Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging.

Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data. Our findings indicate that the ultrasound sensor can track motion in deep-seated organs (bladder) as well as respiratory-related motion. The Time-of-Flight camera offers ease of interpretation and performs well in detecting surface motion (respiration). The Pilot-Tone demonstrates efficacy in tracking bulk respiratory motion and motion of major organs (liver). Simultaneous use of all three sensors could provide complementary motion information outside the MRI bore, providing potential value for motion tracking during position-sensitive treatments such as radiation therapy.

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.

Magnetization-prepared spoiled gradient-echo snapshot imaging for efficient measurement of R2 -R1ρ in knee cartilage.

PURPOSE: To validate the potential of quantifying R2 -R1ρ using one pair of signals with T1ρ preparation and T2 preparation incorporated to magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS) acquisition and to find an optimal preparation time (Tprep ) for in vivo knee MRI. METHODS: Bloch equation simulations were first performed to assess the accuracy of quantifying R2 -R1ρ using T1ρ - and T2 -prepared signals with an equivalent Tprep . For validation of this technique in comparison to the conventional approach that calculates R2 -R1ρ after estimating both T2 and T1ρ , phantom experiments and in vivo validation with five healthy subjects and five osteoarthritis patients were performed at a clinical 3T scanner. RESULTS: Bloch equation simulations demonstrated that the accuracy of this efficient R2 -R1ρ quantification method and the optimal Tprep can be affected by image signal-to-noise ratio (SNR) and tissue relaxation times, but quantification can be closest to the reference with an around 25 ms Tprep for knee cartilage. Phantom experiments demonstrated that the proposed method can depict R2 -R1ρ changes with agarose gel concentration. With in vivo data, significant correlation was observed between cartilage R2 -R1ρ measured from the conventional and the proposed methods, and a Tprep of 25.6 ms provided the most agreement by Bland-Altman analysis. R2 -R1ρ was significantly lower in patients than in healthy subjects for most cartilage compartments. CONCLUSION: As a potential biomarker to indicate cartilage degeneration, R2 -R1ρ can be efficiently measured using one pair of T1ρ -prepared and T2 -prepared signals with an optimal Tprep considering cartilage relaxation times and image SNR.

Principal Component Analysis of Simultaneous PET-MRI Reveals Patterns of Bone-Cartilage Interactions in Osteoarthritis.

BACKGROUND: Bone-cartilage interactions have been implicated in causing osteoarthritis (OA). PURPOSE: To use [18 F]-NaF PET-MRI to 1) develop automatic image processing code in MatLab to create a model of bone-cartilage interactions and 2) find associations of bone-cartilage interactions with known manifestations of OA. STUDY TYPE: Prospective study aimed to evaluate a data analysis method. POPULATION: Twenty-nine patients with knee pain or joint stiffness. FIELD STRENGTH/SEQUENCE: 3T MRI (GE), 3D CUBE FSE, 3D combined T1 ρ/T2 MAPSS, [18F]-sodium fluoride, SIGNA TOF (OSEM). ASSESSMENT: Correlation between MRI (cartilage) and PET (bone) quantitative parameters, bone-cartilage interactions model described by modes of variation as derived by principal component analysis (PCA), WORMS scoring on cartilage lesions, bone marrow abnormalities, subchondral cysts. STATISTICAL TESTS: Linear regression, Pearson correlation. RESULTS: Mode 1 was a positive predictor of the bone abnormality score (P = 0.0003, P = 0.001, P = 0.0007) and the cartilage lesion score (P = 0.03, P = 0.01, P = 0.02) in the femur, tibia, and patella, respectively. For the cartilage lesion scores, mode 5 was the most important positive predictor in the femur (P = 3.9E-06), and mode 2 were predictors, significant negative predictor in the tibia (P = 0.007). In the patella, mode 1 was a significant positive predictor of the bone abnormality score (P = 0.0007). DATA CONCLUSION: By successfully building an automatic code to create a bone-cartilage interface, we were able to observe dynamic relationships between biochemical changes in the cartilage accompanied with bone remodeling, extended to the whole knee joint instead of simple colocalized observations, shedding light on the interactions that occur between bone and cartilage in OA. Evidence Level: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;52:1462-1474.

Computer-Aided Detection AI Reduces Interreader Variability in Grading Hip Abnormalities With MRI.

