Keywords: Bone, Bone

Motivation: In preoperative cervical spine assessment, precise visualization of the vertebral artery (VA) course is of paramount importance. We present a new sequence called FRACTURE-Angiography.

Goal(s): The purpose is to evaluate the clinical applicability of FRACTURE-Angiography compared to FRACTURE and TOF-MRA.

Approach: Both quantitative and qualitative evaluations were performed. In the quantitative evaluations, we assessed the depiction ability of the VA and bone tissue based on the contrast ratio with background tissue. The qualitative evaluations for 3D-fusion images were conducted from the perspectives of bone morphology and VA course. 

Results: FRACTURE-Angiography could image arterial signals and bone morphology simultaneously and satisfy clinical requirements.

Impact: FRACTURE-Angiography allows simultaneous acquisition of bone tissue and arterial images in a single imaging session. As a result, it has the advantage of shortening scan time and minimizing the gap between scans.

Keywords: Whole Joint, New Devices

Motivation: Traction imaging helps visualize the wrist articular cartilage, intrinsic ligaments, and triangular fibrocartilaginous complex (TFCC). The current traction method is cumbersome to use limiting more widespread usage.

Goal(s): To design a direct wrist traction device that is practical, comfortable, effective, and versatile.

Approach: A new traction device was designed. One patient underwent wrist imaging before and after traction with this device. The degree of joint distraction and articular cartilage, ligament and TFCC visibility was evaluated as was the degree of subject discomfort.

Results: The traction device was easy to apply, achieved good traction with minimal subject discomfort.

Impact: This practical traction device can potentially improve the quality of wrist MR imaging, negating the need for wrist arthrography and improving the detection of articular cartilage, intrinsic ligament and TFCC injury. 

Keywords: Bone, MSK, tumor; MRI; APT

Motivation: Current imaging methods for detecting tumor protein content are immature, thereby limiting early diagnosis, qualitative assessment, and post-treatment evaluation. 

Goal(s): Magnetic resonance imaging (MRI) has contributed significantly to improvement in the management of bone and soft tissue tumors. However, the use of MRI based amide proton transfer (APT) technique to diagnose bone and soft tissue tumors remain less explored. 

Approach: We use APT imaging to distinguish benign, intermediate and malignant bone and soft tissue tumors.

Results: APT value was different in benign, intermediate and malignant tumors. There is high sensitivity and specificity for the qualitative diagnosis of bone and soft tissue tumors.

Impact: APT imaging can provide diagnostic basis for the qualitative diagnosis of bone and soft tissue tumors. However, this imaging method is interfered by many factors. Whether tumors of different tissue sources have different APT characteristics needs further study.

Keywords: Bone, Tumor

Motivation: Clinical applications of APTWI for differentiating benign and malignant bone and soft tissue tumors are scarce.

Goal(s): This work investigated and evaluated the ability of APT and DWI parameters in distinguishing benign from malignant bone and soft tissue tumors.

Approach: MTRasym and ADC values of ninety-six patients in benign and malignant lesions were compared using either the independent samples t-test or Mann-Whitney U test. The ROC curve was used to assess the diagnostic performance in differentiation between benign and malignant tumors. 

Results: APTWI combined with DWI showed a significantly improved differentiation between benign and malignant tumors.

Impact: The combination of APTWI and DWI offers valuable insights into changes in cellular proteins and peptides. When MTRasym_max is combined with ADC_min, it yields superior diagnostic performance for distinguishing between malignant and benign bone and soft tissue tumors.

Keywords: Bone, Diabetes, critical limb ischemia; endothelial progenitor cells; dynamic contrast enhanced magnetic resonance imaging; metabolomics

Motivation: Diabetic lipid metabolism induces impairments of bone marrow endothelial progenitor cells (EPCs) ¹, accelerates microangiopathy of critical limb ischemia ^2,3 and increases amputation rate ⁴.

Goal(s): To explore the effect of diabetes mellitus (DM) on EPCs function and fatty acid metabolism in rabbits with critical limb ischemia using DCE-MRI.

Approach: DISCO-acquired DCE-MRI, EPCs function assessment, fatty acid metabolomics and CD31 immunohistochemical staining of proximal femur were performed.

Results: DM increased the significant differences of microvascular permeability parameters among time points and those were correlated with mobilization, migration and angiogenesis of EPCs, and fatty acid anabolism indices in both experimental groups at week 4.

Impact: Early non-invasive evaluating bone marrow endothelial progenitor cell functions in diabetic rabbits with critical limb ischemia could monitor functions of bone marrow EPCs during lipid regulation using DISCO MRI and timely adjust treatment strategies to improve microcirculation and reduce amputation.

