller metallic artifacts and better image quality than standard-of-care 1.5 T MRI. This study aims to assess the feasibility of SEMAC on a new-generation 0.55 T system, optimize the pulse protocol parameters, and compare the results with those of a standard-of-care 1.5 T MRI. MATERIALS AND METHODS/METHODS:Titanium (Ti) and cobalt-chromium total hip arthroplasty implants embedded in a tissue-mimicking American Society for Testing and Materials gel phantom were evaluated using turbo spin echo, view angle tilting (VAT), and combined VAT and SEMAC (VAT + SEMAC) pulse sequences. To refine an MRI protocol at 0.55 T, the type of metal artifact reduction techniques and the effect of various pulse sequence parameters on metal artifacts were assessed through qualitative ranking of the images by 3 expert readers while taking measured spatial resolution, signal-to-noise ratios, and acquisition times into consideration. Signal-to-noise ratio efficiency and artifact size of the optimized 0.55 T protocols were compared with the 1.5 T standard and compressed-sensing SEMAC sequences. RESULTS:Overall, the VAT + SEMAC sequence with at least 6 SEMAC encoding steps for Ti and 9 for cobalt-chromium implants was ranked higher than other sequences for metal reduction (P < 0.05). Additional SEMAC encoding partitions did not result in further metal artifact reductions. Permitting minimal residual artifacts, low magnetic susceptibility Ti constructs may be sufficiently imaged with optimized turbo spin echo sequences obviating the need for SEMAC. In cross-platform comparison, 0.55 T acquisitions using the optimized protocols are associated with 45% to 64% smaller artifacts than 1.5 T VAT + SEMAC and VAT + compressed-sensing/SEMAC protocols at the expense of a 17% to 28% reduction in signal-to-noise ratio efficiency. B1-related artifacts are invariably smaller at 0.55 T than 1.5 T; however, artifacts related to B0 distortion, although frequently smaller, may appear as signal pileups at 0.55 T. CONCLUSIONS:Our results suggest that new-generation low-field SEMAC MRI reduces metal artifacts around hip arthroplasty implants to better advantage than current 1.5 T MRI standard of care. While the appearance of B0-related artifacts changes, reduction in B1-related artifacts plays a major role in the overall benefit of 0.55 T.
How does a "Dry Tap" Impact the Accuracy of Preoperative Aspiration Results in Predicting Chronic PJI?
INTRODUCTION/BACKGROUND:Periprosthetic joint infection (PJI) after total hip arthroplasty (THA) is challenging to diagnose. We aimed to evaluate the impact of dry taps requiring saline lavage during preoperative intraarticular hip aspiration on the accuracy of diagnosing PJI before revision surgery. METHODS:A retrospective review was conducted for THA patients with suspected PJI who received an image-guided hip aspiration from May 2016 to February 2020. Musculoskeletal Infection Society (MSIS) diagnostic criteria for PJI were compared between patients who had dry tap (DT) versus successful tap (ST). Sensitivity and specificity of synovial markers were compared between the DT and ST groups. Concordance between preoperative and intraoperative cultures was determined for the two groups. RESULTS:In total, 335 THA patients met inclusion criteria. A greater proportion of patients in the ST group met MSIS criteria preoperatively (30.2%vs.8.3%, p<0.001). Patients in the ST group had higher rates of revision for PJI (28.4%vs.17.5%, p=0.026) and for any indication (48.4%vs.36.7%, p=0.039). MSIS synovial WBC count thresholds were more sensitive in the ST group (90.0%vs.66.7%). There was no difference in culture concordance (67.9%vs.65.9%,p=0.709), though the DT group had a higher rate of negative preoperative cultures followed by positive intraoperative cultures (85.7%vs.41.1%, p=0.047). CONCLUSION/CONCLUSIONS:Our results indicate that approximately one-third of patients have dry hip aspiration, and in these patients cultures are less predictive of intraoperative findings. This suggests that surgeons considering potential PJI after THA should apply extra scrutiny when interpreting negative results in patients who require saline lavage for hip joint aspiration.