BACKGROUND: Accurate interpretation of hip MRI is time-intensive and difficult, prone to inter- and intrareviewer variability, and lacks a universally accepted grading scale to evaluate morphological abnormalities. PURPOSE: To 1) develop and evaluate a deep-learning-based model for binary classification of hip osteoarthritis (OA) morphological abnormalities on MR images, and 2) develop an artificial intelligence (AI)-based assist tool to find if using the model predictions improves interreader agreement in hip grading. STUDY TYPE: Retrospective study aimed to evaluate a technical development. POPULATION: A total of 764 MRI volumes (364 patients) obtained from two studies (242 patients from LASEM [FORCe] and 122 patients from UCSF), split into a 65-25-10% train, validation, test set for network training. FIELD STRENGTH/SEQUENCE: 3T MRI, 2D T2 FSE, PD SPAIR. ASSESSMENT: Automatic binary classification of cartilage lesions, bone marrow edema-like lesions, and subchondral cyst-like lesions using the MRNet, interreader agreement before and after using network predictions. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, area under curve (AUC), specificity and sensitivity, and balanced accuracy. RESULTS: For cartilage lesions, bone marrow edema-like lesions and subchondral cyst-like lesions the AUCs were: 0.80 (95% confidence interval [CI] 0.65, 0.95), 0.84 (95% CI 0.67, 1.00), and 0.77 (95% CI 0.66, 0.85), respectively. The sensitivity and specificity of the radiologist for binary classification were: 0.79 (95% CI 0.65, 0.93) and 0.80 (95% CI 0.59, 1.02), 0.40 (95% CI -0.02, 0.83) and 0.72 (95% CI 0.59, 0.86), 0.75 (95% CI 0.45, 1.05) and 0.88 (95% CI 0.77, 0.98). The interreader balanced accuracy increased from 53%, 71% and 56% to 60%, 73% and 68% after using the network predictions and saliency maps. DATA CONCLUSION: We have shown that a deep-learning approach achieved high performance in clinical classification tasks on hip MR images, and that using the predictions from the deep-learning model improved the interreader agreement in all pathologies. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:1163-1172.

FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging.

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

Electron-Phonon Interaction Contribution to the Total Energy of Group IV Semiconductor Polymorphs: Evaluation and Implications.

In density functional theory (DFT)-based total energy studies, the van der Waals (vdW) and zero-point vibrational energy (ZPVE) correction terms are included to obtain energy differences between polymorphs. We propose and compute a new correction term to the total energy, due to electron-phonon interactions (EPI). We rely on Allen's general formalism, which goes beyond the quasi-harmonic approximation (QHA), to include the free energy contributions due to quasiparticle interactions. We show that, for semiconductors and insulators, the EPI contributions to the free energies of electrons and phonons are the corresponding zero-point energy contributions. Using an approximate version of Allen's formalism in combination with the Allen-Heine theory for EPI corrections, we calculate the zero-point EPI corrections to the total energy for cubic and hexagonal polytypes of carbon, silicon and silicon carbide. The EPI corrections alter the energy differences between polytypes. In SiC polytypes, the EPI correction term is more sensitive to crystal structure than the vdW and ZPVE terms and is thus essential in determining their energy differences. It clearly establishes that the cubic SiC-3C is metastable and hexagonal SiC-4H is the stable polytype. Our results are consistent with the experimental results of Kleykamp. Our study enables the inclusion of EPI corrections as a separate term in the free energy expression. This opens the way to go beyond the QHA by including the contribution of EPI on all thermodynamic properties.

FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging.

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.

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.

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.

Automatic hip abductor muscle fat fraction estimation and association with early OA cartilage degeneration biomarkers.

The aim of this study was to develop an automatic segmentation method for hip abductor muscles and find their fat fraction associations with early stage hip osteoarthritis (OA) cartilage degeneration biomarkers. This Institutional Review Board approved, Health Insurance Portability and Accountability Act compliant prospective study recruited 61 patients with evidence of hip OA or Femoroacetabular Impingement (FAI). Magnetic resonance (MR) images were acquired for cartilage segmentation, T1ρ and T2 relaxation times computation and grading of cartilage lesion scores. A 3D V-Net (Dice loss, Adam optimizer, learning rate = 1e-4 , batch size = 3) was trained to segment the three muscles (gluteus medius, gluteus minimus, and tensor fascia latae). The V-Net performance was measured using Dice, distance maps between manual and automatic masks, and Bland-Altman plots of the fat fractions and volumes. Associations between muscle fat fraction and T1ρ , T2 relaxation times values were found using voxel based relaxometry (VBR). A p < 0.05 was considered significant. The V-Net had a Dice of 0.90, 0.88, and 0.91 (GMed, GMin, and TFL). The VBR results found associations of fat fraction of all three muscles in early stage OA and FAI patients with T1ρ , T2 relaxation times. Using an automatic, validated segmentation model, the associations derived between OA biomarkers and muscle fat fractions provide insight into early changes that occur in OA, and show that hip abductor muscle fat is associated with markers of cartilage degeneration.

Longitudinal analysis of the contribution of 3D patella and trochlear bone shape on patellofemoral joint osteoarthritic features.

