Search

Abstract Background A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and efficiency. Methods A method comprising region-of-interest (ROI) detection and cascade prediction was formulated for diagnosing cervical spinal stenosis based on a DL model. First, three part-specific convolutional neural networks were employed to detect the ROIs in different parts of the cervical MR images. Cascade prediction of the stenosis categories was subsequently performed to record the stenosis level and position on each patient slice. Finally, the results were combined to obtain a patient-level diagnostic report. Performance was evaluated based on the accuracy (ACC), area under the curve (AUC), sensitivity, specificity, F1 Score, diagnosis time of the DL model, and recall rate for ROI detection localization. Results The average recall rate of the ROI localization was 89.3% (neural foramen) and 99.7% (central canal) under the five-fold cross-validation of the DL model. In the dichotomous classification (normal or mild vs. moderate or severe), the ACC and AUC of the DL model were comparable to those of the radiologists, and the F1 score (84.8%) of the DL model was slightly higher than that of the radiologists (83.8%) for the central canal. Diagnosing whether the central canal or neural foramen of a slice is narrowed in the cervical MRI scan required an average of 15 and 0.098 s for the radiologists and DL model, respectively. Conclusions The DL model demonstrated comparable performance with subspecialist radiologists for the detection and classification of central canal and neural foraminal stenosis on cervical spine MRI. Moreover, the DL model demonstrated significant timesaving ability.

Abstract Background Early cervical spondylotic myelopathy (CSM) is challenging to diagnose and easily missed. Diffusion MRI (dMRI) has the potential to identify early CSM. Methods Using diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI), a 1:1 matched case-control study was conducted to evaluate the potential of dMRI in identifying early CSM and assessing uncompressed segments of CSM patients. CSM patients and volunteers were matched by age and spinal location. The differences in dMRI parameters between groups were assessed by the paired t-test, the multicollinearity of the dMRI parameters was evaluated by the variance inflation factor (VIF), and the value of dMRI parameters in distinguishing controls from CSM patients was determined by logistic regression. The univariate t-test was used to analyse differences between CSM patients and volunteers in adjacent uncompressed areas. Results In total, 56 CSM patients and 56 control volunteers were included. Paired t-tests revealed significant differences in nine dMRI parameters between groups. Multicollinearity calculated through VIF and combined with logistic regression showed that the orientation division index (ODI) was significantly positively correlated (r = 2.12, p = 0.035), and the anisotropic water fraction (AWF) was significantly negatively correlated (r = −0.98, p = 0.015). The fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (ISOVF), ODI, and AWF were significantly different in the upper and lower uncompressed areas at all ages. Conclusion dMRI can noninvasively identify early CSM patients and potentially identify the extent of CSM lesions involving the cervical spinal cord. Critical relevance statement Diffusion MRI (dMRI) can identify early cervical spondylotic myelopathy (CSM) and has the potential to help determine the extent of CSM involvement. The application of dMRI can help screen for early CSM and develop clinical surgical and rehabilitation treatment plans. Key points • Diffusion MRI can differentiate between normal and early-stage cervical spondylotic myelopathy patients. • Diffusion MRI has the ability to identify the extent of spinal cord involvement in cervical spondylotic myelopathy. • Diffusion MRI enables the early screening of cervical spondylotic myelopathy and helps guide clinical treatment. Graphical Abstract

Abstract Purposes To develop a deep learning (DL) model to measure the sagittal Cobb angle of the cervical spine on computed tomography (CT). Materials and methods Two VB-Net-based DL models for cervical vertebra segmentation and key-point detection were developed. Four-points and line-fitting methods were used to calculate the sagittal Cobb angle automatically. The average value of the sagittal Cobb angle was manually measured by two doctors as the reference standard. The percentage of correct key points (PCK), matched samples t test, intraclass correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), and Bland‒Altman plots were used to evaluate the performance of the DL model and the robustness and generalization of the model on the external test set. Results A total of 991 patients were included in the internal data set, and 112 patients were included in the external data set. The PCK of the DL model ranged from 78 to 100% in the test set. The four-points method, line-fitting method, and reference standard measured sagittal Cobb angles were − 1.10 ± 18.29°, 0.30 ± 13.36°, and 0.50 ± 12.83° in the internal test set and 4.55 ± 20.01°, 3.66 ± 18.55°, and 1.83 ± 12.02° in the external test set, respectively. The sagittal Cobb angle calculated by the four-points method and the line-fitting method maintained high consistency with the reference standard (internal test set: ICC = 0.75 and 0.97; r = 0.64 and 0.94; MAE = 5.42° and 3.23°, respectively; external test set: ICC = 0.74 and 0.80, r = 0.66 and 0.974, MAE = 5.25° and 4.68°, respectively). Conclusions The DL model can accurately measure the sagittal Cobb angle of the cervical spine on CT. The line-fitting method shows a higher consistency with the doctors and a minor average absolute error.