Factors predicting hip joint aspiration yield or "dry taps" in patients with total hip arthroplasty
BACKGROUND:Image-guided joint aspirations used to assist the diagnosis of periprosthetic joint infection (PJI) may commonly result in a dry tap-or insufficient fluid for culture and cell count analysis. Dry tap aspirations are painful and invasive for patients and often utilize a subsequent saline lavage to obtain a microbiology sample. Currently, there is a paucity of the literature addressing predictors that could suggest whether a dry tap will occur. The purpose of this study was to examine the effects of various factors on "dry tap" occurrence in patients with suspected PJI following total hip arthroplasty (THA). METHODS:A retrospective review was performed among THA patients suspected for PJI who received image-guided joint aspiration procedures at our institution from May 2016 to February 2020. The procedural factors included the imaging modality used for aspiration, anatomic approach, needle gauge size used, and the presence of a trainee. The patient-specific factors included number of prior ipsilateral hip surgeries, femoral head size, ESR/CRP values, and BMI. RESULTS:In total, 336 patients met our inclusion criteria. One hundred and twenty hip aspirations resulted in a dry tap (35.7%) where the patients underwent a saline lavage. Among the procedural and patient-specific factors, none of the factors were found to be statistically different between the two cohorts nor conferred any greater odds of a dry tap occurring. CONCLUSION/CONCLUSIONS:No associations with dry tap occurrence were found among the procedural and patient-specific factors studied. Further research is needed to identify additional factors that may be more predictive of dry taps.
Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.
Anterior shoulder instability in the aging population: MRI injury pattern and management
Background: Literature on glenohumeral dislocations has focused on younger patient populations due to high recurrence rates. However, the spectrum of injuries sustained in younger versus older patient populations is reported to be quite different. Objective: To describe MRI findings and management of anterior shoulder instability in the aging (â‰¥60 years) population. Methods: Shoulder MRIs of anterior glenohumeral dislocators aged â‰¥40 were subdivided into <60 or â‰¥60 age groups, and reviewed by two musculoskeletal radiologists for: Hill-Sachs lesion, other fracture, glenoid injury, capsulolabral injury, rotator cuff tear, muscle atrophy, and axillary nerve injury. Fischer exact and logistic regression evaluated for significant differences between cohorts, and inter-reader agreement was assessed. Surgical management was recorded, if available. Results: 104 shoulder MRIs (40-79 years, mean=58.3, 52 females, 52 males) were reviewed (N=54 age <60, N=50 age â‰¥60). Acute high-grade or full-thickness supraspinatus (64.0% vs. 37.0%, p=0.001), infraspinatus (28.0% vs. 14.8%, p=0.028), and subscapularis tears (22.0% vs. 3.7%, p=0.003) were more common in the â‰¥60 group. Hill-Sachs lesions were more common in the <60 group (81.5% vs. 62.0%, p=0.046). Greater tuberosity fractures were seen in 15.3% of the overall cohort, coracoid fractures in 4.8%, and axillary nerve injuries in 16.3%. Inter-reader concordance was 88.5-89.4% for rotator cuff tears, and 89.4-97.1% for osseous injury. The <60 group had rotator cuff repair in 11/37 subjects (29.7%), and labral repair in 11/37 (29.7%), while the â‰¥60 group underwent rotator cuff repair in 17/36 (47.2%), reverse shoulder arthroplasty in 6/36 (16.7%), and labral repair in 6/36 (16.7%). Conclusion: Radiologists should have a high index of suspicion for acute rotator cuff tears in anterior shoulder instability, especially in aging populations. Greater tuberosity or coracoid fractures and axillary nerve injury occur across all ages, while Hill-Sachs injuries are more common in younger patients. Clinical Impact: Acute, high-grade or full-thickness rotator cuff tears are seen with higher frequency in older populations after anterior glenohumeral dislocation in the elderly. Osseous and nerve injuries are important causes of patient morbidity that, if not carefully sought out, may be overlooked by the interpreting radiologist on routine imaging.
