Faculty Bibliography

RATIONALE AND OBJECTIVES/OBJECTIVE:To determine the impact of COVID-19 workflow changes on patient throughput at the outpatient imaging facilities of a large healthcare system in New York City. MATERIALS AND METHODS/METHODS:COVID-19 workflow changes to permit social distancing and patient and staff safety included screening at the time of scheduling, encouraging patients to use our digital platform to complete registration/safety forms prior to appointments, stationing screeners at all entrances, limiting waiting room capacity, implementing a texting system to notify patients of delays, limiting dressing room use by encouraging patients to wear exam-appropriate clothing, and accelerating MRI protocols without reducing image quality. We assessed patients' pre-exam wait times, MR exam times, overall time spent on site, and registration for and use of the digital portal before (February 2020) and after (June 2020) implementation of these measures. RESULTS:Across 17 outpatient imaging centers, workflow changes resulted in a 23.1% reduction (-6.8 minutes) in all patients' pre-exam wait times (p <0.00001). Pre-exam wait times for MRI, CT, ultrasound, x-ray, and mammography decreased 28.4% (-10.3 minutes), 16.5% (-6.7 minutes), 25.3% (-7.7 minutes), 22.8% (-3.7 minutes), and 23.9% (-5.0 minutes), respectively (p < 0.00001 for all). MR exam times decreased 9.7% (-3.5 minutes) and patients' overall time on site decreased 15.2% (-8.0 minutes). The proportions of patients actively using the digital patient portal (56.1%-70.1%) and completing forms electronically prior to arrival (24.9%-47.1%) increased (p < 0.0001 for both). CONCLUSION/CONCLUSIONS:Workflow changes necessitated by the COVID-19 pandemic to ensure safety of patients and staff have permitted higher outpatient throughput.

Twenty men with spinal cord injury (SCI) were randomized into two 16-week intervention groups receiving testosterone treatment (TT) or TT combined with resistance training (TT + RT). TT + RT appears to hold the potential to reverse or slow down bone loss following SCI if provided over a longer period.

Regulatory approval of ultrahigh field (UHF) MR imaging scanners for clinical use has opened new opportunities for musculoskeletal imaging applications. UHF MR imaging has unique advantages in terms of signal-to-noise ratio, contrast-to-noise ratio, spectral resolution, and multinuclear applications, thus providing unique information not available at lower field strengths. But UHF also comes with a set of technical challenges that are yet to be resolved and may not be suitable for all imaging applications. This review focuses on the latest research in musculoskeletal MR imaging applications at UHF including morphologic imaging, T₂, T₂∗, and T_1ρ mapping, chemical exchange saturation transfer, sodium imaging, and phosphorus spectroscopy imaging applications.

OBJECTIVE:field on metal implant-induced artifacts of titanium (Ti) and cobalt-chromium (CoCr) hip arthroplasty implants at 1.5-T and 3.0-T field strengths. MATERIAL AND METHODS/METHODS:field as the system default, as well as 3.0-T, which permitted CP and EP. Manual segmentation quantified the size of the metal artifacts at the level of the acetabular cup, femoral neck, and femoral shaft. RESULTS:In the acetabular cup and femoral neck, 1.5-T CP achieved smaller artifact sizes than 3.0-T CP (28-29% on HBW-TSE, p = 0.002-0.005; 17-34% on SEMAC, p = 0.019-0.102) and 3.0-T EP (25-28% on HBW-TSE, p = 0.010-0.011; 14-36% on SEMAC, p = 0.058-0.135) techniques. In the femoral stem region, 3.0-T EP achieved more efficient artifact suppression than 3.0-T CP (HBW-TSE 44-45%, p < 0.001-0.022; SEMAC 76-104%, p < 0.001-0.022) and 1.5-T CP (HBW-TSE 76-96%, p < 0.001-0.003; SEMAC 138-173%, p = 0.003-0.005) techniques. CONCLUSION/CONCLUSIONS:Despite slightly superior metal reduction ability of the 1.5-T in the region of the acetabular cup and prosthesis neck, 3.0-T MRI of hip arthroplasty implants using elliptically polarized RF pulses may overall be more effective in reducing metal artifacts than the current standard 1.5-T MRI techniques, which by default implements circularly polarized RF pulses.