Abstract Background High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been recently introduced as an integrative acceleration solution. We compared ACS-accelerated 3-T ankle MRI to conventional methods of compressed sensing (CS) and parallel imaging (PI) . Methods We prospectively included 2 healthy volunteers and 105 patients with ankle pain. ACS acceleration factors for ankle protocol of T1-, T2-, and proton density (PD)-weighted sequences were optimized in a pilot study on healthy volunteers (acceleration factor 3.2–3.3×). Images of patients acquired using ACS and conventional acceleration methods were compared in terms of acquisition times, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic agreement. Shapiro-Wilk test, Cohen κ, intraclass correlation coefficient, and one-way ANOVA with post hoc tests (Tukey or Dunn) were used. Results ACS acceleration reduced the acquisition times of T1-, T2-, and PD-weighted sequences by 32−43%, compared with conventional CS and PI, while maintaining image quality (mostly higher SNR with p < 0.004 and higher CNR with p < 0.047). The diagnostic agreement between ACS and conventional sequences was rated excellent (κ = 1.00). Conclusions The optimum ACS acceleration factors for ankle MRI were found to be 3.2–3.3× protocol. The ACS allows faster imaging, yielding similar image quality and diagnostic performance. Relevance statement AI-assisted compressed sensing significantly accelerates ankle MRI times while preserving image quality and diagnostic precision, potentially expediting patient diagnoses and improving clinical workflows. Key points • AI-assisted compressed sensing (ACS) significantly reduced scan duration for ankle MRI. • Similar image quality achieved by ACS compared to conventional acceleration methods. • A high agreement by three acceleration methods in the diagnosis of ankle lesions was observed. Graphical Abstract

Abstract Objective This study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT). Methods Patients with spinal metastases who were treated using SBRT at our hospital between July 2018 and April 2023 were recruited. We assessed their response to treatment using the revised Response Evaluation Criteria in Solid Tumors (version 1.1). The lesions were categorized into progressive disease (PD) and non-PD groups. Radiomics features were extracted from T1-weighted image (T1WI), T2-weighted image (T2WI), and fat-suppression T2WI sequences. Feature selection involved intraclass correlation coefficients, minimal-redundancy-maximal-relevance, and least absolute shrinkage and selection operator methods. Thirteen ML algorithms were employed to construct the radiomics prediction models. Clinical, conventional imaging, and radiomics features were integrated to develop combined models. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the clinical value was assessed using decision curve analysis. Results We included 194 patients with 142 (73.2%) lesions in the non-PD group and 52 (26.8%) in the PD group. Each region of interest generated 2264 features. The clinical model exhibited a moderate predictive value (area under the ROC curve, AUC = 0.733), while the radiomics models demonstrated better performance (AUC = 0.745–0.825). The combined model achieved the best performance (AUC = 0.828). Conclusion The MRI-based radiomics models exhibited valuable predictive capability for treatment outcomes in patients with spinal metastases undergoing SBRT. Critical relevance statement Radiomics prediction models have the potential to contribute to clinical decision-making and improve the prognosis of patients with spinal metastases undergoing SBRT. Key points • Stereotactic body radiotherapy effectively delivers high doses of radiation to treat spinal metastases. • Accurate prediction of treatment outcomes has crucial clinical significance. • MRI-based radiomics models demonstrated good performance to predict treatment outcomes. Graphical Abstract