Using Deep Learning to Accelerate Knee MRI at 3T: Results of an Interchangeability Study
OBJECTIVE:Deep Learning (DL) image reconstruction has the potential to disrupt the current state of MR imaging by significantly decreasing the time required for MR exams. Our goal was to use DL to accelerate MR imaging in order to allow a 5-minute comprehensive examination of the knee, without compromising image quality or diagnostic accuracy. METHODS:A DL model for image reconstruction using a variational network was optimized. The model was trained using dedicated multi-sequence training, in which a single reconstruction model was trained with data from multiple sequences with different contrast and orientations. Following training, data from 108 patients were retrospectively undersampled in a manner that would correspond with a net 3.49-fold acceleration of fully-sampled data acquisition and 1.88-fold acceleration compared to our standard two-fold accelerated parallel acquisition. An interchangeability study was performed, in which the ability of 6 readers to detect internal derangement of the knee was compared for the clinical and DL-accelerated images. RESULTS:The study demonstrated a high degree of interchangeability between standard and DL-accelerated images. In particular, results showed that interchanging the sequences would result in discordant clinical opinions no more than 4% of the time for any feature evaluated. Moreover, the accelerated sequence was judged by all six readers to have better quality than the clinical sequence. CONCLUSIONS:An optimized DL model allowed for acceleration of knee images which performed interchangeably with standard images for the detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images.
Supraspinatus muscle shear wave elastography (SWE): detection of biomechanical differences with varying tendon quality prior to gray-scale morphologic changes
OBJECTIVE:The purpose of this study was to determine whether SWE can detect biomechanical changes in the supraspinatus muscle that occur with increasing supraspinatus tendon abnormality prior to morphologic gray-scale changes. MATERIALS AND METHODS/METHODS:An IRB approved, HIPAA compliant retrospective study of shoulder ultrasounds from 2013-2018 was performed. The cohort consisted of 88 patients (mean age 55 Â± 15 years old) with 110 ultrasounds. Images were acquired in longitudinal orientation to the supraspinatus muscle with shear wave velocity (SWV) point quantification. The tendon and muscle were graded in order of increasing tendinosis/tear (1-4 scale) and increasing fatty infiltration (0-3 scale). Mixed model analysis of variance, analysis of covariance, and Spearman rank correlation were used for statistical analysis. RESULTS:There was no statistically significant age or sex dependence for supraspinatus muscle SWV (p = 0.314, 0.118, respectively). There was no significant correlation between muscle SWV and muscle or tendon grade (p = 0.317, 0.691, respectively). In patients with morphologically normal muscle on gray-scale ultrasound, there were significant differences in muscle SWV when comparing tendon grade 3 with grades 1, 2, and 4 (p = 0.018, 0.025, 0.014, respectively), even when adjusting for gender and age (p = 0.044, 0.028, 0.018, respectively). Pairwise comparison of tendon grades other than those mentioned did not achieve statistical significance (p > 0.05). CONCLUSION/CONCLUSIONS:SWE can detect biomechanical differences within the supraspinatus muscle that are not morphologically evident on gray-scale ultrasound. Specifically, supraspinatus tendon partial tears with moderate to severe tendinosis may correspond to biomechanically distinct muscle properties compared to both lower grades of tendon abnormality and full-thickness tears.
Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence
Artificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.
Anteroposterior Radiograph of the Ankle with Cross-Sectional Imaging Correlation
The focus of this article is to illustrate various pathologic entities and variants, heralding disease about the ankle, based on scrutiny of AP radiographs of the ankle, with correlative findings on cross-sectional imaging. Many of these entities can only be detected on the AP ankle radiograph and, if not recognized, may lead to delayed diagnosis and persistent morbidity to the patient. However, a vigilant radiologist, equipped with the knowledge of the characteristic appearance and typical locations of the imaging findings, should be able to make the crucial initial diagnosis and surmise additional findings to be confirmed on cross-sectional imaging.
Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions
OBJECTIVE. The objective of this article is to show how artificial intelligence (AI) has impacted different components of the imaging value chain thus far as well as to describe its potential future uses. CONCLUSION. The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.