Hollis G Potter’s research while affiliated with Hospital for Special Surgery and other places

This study applied radiomics to MRI data for automated classification of soft tissue abnormalities near total hip arthroplasty (THA). A total of 126 subjects with 1.5 T MRI of symptomatic THA were included in the analysis. Peri‐prosthetic soft tissue regions of interest were manually segmented and classified by an expert radiologist. An established radiomics library was used to extract 96 features from 2D image patches across segmented regions. Logistic regression was employed as the primary radiomic classifier, achieving an average area under curve (AUC) of 0.71 in differentiating tissue classifications spanning normal, infected, and several inflammatory, noninfectious categories. Notably, infection cases were identified with the highest accuracy, attaining an AUC of 0.79. Statement of Clinical Significance: This study demonstrates that radiomics applied to MRI data can effectively automate the classification of soft tissue abnormalities in symptomatic total hip arthroplasty, particularly in differentiating periprosthetic infections.

Multiligament knee injuries (MLKIs) represent a broad spectrum of pathology with potentially devastating consequences. Currently, disagreement in the terminology, diagnosis and treatment of these injuries limits clinical care and research. This study aimed to develop consensus on the nomenclature, diagnosis, treatment and rehabilitation strategies for patients with MLKI, while identifying important research priorities for further study. An international consensus process was conducted using validated Delphi methodology in line with British Journal of Sports Medicine guidelines. A multidisciplinary panel of 39 members from 14 countries, completed 3 rounds of online surveys exploring aspects of nomenclature, diagnosis, treatment, rehabilitation and future research priorities. Levels of agreement (LoA) with each statement were rated anonymously on a 5-point Likert scale, with experts encouraged to suggest modifications or additional statements. LoA for consensus in the final round were defined ‘a priori’ if >75% of respondents agreed and fewer than 10% disagreed, and dissenting viewpoints were recorded and discussed. After three Delphi rounds, 50 items (92.6%) reached consensus. Key statements that reached consensus within nomenclature included a clear definition for MLKI (LoA 97.4%) and the need for an updated MLKI classification system that classifies injury mechanism, extent of non-ligamentous structures injured and the presence or absence of dislocation. Within diagnosis, consensus was reached that there should be a low threshold for assessment with CT angiography for MLKI within a high-energy context and for certain injury patterns including bicruciate and PLC injuries (LoA 89.7%). The value of stress radiography or intraoperative fluoroscopy also reached consensus (LoA 89.7%). Within treatment, it was generally agreed that existing literature generally favours operative management of MLKI, particularly for young patients (LoA 100%), and that single-stage surgery should be performed whenever possible (LoA 92.3%). This consensus statement will facilitate clinical communication in MLKI, the care of these patients and future research within MLKI.

The objective of the study was to evaluate tibial cartilage thickness (TCT), T1ρ and T2 values within both loaded and baseline configurations in a cadaveric knee model using a 3D bone based tibial coordinate system. Ten intact cadaveric knees were mounted into an magnetic resonance imaging (MRI) compatible loading device. Morphologic and quantitative MRI (qMRI) images were acquired with the knee in a baseline configuration and after application of 50% body weight. The morphologic images were evaluated for cartilage degeneration using a modified Noyes scoring system. A 3D bone‐based tibial coordinate system was utilized to evaluate regional changes of tibial T1ρ, T2, and cartilage thickness values among regions covered and uncovered by the meniscus. Inter‐regional differences in medial and lateral MRI outcomes were found between loaded and baseline configurations. Cartilage regions covered by the meniscus demonstrated disparate qMRI and TCT results as compared to cartilage regions not covered by the meniscus. The regions covered by meniscus experienced a ~3.5%, ~0.5%, and ~5.5% reduction of T1ρ ( p < 0.05, medial and lateral compartments), T2 and TCT, respectively, in both compartments while regions not covered by the meniscus experienced larger reductions of ~10%, ~2%, and ~10.5% reduction of T1ρ ( p < 0.05, medial and lateral compartments), T2 and TCT ( p < 0.05, lateral compartment only), respectively, in both compartments. T1ρ and T2 decreases following application of 50% body weight load were substantially larger in the tibial regions with modified Noyes grade 3 ( n = 2) compared to either healthy regions ( n = 85, p < 0.0.003) or regions with modified Noyes grade 2 ( n = 13, p < 0.004). Interregional differences in MRI outcomes reflect variations in structure and function, and largely followed a pattern in cartilage regions that were covered or not covered by the meniscus. Results of the current study suggest that ΔT1ρ and ΔT2 values may be sensitive to superficial fissuring, more than baseline or loaded T1ρ or T2 values, or TCT alone, however future studies with additional specimens, with greater variability in OA grade distribution, may further emphasize the current findings.

Historically, pedicle screw accuracy measurements have relied on CT and expert visual assessment of the position of pedicle screws relative to preoperative plans. Proper pedicle screw placement is necessary to avoid complications, cost and morbidity of revision procedures. The aim of this study was to determine accuracy and precision of pedicle screw insertion via a novel computer vision algorithm using preoperative and postoperative computed tomography (CT) scans. Three cadaveric specimens were utilized. Screw placement planning on preoperative CT was performed according to standard clinical practice. Two experienced surgeons performed bilateral T2–L4 instrumentation using robotic-assisted navigation. Postoperative CT scans of the instrumented levels were obtained. Automated segmentation and computer vision techniques were employed to align each preoperative vertebra with its postoperative counterpart and then compare screw positions along all three axes. Registration accuracy was assessed by preoperatively embedding spherical markers (tantalum beads) to measure discrepancies in landmark alignment. Eighty-eight pedicle screws were placed in 3 cadavers’ spines. Automated registrations between pre- and postoperative CT achieved sub-voxel accuracy. For the screw tip and tail, the mean three-dimensional errors were 1.67 mm and 1.78 mm, respectively. Mean angular deviation of screw axes from plan was 1.58°. For screw mid-pedicular accuracy, mean absolute error in the medial–lateral and superior–inferior directions were 0.75 mm and 0.60 mm, respectively. This study introduces automated algorithms for determining accuracy and precision of planned pedicle screws. Our accuracy outcomes are comparable or superior to recent robotic-assisted in vivo and cadaver studies. This computerized workflow establishes a standardized protocol for assessing pedicle screw placement accuracy and precision and provides detailed 3D translational and angular accuracy and precision for baseline comparison.

A growing application of ultrashort echo time (UTE) magnetic resonance imaging (MRI) is the use of zero echo time (ZTE) MRI for the assessment of osseous injury and morphometry. Owing to the tissue’s short transverse relaxation time, bone is inadequately rendered and often indistinguishable from adjacent short-T2 tissues (e.g., ligament and tendon) in conventional magnetic resonance (MR) pulse sequences. ZTE-MRI enables visualization of osseous/calcified tissues by acquiring signals immediately after application of the radiofrequency (RF) pulse, resulting in images that render tissues with long transverse relaxation times hyperintense. Appropriate post-processing techniques are employed to then invert the contrast of the images to yield tissue contrast similar to computed tomography (CT), i.e., “bright” bone with intermediate-intensity soft tissues. This chapter will cover the clinical motivations for utilizing ZTE for bone imaging, the general workflow of acquisition and post-processing, considerations for acquisition and prescription of the sequence, select examples of orthopedic applications, and limitations of ZTE-MRI.