Abstract Background Inferior vena cava tumor thrombus (IVCTT) invading the IVC wall majorly affects the surgical method choice and prognosis in renal tumors. Enhanced multiparameteric MRI plays an important role in preoperative evaluation. In this work, an MRI-based diagnostic model for IVCTT was established so as to guide the preoperative decisions. Methods Preoperative MR images of 165 cases of renal tumors with IVCTT were retrospectively analyzed, and imaging indicators were analyzed, including IVCTT morphology and Mayo grade, IVCTT diameter measurements, bland thrombosis, primary MRI-based diagnosis of renal tumor, and involvement of contralateral renal vein. The indicators were analyzed based on intraoperative performance and resection scope of the IVC wall. Multivariate logistic regression analysis was used to establish the diagnostic model. Results The morphological classification of the IVCTT, primary MRI-based diagnosis of renal tumors, maximum transverse diameter of IVCTT, and length of the bland thrombus were the main indexes predicting IVC wall invasion. The MRI-based diagnostic model established according to these indexes had good diagnostic efficiency. The prediction probability of 0.61 was set as the cutoff value. The area under the curve of the test set was 0.88, sensitivity was 0.79, specificity was 0.85, and prediction accuracy was 0.79 under the optimal cutoff value. Conclusion The preoperative MRI-based diagnostic model could reliably predict IVC wall invasion, which is helpful for better prediction of IVC-associated surgical operations.

ObjectiveCharcot–Marie–Tooth (CMT) disease is the most common inherited neuromuscular disorder. Multi-echo Dixon MRI technique is a highly sensitive method for quantifying muscle fatty infiltration, which may provide excellent value for the assessment of CMT. Due to the rareness of the disease, its use in CMT disease has been rarely evaluated, especially in subtypes.MethodsThirty-four CMT1 patients, 25 CMT2 patients, and 10 healthy controls were recruited. All of the recruited CMT1 patients are CMT1A with PMP22 duplication. Among CMT2 patients, 7 patients are CMT2A with MFN2 mutation, and 7 patients have SORD mutations. Multi-echo Dixon MRI imaging was performed. The fat fractions (FFs) of 5 muscle compartments of the leg were measured at proximal, middle, and distal levels by two specialized musculoskeletal radiologists. Comparisons between CMT1, CMT2, and genetically defined subtypes were conducted.ResultsA proximal-distal gradient (27.6 ± 15.9, 29.9 ± 19.7, and 40.5 ± 21.4, p = 0.015) with a peroneal predominance (p = 0.001) in fat distribution was observed in CMT1. Significant differences in the soleus muscle FFs at proximal (19.1 ± 14.7 vs. 34.8 ± 25.1, p = 0.034) and medial levels (23.5 ± 21 vs. 38.0 ± 25.6, p = 0.044) were observed between CMT1 and CMT2 patients. Between PMP2 duplication and MFN2 mutation group, a significant difference in the soleus muscle FF was also observed (23.5 ± 21.0 vs. 54.7 ± 20.2, p = 0.039). Prominent correlations of calf muscle FFs with functional scores were observed.DiscussionMulti-echo Dixon MRI imaging is a valuable tool for assessing disease severity in CMT. The difference in patterns of fatty infiltration of CMT subtypes is first reported, which could provide references when making targeted training plans.

IntroductionSacral laminoplasty with titanium mesh and titanium screws can reduce symptomatic sacral extradural spinal meningeal cysts (SESMCs) recurrence and operation complications. However, due to a defect or thinning of the sacrum, the screws cannot be securely anchored and there are also problems with permanent metal implantation for titanium mesh and screws. We propose that sacral laminoplasty with absorbable clamps can provide rigid fixation even for a thinned or defected sacrum without leaving permanent metal implants.MethodsIn the direct microsurgical treatment of symptomatic SESMCs, we performed one-stage sacral laminoplasty with autologous sacral lamina reimplantation fixed by absorbable fixation clamps. Retrospectively, we analyzed intraoperative handling, planarity of the sacral lamina, and stability of the fixation based on clinical and radiological data.ResultsBetween November 2021 to October 2022, we performed sacral laminoplasty with the absorbable craniofix system in 28 consecutive patients with SESMCs. The size of the sacral lamina flaps ranged from 756 to 1,052 mm2 (average 906.21 ± 84.04 mm2). We applied a minimum of two (in four cases) and up to four (in four cases) Craniofix clamps in the operation, with three (in 20 cases) being the most common (82.14%, 20/28) and convenient to handle. Excellent sacral canal reconstruction could be confirmed intraoperatively by the surgeons and postoperatively by CT scans. No intraoperative complications occurred.ConclusionsOne-stage sacral laminoplasty with absorbable fixation clamps is technically feasible, and applying 3 of these can achieve a stable fixation effect and are easy to operate. Restoring the normal structure of the sacral canal could reduce complications and improve surgical efficacy.