Keywords: Bone, Tumor, Neoadjuvant chemotherapy · Response prediction

Motivation: The efficacy of neoadjuvant chemotherapy (NAC) directly affects the clinical treatment of osteosarcoma (OS) patients. Consequently, it is essential to accurately assess the effectiveness of NAC.

Goal(s): To develop an automated method for accurately segmenting tumors and predicting the response to NAC in OS patients from conventional sequences of preoperative MRI.

Approach: In the present study, we accomplished two tasks. One involves constructing a deep learning model for automatic tumor segmentation, while the other entails predicting the response to NAC using different feature extraction methods in OS patients.

Results: Radiomics models can serve as a non-invasive tool for predicting treatment response in OS.

Impact: Radiomics have the potential to non-invasively predict the neoadjuvant chemotherapeutic responses. This tool could significantly contribute to avoiding ineffective chemotherapy and optimizing the management of OS patients in the era of personalized medicine.

Keywords: Bone, Skeletal

Motivation: Treatment options for spinal tumors are challenging.

Goal(s): To predict the progression-free survival (PFS) of aggressive spinal tumors based on MRI combined with clinical features to aid early personalized treatment decisions.

Approach: Clinical, pathological and imaging data of 211 patients underwent spinal tumor surgery at two research centers were retrospectively analyzed. Tumoral and peritumoral features were extracted from T1WI and T2WI images. Clinical-radiomics nomogram was developed by radiomics signatures and the predictive clinical parameters.

Results: The integrative model showed best performance in PFS evaluation for the prediction of postoperative PFS, with the AUC was 0.86 and 0.81 in the training and test cohort. 

Impact: Rad-score is an independent factor for PFS after resection for spinal tumor. The nomogram established in this study could be an effective tool for the clinical prediction of PFS after surgery.

Keywords: Other Musculoskeletal, Tumor, CEST

Motivation: The precise assessment of the efficacy of preoperative neoadjuvant chemotherapy in osteosarcoma is important for clinical decision making, which requires the consideration of complex factors.

Goal(s): In this study, we investigated the ability of ioversol chemical exchange saturation transfer (CEST) imaging in detecting pH in the microenvironment of rat osteosarcoma.

Approach: Assessing pH in the microenvironment of osteosarcoma using CEST imaging.

Results: It was found that the lower pH value, the lower ratiometric value acquired by two different saturation power of 1μT and 3μT.

Impact: In addition, CEST can detect the pH-dependent ratiometric value of tumor microenvironment non-invasively and sensitively. It is expected to become one of the methods for early evaluation of the effect of preoperative neoadjuvant chemotherapy in cancer patients.

Keywords: Whole Joint, Radiomics, femoroacetabular impingement, Radiomics, machine learning

Motivation: Radiomics could differentiate the symptomatic hip from the asymptomatic contralateral hip in patients with femoroacetabular impingement (FAI). This study investigates its potential in distinguishing FAI patients from healthy subjects.

Goal(s): To compare the diagnostic performance of radiomic features and clinical metrics in FAI diagnosis.

Approach: We used 3D Dixon MRI data (10 healthy subjects and 10 FAI patients). We trained machine learning models on radiomic features extracted from MRI to classify subjects as healthy or FAI. Models were trained also on clinical metrics for comparison.

Results: Radiomic features accurately identified FAI patients without errors (100% accuracy). Clinical metrics achieved 74% accuracy.

Impact: Radiomic features exhibited a remarkable diagnostic performance, accurately identifying all FAI patients and healthy subjects. This study shows the promise of radiomics to enable automated FAI diagnosis.

Keywords: Whole Joint, Infection

Motivation: Magnetic resonance imaging (MRI) is the gold standard for diagnosis of acute pediatric musculoskeletal infection. However, there are several barriers in the pediatric population, namely poor efficiency of image acquisition. 

Goal(s): The present study evaluates physician perspectives on a novel rapid MRI protocol for capturing rapid, non-contrast, limited sequence protocol that has been shown to greatly decrease sedation, scan time, hospital length of stay, and charges without missing actionable diagnoses. 

Approach: We administered a series of traditional and rapid protocol cases and associated quiz to six providers.

Results: We found similar success rates of diagnoses and physician confidence between rapid and traditional sequences.

Impact: Our study shows that rapid MRI protocols can be adopted without impeding diagnostic accuracy or reducing the confidence of readers.

Keywords: Cartilage, Joints, knee, joints, mri, radiomic

Motivation: Predicting total knee replacement surgery can help patients and healthcare providers make informed treatment decisions.

Goal(s): To develop a machine learning model for predicting patients that will undergo a total knee replacement using radiomics.

Approach: To extract radiomic features from images of patients and healthy subjects and train different machine learning models to predict patients' outcomes. 

