Multiparametric MRI of Knees in Collegiate Basketball Players: Associations With Morphological Abnormalities and Functional Deficits.

Background: Rates of cartilage degeneration in asymptomatic elite basketball players are significantly higher compared with the general population due to excessive loads on the knee. Compositional quantitative magnetic resonance imaging (qMRI) techniques can identify local biochemical changes of macromolecules observed in cartilage degeneration. Purpose/Hypothesis: The purpose of this study was to utilize multiparametric qMRI to (1) quantify how T1ρ and T2 relaxation times differ based on the presence of anatomic abnormalities and (2) correlate T1ρ and T2 with self-reported functional deficits. It was hypothesized that prolonged relaxation times will be associated with knees with MRI-graded abnormalities and knees belonging to basketball players with greater self-reported functional deficits. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 75 knees from National Collegiate Athletic Association Division I basketball players (40 female, 35 male) were included in this multicenter study. All players completed the Knee injury and Osteoarthritis Outcome Score (KOOS) and had bilateral knee MRI scans taken. T1ρ and T2 were calculated on a voxel-by-voxel basis. The cartilage surfaces were segmented into 6 compartments: lateral femoral condyle, lateral tibia, medial femoral condyle, medial tibia (MT), patella (PAT), and trochlea (TRO). Lesions from the MRI scans were graded for imaging abnormalities, and statistical parametric mapping was performed to study cross-sectional differences based on MRI scan grading of anatomic knee abnormalities. Pearson partial correlations between relaxation times and KOOS subscore values were computed, obtaining r value statistical parametric mappings and P value clusters. Results: Knees without patellar tendinosis displayed significantly higher T1ρ in the PAT compared with those with patellar tendinosis (average percentage difference, 10.4%; P = .02). Significant prolongation of T1ρ was observed in the MT, TRO, and PAT of knees without compared with those with quadriceps tendinosis (average percentage difference, 12.7%, 13.3%, and 13.4%, respectively; P ≤ .05). A weak correlation was found between the KOOS-Symptoms subscale values and T1ρ/T2. Conclusion: Certain tissues that bear the brunt of impact developed tendinosis but spared cartilage degeneration. Whereas participants reported minimal functional deficits, their high-impact activities resulted in structural damage that may lead to osteoarthritis after their collegiate careers.

Large-Scale Analysis of Meniscus Morphology as Risk Factor for Knee Osteoarthritis.

OBJECTIVE: Although it is established that structural damage of the meniscus is linked to knee osteoarthritis (OA) progression, the predisposition to future development of OA because of geometric meniscal shapes is plausible and unexplored. This study aims to identify common variations in meniscal shape and determine their relationships to tissue morphology, OA onset, and longitudinal changes in cartilage thickness. METHODS: A total of 4,790 participants from the Osteoarthritis Initiative data set were studied. A statistical shape model was developed for the meniscus, and shape scores were evaluated between a control group and an OA incidence group. Shape features were then associated with cartilage thickness changes over 8 years to localize the relationship between meniscus shape and cartilage degeneration. RESULTS: Seven shape features between the medial and lateral menisci were identified to be different between knees that remain normal and those that develop OA. These include length-width ratios, horn lengths, root attachment angles, and concavity. These "at-risk" shapes were linked to unique cartilage thickness changes that suggest a relationship between meniscus geometry and decreased tibial coverage and rotational imbalances. Additionally, strong associations were found between meniscal shape and demographic subpopulations, future tibial extrusion, and meniscal and ligamentous tears. CONCLUSION: This automatic method expanded upon known meniscus characteristics that are associated with the onset of OA and discovered novel shape features that have yet to be investigated in the context of OA risk.

Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based Image Reconstruction at 3T.

OBJECTIVES: To evaluate whether combining fast acquisitions with deep-learning reconstruction can provide diagnostically useful images and quantitative assessment comparable to standard-of-care acquisitions for lumbar spine magnetic resonance imaging (MRI). METHODS: Eighteen patients were imaged with both standard protocol and fast protocol using reduced signal averages, each protocol including sagittal fat-suppressed T2-weighted, sagittal T1-weighted, and axial T2-weighted 2D fast spin-echo sequences. Fast-acquisition data was additionally reconstructed using vendor-supplied deep-learning reconstruction with three different noise reduction factors. For qualitative analysis, standard images as well as fast images with and without deep-learning reconstruction were graded by three radiologists on five different categories. For quantitative analysis, convolutional neural networks were applied to sagittal T1-weighted images to segment intervertebral discs and vertebral bodies, and disc heights and vertebral body volumes were derived. RESULTS: Based on noninferiority testing on qualitative scores, fast images without deep-learning reconstruction were inferior to standard images for most categories. However, deep-learning reconstruction improved the average scores, and noninferiority was observed over 24 out of 45 comparisons (all with sagittal T2-weighted images while 4/5 comparisons with sagittal T1-weighted and axial T2-weighted images). Interobserver variability increased with 50 and 75% noise reduction factors. Deep-learning reconstructed fast images with 50% and 75% noise reduction factors had comparable disc heights and vertebral body volumes to standard images (r2≥ 0.86 for disc heights and r2≥ 0.98 for vertebral body volumes). CONCLUSIONS: This study demonstrated that deep-learning-reconstructed fast-acquisition images have the potential to provide noninferior image quality and comparable quantitative assessment to standard clinical images.