Purpose: Osteoporosis (OP) is a disease of weak bone associated with increased fracture risk (Fx). An important component of bone tissue is bone marrow adipose tissue (BAT), which has been previously associated with Fx and OP. Recent studies have shown an association between BMD and fat quantity in the spine and femur using Chemical Shift Encoded MRI (CSE-MRI). The aim of our study was to apply CSE-MRI in thigh muscle (MUS), BAT, and subcutaneous fat (SAT) of the pelvic region in osteoporosis patients with varying degrees of Fx.
Material(s) and Method(s): This study had institutional review board approval and written informed consent was obtained from all n=128 recruited female subjects with OP. Patients were divided into three groups for analysis based upon overall FRAX score: low (LOW, FRAX < 10, n=42, 57+/-6.9y, BMI 23+/-4.1 kg/m2), moderate (MOD, 10>FRAX>20, n=52, 62+/-6.9y, BMI 22+/-3.4 kg/m2) and high (HIGH, FRAX>20, n=34, 64+/-5.8y, BMI 22+/-3.1 kg/m2). 3T MRI acquisition were performed a 3T using a 3D spoiled gradient-echo sequence. An automatic reconstruction pipeline allowed computation of proton density fat fraction (PDFF), susceptibility mapping (QSM) and R2*. BAT, MUS and SAT were segmented by thresholding the PDFF map. An unpaired one-way ANOVA test was used to assess significant differences.
Result(s): Overall, in BAT, we found a higher amount of PDFF in HIGH subjects compared to LOW subjects (+5%, p= 0.032). In muscle, we found a higher amount of PDFF in HIGH compared to both LOW (+8.87%, p =0.008) and MOD subjects (+9.25%, p= 0.006). There were no differences between groups with regards to R2*measured. We found diamagnetic BAT and MUS and paramagnetic SAT. Susceptibility of SAT was higher in LOW compared to both HIGH (-31%, p= 0.008) and MOD (-23%, p= 0.04) subjects. Volume of MUS was lower in MOD compared to LOW (-8%, p=0.009) and HIGH (-9%, p=0.045).
Conclusion(s): Our result suggests that fracture risk is related to an increased amount of adipose tissue. 3T CSE-MRI could be used in the future to study the relationship between adipose tissue and bone health and possibly even provide an additional surrogate marker of Fx beyond BMD

OBJECTIVE:Predictive biomarkers of progression in knee osteoarthritis are sought to enable clinical trials of structure-modifying drugs. A peripheral blood leukocyte (PBL) inflammatory gene signature, MRI-based bone marrow lesions (BML) and meniscus extrusion scores, meniscal lesions, and osteophytes on X-ray each have been shown separately to predict radiographic joint space narrowing (JSN) in subjects with symptomatic knee osteoarthritis (SKOA). In these studies, we determined whether the combination of the PBL inflammatory gene expression and these imaging findings at baseline enhanced the prognostic value of either alone. METHODS:PBL inflammatory gene expression (increased mRNA for IL-1β, TNFα, and COX-2), routine radiographs, and 3T knee MRI were assessed in two independent populations with SKOA: an NYU cohort and the Osteoarthritis Initiative (OAI). At baseline and 24 months, subjects underwent standardized fixed-flexion knee radiographs and knee MRI. Medial JSN (mJSN) was determined as the change in medial JSW. Progressors were defined by an mJSN cut-point (≥ 0.5 mm/24 months). Models were evaluated by odds ratios (OR) and area under the receiver operating characteristic curve (AUC). RESULTS:We validated our prior finding in these two independent (NYU and OAI) cohorts, individually and combined, that an inflammatory PBL inflammatory gene expression predicted radiographic progression of SKOA after adjustment for age, sex, and BMI. Similarly, the presence of baseline BML and meniscal lesions by MRI or semiquantitative osteophyte score on X-ray each predicted radiographic medial JSN at 24 months. The combination of the PBL inflammatory gene expression and medial BML increased the AUC from 0.66 (p = 0.004) to 0.75 (p < 0.0001) and the odds ratio from 6.31 to 19.10 (p < 0.0001) in the combined cohort of 473 subjects. The addition of osteophyte score to BML and PBL inflammatory gene expression further increased the predictive value of any single biomarker. A causal analysis demonstrated that the PBL inflammatory gene expression and BML independently influenced mJSN. CONCLUSION/CONCLUSIONS:The use of the PBL inflammatory gene expression together with imaging biomarkers as combinatorial predictive biomarkers, markedly enhances the identification of radiographic progressors. The identification of the SKOA population at risk for progression will help in the future design of disease-modifying OA drug trials and personalized medicine strategies.

Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). Conclusion The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Richardson in this issue.