Results: The best model achieved an accuracy of 87.2%. Three out of the four most significant radiomic features selected in the All subset were derived from the meniscus areas, suggesting that the meniscus may play a crucial role in predicting patient outcomes.

Impact: Radiomic features from MRI scans effectively classify TKR-positive patients, particularly those incorporating meniscus features. These models potentially can predict patient outcomes and guide treatment decisions, but further research is needed to enhance performance and validate findings in broader patient populations.

Keywords: Other Musculoskeletal, Tumor

Motivation: The integration of various diffusion models can yield more precise insights into tumor diffusion and perfusion.

Goal(s): To investigate the utility of combining DWI, IVIM and DKI in the differential diagnosis of benign and malignant bone tumors.

Approach: Six parameters derived from three diffusion models of 107 patients were statistically compared through either the independent samples t-test or Mann-Whitney U test. Diagnostic performance was assessed using ROC curves for both individual examinations and their combined analysis in distinguishing between benign and malignant tumors.  

Results: The combination of ADC, D, D*, MK, and MD values had better diagnostic efficacy than these parameters alone.

Impact: Statistically significant differences were observed in the values of ADC, D, D*, MD, and MK between benign and malignant bone tumors. Furthermore, the combination of ADC, D, D*, MK, and MD values had better diagnostic efficacy than these parameters alone.

Keywords: Bone, Bone, Radiation, MRI, Texture Analysis

Motivation: To promote a non-invasive method to evaluate the radiation induced femoral head changes.

Goal(s): To investigate the value of MRI texture analysis for evaluating radiation induced femoral head changes in rectal cancer patients after radiotherapy.

Approach: The texture parameters of T1WI, T2WI and DWI images were extracted in femoral head. And the differences of texture parameters before and after radiotherapy were compared.

Results: We found that when combined the texture parameters of T1WI, T2WI and DWI images, the diagnostic efficiency of femoral head changes was optimal with AUC of 0.85 (sensitivity,0.81; specificity,0.80).

Impact: MRI texture analysis may provide a non-invasive method to evaluate the radiation induced femoral head changes, providing imaging clues for monitoring the potential radiation-induced femoral head injury.

Keywords: Cartilage, MSK

Motivation: Identifying patients with a  backfill progression is crucial for predicting clinical prognosis and adjusting treatment approaches in the disease process.

Goal(s): This study aimed to extract radiomics features for the sacroiliac joint on MRI images in patients with axSpA to predict backfill progression within one year.

Approach: This retrospective study analyzed 257 patients diagnosed with axSpA. The radiomics and clinical models were combined to construct a nomogram model through multivariable logistic regression analysis.

Results: Seven radiomics features were extracted to generate a Rad-score. The AUCs of the radiomics, clinical, and nomogram models in the training cohort were 0.90, 0.78 and 0.93, respectively.

Impact: The built radiomics-based nomogram has good predictive value for structural progression in patients with axial spondyloarthritis.

Keywords: Bone, Relaxometry, Axial spondyloarthritis, T1 mapping

Motivation: Currently, there is a lack of an objective quantitative measure to comprehensively evaluate the inflammatory activity of axSpA, which poses certain challenges in accurately assessing the disease activity.

Goal(s): To establish a more reliable combined-parameter model for assessing the inflammatory activity of axSpA.

Approach: T1 mapping values, T2* mapping values and the frequency of MAIT cells and CD69⁺MAIT cells are constructed into single-parameter and combined-parameter models using logistic regression. The diagnostic efficacy was evaluated by employing the ROC curves.

Results: The model combining T1 mapping with CD69+MAIT cells showed relatively superior diagnostic efficacy in differentiating the severity of axSpA disease activity.

Impact: The combined-parameter model incorporating T1 mapping and CD69+MAIT cells provided an effective objective quantitative indicator for evaluating the inflammatory activity of axSpA.

Keywords: Bone, Artifacts

Motivation: Metal artifact reduction techniques needs longer scan time and shows incomplete MRI visibility for postoperative spinal evaluation. 

Goal(s): We assessed the reliability of localizer sequence image factors for predicting the metal artifact range in MRI. 

Approach: Multivariate ordinal logistic regression models were generated using prosthesis information and imaging factors measured on localizer images, to verify the factors correlated to the visibility of spinal canal and neural foramen on MRI. 

Results: Metal artifact degree at midline and foramen level of sagittal plane and inter-screw distance of coronal plane in localizer images were significant factors affecting metal artifact range of MRI.

Impact: We found that the metal artifact degree in localizer sequence image can be correlated with the metal artifact range of diagnostic sequence image, and used to screen proper patients to apply metal artifact reduction sequence in postoperative spinal MRI.