K2S Challenge: From Undersampled K-Space to Automatic Segmentation.

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.

Deep Learning for Multi-Tissue Segmentation and Fully Automatic Personalized Biomechanical Models from BACPAC Clinical Lumbar Spine MRI.

STUDY DESIGN: In vivo retrospective study of fully automatic quantitative imaging feature extraction from clinically acquired lumbar spine magnetic resonance imaging (MRI). OBJECTIVE: To demonstrate the feasibility of substituting automatic for human-demarcated segmentation of major anatomic structures in clinical lumbar spine MRI to generate quantitative image-based features and biomechanical models. SETTING: Previous studies have demonstrated the viability of automatic segmentation applied to medical images; however, the feasibility of these networks to segment clinically acquired images has not yet been demonstrated, as they largely rely on specialized sequences or strict quality of imaging data to achieve good performance. METHODS: Convolutional neural networks were trained to demarcate vertebral bodies, intervertebral disc, and paraspinous muscles from sagittal and axial T1-weighted MRIs. Intervertebral disc height, muscle cross-sectional area, and subject-specific musculoskeletal models of tissue loading in the lumbar spine were then computed from these segmentations and compared against those computed from human-demarcated masks. RESULTS: Segmentation masks, as well as the morphological metrics and biomechanical models computed from those masks, were highly similar between human- and computer-generated methods. Segmentations were similar, with Dice similarity coefficients of 0.77 or greater across networks, and morphological metrics and biomechanical models were similar, with Pearson R correlation coefficients of 0.69 or greater when significant. CONCLUSIONS: This study demonstrates the feasibility of substituting computer-generated for human-generated segmentations of major anatomic structures in lumbar spine MRI to compute quantitative image-based morphological metrics and subject-specific musculoskeletal models of tissue loading quickly, efficiently, and at scale without interrupting routine clinical care.

Automatic detection and voxel-wise mapping of lumbar spine Modic changes with deep learning.

Background: Modic changes (MCs) are the most prevalent classification system for describing magnetic resonance imaging (MRI) signal intensity changes in the vertebrae. However, there is a growing need for novel quantitative and standardized methods of characterizing these anomalies, particularly for lesions of transitional or mixed nature, due to the lack of conclusive evidence of their associations with low back pain. This retrospective imaging study aims to develop an interpretable deep learning-based detection tool for voxel-wise mapping of MCs. Methods: Seventy-five lumbar spine MRI exams that presented with acute-to-chronic low back pain, radiculopathy, and other symptoms of the lumbar spine were enrolled. The pipeline consists of two deep convolutional neural networks to generate an interpretable voxel-wise Modic map. First, an autoencoder was trained to segment vertebral bodies from T1-weighted sagittal lumbar spine images. Next, two radiologists segmented and labeled MCs from a combined T1- and T2-weighted assessment to serve as ground truth for training a second autoencoder that performs segmentation of MCs. The voxels in the detected regions were then categorized to the appropriate Modic type using a rule-based signal intensity algorithm. Post hoc, three radiologists independently graded a second dataset with the aid of the model predictions in an artificial (AI)-assisted experiment. Results: The model successfully identified the presence of changes in 85.7% of samples in the unseen test set with a sensitivity of 0.71 (±0.072), specificity of 0.95 (±0.022), and Cohen's kappa score of 0.63. In the AI-assisted experiment, the agreement between the junior radiologist and the senior neuroradiologist significantly improved from Cohen's kappa score of 0.52 to 0.58 (p < 0.05). Conclusions: This deep learning-based approach demonstrates substantial agreement with radiologists and may serve as a tool to improve inter-rater reliability in the assessment of MCs.

Multiparametric MRI characterization of knee articular cartilage and subchondral bone shape in collegiate basketball players.