BackgroundArterial spin labeling (ASL) is a non-invasive technique for measuring cerebral perfusion. Its accuracy is affected by the arterial transit time. This study aimed to (1) evaluate the accuracy of ASL in measuring the cerebral perfusion of patients who underwent carotid endarterectomy (CEA) and (2) determine a better postlabeling delay (PLD) for pre- and postoperative perfusion imaging between 1.5 and 2.0 s.MethodsA total of 24 patients scheduled for CEA due to severe carotid stenosis were included in this study. All patients underwent ASL with two PLDs (1.5 and 2.0 s) and computed tomography perfusion (CTP) before and after surgery. Cerebral blood flow (CBF) values were measured on the registered CBF images of ASL and CTP. The correlation in measuring perioperative relative CBF (rCBF) and difference ratio of CBF (DRCBF) between ASL with PLD of 1.5 s (ASL1.5) or 2.0 s (ASL2.0) and CTP were also determined.ResultsThere were no significant statistical differences in preoperative rCBF measurements between ASL1.5 and CTP (p = 0.17) and between ASL2.0 and CTP (p = 0.42). Similarly, no significant differences were found in rCBF between ASL1.5 and CTP (p = 0.59) and between ASL2.0 and CTP (p = 0.93) after CEA. The DRCBF measured by CTP was found to be marginally lower than that measured by ASL2.0_1.5 (p = 0.06) and significantly lower than that measured by ASL1.5_1.5 (p = 0.01), ASL2.0_2.0 (p = 0.03), and ASL1.5_2.0 (p = 0.007). There was a strong correlation in measuring perioperative rCBF and DRCBF between ASL and CTP (r = 0.67–0.85, p

Abstract Background Subspine impingement is considered a source of residual hip symptoms after primary hip arthroscopy, and the role of the subspine space and soft tissue is not clear. The purpose of this study was to analyze the relationship between the subspine space and labrum size in subspine impingement patients. Methods We performed a retrospective study of patients with femoroacetabular impingement between July 2016 and July 2020. Sixteen patients without hip symptom relief after primary hip arthroscopic treatment of femoroacetabular impingement and undergoing revision surgery for anterior inferior iliac spine compression were included as the study group. Forty-eight matched patients who underwent only primary surgery and whose hip discomfort was relieved without a diagnosis of subspine impingement were included as the control group. The patients’ preoperative computerized tomography data were reviewed, and the anterior inferior iliac spine dimensions and the size of the subspine space were measured. The size of the labrum at the 11:30, 1:30, and 3 o’clock positions was measured with the use of magnetic resonance imaging. The ratio of the subspine space to the labrum was also calculated. Results There was no significant difference in anterior inferior iliac spine dimensions between these two groups (p > 0.05). A relatively narrow subspine space was found in the study group, especially in the direction of the anterior inferior iliac spine. Compared with the control group, subspine impingement patients were identified with larger labrums at 11:30 (8.20 ± 1.95 mm vs. 6.81 ± 0.50 mm, p = 0.016), 1:30 (7.83 ± 1.61 mm and 6.25 ± 0.78 mm, p = 0.001) and 3:00 (9.50 ± 1.73 mm vs. 7.48 ± 0.99 mm, p = 0.001). A relative mismatch between the subspine space and the labrum was also identified in the study group. The ratios of the labrum width to the subspine area were significantly larger in the study group than in the control group. Conclusion This study reported potential additional criteria for subspine impingement—a large labrum and a relatively narrow subspine space—instead of abnormal anterior inferior iliac spine dimensions. For those with a large labrum and narrow subspine space, the diagnosis of subspine impingement should be carefully made, and arthroscopic anterior inferior iliac spine decompression may be important.

Abstract Objectives To evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V). Methods Carotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups. Results The image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000–0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000–0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000–0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05). Conclusions Compared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction.

ObjectivesNeuromyelitis optica spectrum disorder (NMOSD) and long-segment degenerative cervical myelopathy (DCM) may have a similar appearance on MRI. This study aimed to identify the differences in spinal cord gadolinium enhancement features between NMOSD and long-segment DCM.MethodsSpinal cord gadolinium enhancement of 27 NMOSD patients and 30 long-segment DCM patients were retrospectively analyzed. Enhancements were evaluated for their number, length, location on the sagittal images, distribution on the axial images, and form on the sagittal images. The Wilcoxon rank sum test was performed to compare numerical variables. The Pearson chi-squared test was performed to compare categorical variables.ResultsThe median number of enhanced lesions (p < 0.05), the median length of the enhancements (p < 0.05), and the location of enhancement on sagittal images (p < 0.05) of NMOSD patients and long-segment DCM patients showed significant differences. The axial distribution of enhancements did not show a significant difference between NMOSD and long-segment DCM patients (p = 0.115). On the sagittal images, linear and ring-formed enhancements were observed in 10 (27.0%) and 17 (63.0%) NMOSD patients, respectively. The enhancements in long-segment DCM patients had a transverse band or pancake-like appearance in 15 (50%) patients and an irregular flake-like appearance with a longitudinally oriented long axis in 15 patients (50%).ConclusionBy analyzing the number, length, location, and form of the gadolinium enhancements, NMOSD and long-segment DCM could be well-differentiated.