To explore bone shape features that are associated with patellofemoral joint (PFJ) osteoarthritic features. Thirty subjects with PFJ degeneration (six males, 53.2 ± 9.8 years) and 23 controls (12 males, 48.1 ± 10.6 years) were included. Magnetic resonance (MR) assessment was performed to provide bone segmentation, morpholgocial grading, and cartilage relaxation times. In addition, subject self-reported symptoms were reported. Logistic regressions were used to identify the shape features that were associated with the presence and worsening of PFJ morphological lesions over 3 years, and worsening of self-reported symptoms. Statistical parametric mapping was used to evaluate the associations between shape features and cartilage relaxation times at 3 years. Results indicated that subjects with PFJ degeneration exhibited a trochlea with longer lateral condyle and shallower trochlear groove (adjusted odds ratio [OR] = 0.30; 95% confidence interval [CI]: 0.10, 0.86; P = .025). Subjects with worsening of PFJ degeneration exhibited a patella with equally distributed facets (adjusted OR = 3.14; 95% CI: 1.05, 9.37; P = .040) and lateral bump (adjusted OR = 0.14; 95% CI: 0.02, 0.83; P = .030). No shape features were associated with worsening of self-reported symptoms. Elevated T1ρ and T2 times at 3 years were associated with a patella with a lateral hook, equally distributed facets, round and thick as well as a trochlea larger in size (R = 0.38~0.46, P = .015~.025). The study demonstrated the ability of 3D statistical shape modeling to quantify patella and trochlear bone shape features that are associated with the presence and progression of PFJ osteoarthritic features.

Deep Learning for Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries from MRI.

PURPOSE: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. MATERIALS AND METHODS: In this retrospective study, 1243 knee MR images (1008 intact, 18 partially torn, 77 fully torn, and 140 reconstructed ACLs) from 224 patients (mean age, 47 years ± 14 [standard deviation]; 54% women) were analyzed. The MRI examinations were performed between 2011 and 2014. A modified scoring metric was used. Classification of ACL injuries using deep learning involved use of two types of CNN, one with three-dimensional (3D) and the other with two-dimensional (2D) convolutional kernels. Performance metrics included sensitivity, specificity, weighted Cohen κ, and overall accuracy, and the McNemar test was used to compare the performance of the CNNs. RESULTS: The overall accuracies for ACL injury classification using the 3D CNN and 2D CNN were 89% (225 of 254) and 92% (233 of 254), respectively (P = .27), and both CNNs had a weighted Cohen κ of 0.83. The 2D CNN and 3D CNN performed similarly in classifying intact ACLs (2D CNN, sensitivity of 93% [188 of 203] and specificity of 90% [46 of 51] vs 3D CNN, sensitivity of 89% [180 of 203] and specificity of 88% [45 of 51]). Classification of full tears by both networks was also comparable (2D CNN, sensitivity of 82% [14 of 17] and specificity of 94% [222 of 237] vs 3D CNN, sensitivity of 76% [13 of 17] and specificity of 100% [236 of 237]). The 2D CNN classified all reconstructed ACLs correctly. CONCLUSION: Two-dimensional and 3D CNNs applied to ACL lesion classification had high sensitivity and specificity, suggesting that these networks could be used to help nonexperts grade ACL injuries. Supplemental material is available for this article. © RSNA, 2020.

[18 F]-Sodium Fluoride PET/MR Imaging for Bone-Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study.

This study characterized the distribution of [18 F]-sodium fluoride (NaF) uptake and blood flow in the femur and acetabulum in hip osteoarthritis (OA) patients to find associations between bone remodeling and cartilage composition in the presence of morphological abnormalities using simultaneous positron emission tomography and magnetic resonance imaging (PET/MR), quantitative magnetic resonance imaging (MRI) and femur shape modeling. Ten patients underwent a [18 F]-NaF PET/MR dynamic scan of the hip simultaneously with: (i) fast spin-echo CUBE for morphology grading and (ii) T1ρ /T2 magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots for cartilage, bone segmentation, bone shape modeling, and T1ρ /T2 quantification. The standardized uptake values (SUVs) and Patlak kinetic parameter (Kpat ) were calculated for each patient as PET outcomes, using an automated post-processing pipeline. Shape modeling was performed to extract the variations in bone shapes in the patients. Pearson's correlation coefficients were used to study the associations between bone shapes, PET outcomes, and patient reported pain. Direct associations between quantitative MR and PET evidence of bone remodeling were established in the acetabulum and femur. Associations of shaft thickness with SUV in the femur (p = 0.07) and Kpat in the acetabulum (p = 0.02), cam deformity with acetabular score (p = 0.09), osteophytic growth on the femur head with Kpat (p = 0.01) were observed. Pain had increased correlations with SUV in the acetabulum (p = 0.14) and femur (p = 0.09) when shaft thickness was accounted for. This study demonstrated the ability of [18 F]-NaF PET-MRI, 3D shape modeling, and quantitative MRI to investigate cartilage-bone interactions and bone shape features in hip OA, providing potential investigative tools to diagnose OA. © 2019 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society J Orthop Res 37:2671-2680, 2019.