Magnetic resonance imaging (MRI) is commonly used to evaluate the morphology of the knee in athletes with high-knee impact; however, complex repeated loading of the joint can lead to biochemical and structural degeneration that occurs before visible morphological changes. In this study, we utilized multiparametric quantitative MRI to compare morphology and composition of articular cartilage and subchondral bone shape between young athletes with high-knee impact (basketball players; n = 40) and non-knee impact (swimmers; n = 25). We implemented voxel-based relaxometry to register all cases to a single reference space and performed a localized compositional analysis of T 1ρ - and T 2 -relaxation times on a voxel-by-voxel basis. Additionally, statistical shape modeling was employed to extract differences in subchondral bone shape between the two groups. Evaluation of cartilage composition demonstrated a significant prolongation of relaxation times in the medial femoral and tibial compartments and in the posterolateral femur of basketball players in comparison to relaxation times in the same cartilage compartments of swimmers. The compositional analysis also showed depth-dependent differences with prolongation of the superficial layer in basketball players. For subchondral bone shape, three total modes were found to be significantly different between groups and related to the relative sizes of the tibial plateaus, intercondylar eminences, and the curvature and concavity of the patellar lateral facet. In summary, this study identified several characteristics associated with a high-knee impact which may expand our understanding of local degenerative patterns in this population.

Targeting iron metabolism in high-grade glioma with 68Ga-citrate PET/MR.

Noninvasive tools that target tumor cells could improve the management of glioma. Cancer generally has a high demand for Fe(III), an essential nutrient for a variety of biochemical processes. We tested whether 68Ga-citrate, an Fe(III) biomimetic that binds to apo-transferrin in blood, detects glioma in preclinical models and patients using hybrid PET/MRI. Mouse PET/CT studies showed that 68Ga-citrate accumulates in subcutaneous U87MG xenografts in a transferrin receptor-dependent fashion within 4 hours after injection. Seventeen patients with WHO grade III or IV glioma received 3.7-10.2 mCi 68Ga-citrate and were imaged with PET/MR 123-307 minutes after injection to establish that the radiotracer can localize to human tumors. Multiple contrast-enhancing lesions were PET avid, and tumor to adjacent normal white matter ratios were consistently greater than 10:1. Several contrast-enhancing lesions were not PET avid. One minimally enhancing lesion and another tumor with significantly reduced enhancement following bevacizumab therapy were PET avid. Advanced MR imaging analysis of one patient with contrast-enhancing glioblastoma showed that metabolic hallmarks of viable tumor spatially overlaid with 68Ga-citrate accumulation. These early data underscore that high-grade glioma may be detectable with a radiotracer that targets Fe(III) transport.

Imaging Hepatocellular Carcinoma With 68Ga-Citrate PET: First Clinical Experience.

While cross-sectional imaging with computed tomography (CT) and magnetic resonance imaging is the primary method for diagnosing hepatocellular carcinoma (HCC), they provide little biological insight into this molecularly heterogeneous disease. Nuclear imaging tools that can detect molecular subsets of tumors could greatly improve diagnosis and management of HCC. To this end, we conducted a patient study to determine whether HCC can be resolved using 68Ga-citrate positron emission tomography (PET). One patient with recurrent HCC was injected with 300 MBq of 68Ga-citrate and imaged with PET/CT 249 minutes post injection. Four (28%) of 14 hepatic lesions were avid for 68Ga-citrate. One extrahepatic lesion was not PET avid. The average maximum standardized uptake value (SUVmax) for the lesions was 7.2 (range: 6.2-8.4), while the SUVmax of the normal liver parenchyma was 4.7 and blood pool was 5.7. The avid lesions were not significantly larger than the quiescent lesions, and a prior contrast CT showed uniform enhancement among the lesions, suggesting that tumor signals are due to specific binding of the radiotracer to the transferrin receptor, rather than enhanced vascularity in the tumor microenvironment. Further studies are required in a larger patient cohort to verify the molecular basis of radiotracer uptake and the clinical utility of this tool.

A PET Imaging Strategy to Visualize Activated T Cells in Acute Graft-versus-Host Disease Elicited by Allogenic Hematopoietic Cell Transplant.

A major barrier to successful use of allogeneic hematopoietic cell transplantation is acute graft-versus-host disease (aGVHD), a devastating condition that arises when donor T cells attack host tissues. With current technologies, aGVHD diagnosis is typically made after end-organ injury and often requires invasive tests and tissue biopsies. This affects patient prognosis as treatments are dramatically less effective at late disease stages. Here, we show that a novel PET radiotracer, 2'-deoxy-2'-[18F]fluoro-9-ß-D-arabinofuranosylguanine ([18F]F-AraG), targeted toward two salvage kinase pathways preferentially accumulates in activated primary T cells. [18F]F-AraG PET imaging of a murine aGVHD model enabled visualization of secondary lymphoid organs harboring activated donor T cells prior to clinical symptoms. Tracer biodistribution in healthy humans showed favorable kinetics. This new PET strategy has great potential for early aGVHD diagnosis, enabling timely treatments and improved patient outcomes. [18F]F-AraG may be useful for imaging activated T cells in various biomedical applications. Cancer Res; 77(11); 2893-902. ©2017 AACR.