Abstract Objectives To analyze the radiologic and clinical changes after denosumab treatment in patients with giant cell tumors (GCTs) in the mobile spine. Methods Clinical data and images by computed tomography and magnetic resonance imaging at a single center were retrospectively reviewed before and after denosumab treatment. Results Pre- and post-treatment data from 24 patients were evaluated. On imaging, marginal ossification and/or bone formation was observed in 22 patients (91.7%). The median maximum diameter of the GCT reduced from 52.5 to 48.2 mm (p

Abstract Background The application of deep learning has allowed significant progress in medical imaging. However, few studies have focused on the diagnosis of benign and malignant spinal tumors using medical imaging and age information at the patient level. This study proposes a multi-model weighted fusion framework (WFF) for benign and malignant diagnosis of spinal tumors based on magnetic resonance imaging (MRI) images and age information. Methods The proposed WFF included a tumor detection model, sequence classification model, and age information statistic module based on sagittal MRI sequences obtained from 585 patients with spinal tumors (270 benign, 315 malignant) between January 2006 and December 2019 from the cooperative hospital. The experimental results of the WFF were compared with those of one radiologist (D1) and two spine surgeons (D2 and D3). Results In the case of reference age information, the accuracy (ACC) (0.821) of WFF was higher than three doctors’ ACC (D1: 0.686; D2: 0.736; D3: 0.636). Without age information, the ACC (0.800) of the WFF was also higher than that of the three doctors (D1: 0.750; D2: 0.664; D3:0.614). Conclusions The proposed WFF is effective in the diagnosis of benign and malignant spinal tumors with complex histological types on MRI.

Abstract Background Epithelioid hemangioendothelioma (EHE) is a low-grade malignant vascular neoplasm with the potential to metastasize. Primary EHE of the spine is very rare and an accurate diagnosis is crucial to treatment planning. We aim to investigate the imaging and clinical data of spinal EHE to improve the understanding of the disease. Methods We retrospectively analyzed the imaging manifestations and clinical data of 12 cases with pathologically confirmed spinal EHE. The imaging features analyzed included number, locations, size, border, density, signal, majority of the lesions, expansile osteolysis, residual bone trabeculae, sclerotic rim, vertebral compression, enhancement. Results Patients included 5 female and 7 male patients (mean age: 43.0 ± 19.6 years; range 15–73 years). Multiple lesions were noted in 1 case and single lesion was noted in 11 cases. The lesions were located in the thoracic, cervical, lumbar, and sacral vertebrae in 7, 3, 1, and 1 cases, respectively. They were centered in the vertebral body and posterior elements in 9 and 3 cases, respectively. Residual bone trabeculae, no sclerotic margin, and surrounding soft-tissue mass were noted in 11 cases, each, and mild expansile osteolysis and vertebral compression were noted in 10 and 6 cases, respectively. MRI was performed for 11 patients, all of whom showed isointensity on T1WI, hyperintensity or slight hyperintensity on T2WI, and hyperintensity on fat-suppressed T2WI. A marked enhancement pattern was noted in 10 cases. Conclusion Spinal EHE tend to develop in the thoracic vertebrae. EHE should be considered when residual bone trabeculae can be seen in the bone destruction area, accompanied by pathological compression fracture, no sclerotic rim, and high signal intensity for a vascular tumor on T2WI.