Real-Time Transferrin-Based PET Detects MYC-Positive Prostate Cancer.

Noninvasive biomarkers that detect the activity of important oncogenic drivers could significantly improve cancer diagnosis and management of treatment. The goal of this study was to determine whether 68Ga-citrate (which avidly binds to circulating transferrin) can detect MYC-positive prostate cancer tumors, as the transferrin receptor is a direct MYC target gene. PET imaging paired with 68Ga-citrate and molecular analysis of preclinical models, human cell-free DNA (cfDNA), and clinical biopsies were conducted to determine whether 68Ga-citrate can detect MYC-positive prostate cancer. Importantly, 68Ga-citrate detected human prostate cancer models in a MYC-dependent fashion. In patients with castration-resistant prostate cancer, analysis of cfDNA revealed that all patients with 68Ga-citrate avid tumors had a gain of at least one MYC copy number. Moreover, biopsy of two PET avid metastases showed molecular or histologic features characteristic of MYC hyperactivity. These data demonstrate that 68Ga-citrate targets prostate cancer tumors with MYC hyperactivity. A larger prospective study is ongoing to demonstrate the specificity of 68Ga-citrate for tumors with hyperactive MYC.Implications: Noninvasive measurement of MYC activity with quantitative imaging modalities could substantially increase our understanding of the role of MYC signaling in clinical settings for which invasive techniques are challenging to implement or do not characterize the biology of all tumors in a patient. Moreover, measuring MYC activity noninvasively opens the opportunity to study changes in MYC signaling in patients under targeted therapeutic conditions thought to indirectly inhibit MYC. Mol Cancer Res; 15(9); 1221-9. ©2017 AACR.

A Feasibility Study Showing [68Ga]Citrate PET Detects Prostate Cancer.

PURPOSE: The management of advanced or recurrent prostate cancer is limited in part by the lack of effective imaging agents. Metabolic changes in prostate cancer have previously been exploited for imaging, culminating in the recent US FDA approval of [11C]choline for the detection of subclinical recurrent disease after definitive local therapy. Despite this milestone, production of [11C]choline requires an on-site cyclotron, limiting the scope of medical centers at which this scan can be offered. In this pilot study, we tested whether prostate cancer could be imaged with positron emission tomography (PET) using [68Ga]citrate, a radiotracer that targets iron metabolism but is produced without a cyclotron. PROCEDURES: Eight patients with castrate-resistant prostate cancer were enrolled in this single-center feasibility study. All patients had evidence of metastatic disease by standard of care imaging [X-ray computed tomography (CT), bone scan, or magnetic resonance imaging (MRI)] prior to PET with [68Ga]citrate. Patients were intravenously injected with increasing doses of [68Ga]citrate (136.9 to a maximum of 259 MBq). Uptake time was steadily increased from 1 h to approximately 3.5 h for the final 4 patients, and all patients were imaged with a PET/MRI. Qualitative and semi-quantitative (maximum standardized uptake value (SUVmax)) assessment of the metastatic lesions was performed and compared to the standard of care imaging. RESULTS: At 1- and 2-h imaging times post injection, there were no detectable lesions with [68Ga]citrate PET. At 3- to 4-h uptake time, there were a total of 71 [68Ga]citrate-positive lesions (67 osseous, 1 liver, and 3 lymph node). Of these, 65 lesions were visible on the standard of care imaging (CT and/or bone scan). One PET-avid osseous vertebral body metastasis was not apparent on either CT or bone scan. Twenty-five lesions were not PET-avid but seen on CT and bone scan (17 bone, 6 lymph node, 1 pleural, and 1 liver). The average of the maximum SUVs for bone or soft tissue metastases for patients treated at higher doses and uptake time was statistically higher than the corresponding parameter in normal liver, muscle, and bone. Visually obvious blood pool activity was observed even 3-4 h post injection, suggesting that further optimization of the [68Ga]citrate imaging protocol is required to maximize signal-to-background ratios. CONCLUSIONS: Our preliminary results support that PET with [68Ga]citrate may be a novel tool for imaging prostate cancer. Future studies are needed to determine the optimal imaging protocol, the clinical significance of [68Ga]citrate uptake, and its role in therapeutic decisions.