PurposeThis study aimed to evaluate the difference in vertebral mechanical properties estimated by finite element analysis (FEA) with different computed tomography (CT) reconstruction kernels and evaluate their accuracy in the screening and classification of osteoporosis.MethodsThere were 31 patients enrolled retrospectively from the quantitative CT database of our hospital, uniformly covering the range from osteoporosis to normal. All subjects’ CT raw data were reconstructed both with a smooth standard convolution kernel (B40f) and a sharpening bone convolution kernel (B70f), and FEA was performed on L1 of each subject based on two reconstructed images to obtain vertebral estimated strength and stiffness. The trabecular volumetric bone mineral density (vBMD) of the same vertebral body was also measured. FEA measurements between two kernels and their accuracy for osteoporosis screening were compared.ResultsThe vertebral stiffness and strength measured in FEA-B40f were significantly lower compared with those of FEA-B70f (12.0%, p = 0.000 and 10.7%, p = 0.000, respectively). The correlation coefficient between FEA-B70F and vBMD was slightly higher than that of FEA-B40F in both vertebral strength and stiffness (strength: r2-B40f = 0.21, p = 0.009 vs. r2-B70f = 0.27, p = 0.003; stiffness: r2-B40f = 0.37, p = 0.002 vs. r2-B70f = 0.45, p=0.000). The receiver operator characteristic curve showed little difference in the classification of osteoporosis between FEA-B40f and FEA-B70f.ConclusionTwo kernels both seemed to be applicable to the opportunistic screening of osteoporosis by CT-FEA despite variance in FE-estimated bone strength and bone stiffness. A protocol for CT acquisition and FEA is still required to guarantee the reproducibility of clinical use.

BackgroundAcute vertebral fracture is usually caused by low-energy injury with osteoporosis and high-energy trauma. The AOSpine thoracolumbar spine injury classification system (AO classification) plays an important role in the diagnosis and treatment of the disease. The diagnosis and description of vertebral fractures according to the classification scheme requires a great deal of time and energy for radiologists.PurposeTo design and validate a multistage deep learning system (multistage AO system) for the automatic detection, localization and classification of acute thoracolumbar vertebral body fractures according to AO classification on computed tomography.Materials and MethodsThe CT images of 1,217 patients who came to our hospital from January 2015 to December 2019 were collected retrospectively. The fractures were marked and classified by 2 junior radiology residents according to the type A standard in the AO classification. Marked fracture sites included the upper endplate, lower endplate and posterior wall. When there were inconsistent opinions on classification labels, the final result was determined by a director radiologist. We integrated different networks into different stages of the overall framework. U-net and a graph convolutional neural network (U-GCN) are used to realize the location and classification of the thoracolumbar spine. Next, a classification network is used to detect whether the thoracolumbar spine has a fracture. In the third stage, we detect fractures in different parts of the thoracolumbar spine by using a multibranch output network and finally obtain the AO types.ResultsThe mean age of the patients was 61.87 years with a standard deviation of 17.04 years, consisting of 760 female patients and 457 male patients. On vertebrae level, sensitivity for fracture detection was 95.23% in test dataset, with an accuracy of 97.93% and a specificity of 98.35%. For the classification of vertebral body fractures, the balanced accuracy was 79.56%, with an AUC of 0.904 for type A1, 0.945 for type A2, 0.878 for type A3 and 0.942 for type A4.ConclusionThe multistage AO system can automatically detect and classify acute vertebral body fractures in the thoracolumbar spine on CT images according to AO classification with high accuracy.

This paper proposes a deep-learning-based image enhancement approach that can generate high-resolution micro-CT-like images from multidetector computed tomography (MDCT). A total of 12,500 MDCT and micro-CT image pairs were obtained from 25 vertebral specimens. Then, a pix2pixHD model was trained and evaluated using the structural similarity index measure (SSIM) and Fréchet inception distance (FID). We performed subjective assessments of the micro-CT-like images based on five aspects. Micro-CT and micro-CT-like image-derived trabecular bone microstructures were compared, and the underlying correlations were analyzed. The results showed that the pix2pixHD method (SSIM, 0.804 ± 0.037 and FID, 43.598 ± 9.108) outperformed the two control methods (pix2pix and CRN) in enhancing MDCT images (p < 0.05). According to the subjective assessment, the pix2pixHD-derived micro-CT-like images showed no significant difference from the micro-CT images in terms of contrast and shadow (p > 0.05) but demonstrated slightly lower noise, sharpness and trabecular bone texture (p < 0.05). Compared with the trabecular microstructure parameters of micro-CT images, those of pix2pixHD-derived micro-CT-like images showed no significant differences in bone volume fraction (BV/TV) (p > 0.05) and significant correlations in trabecular thickness (Tb.Th) and trabecular spacing (Tb.Sp) (Tb.Th, R = 0.90, p < 0.05; Tb.Sp, R = 0.88, p < 0.05). The proposed method can enhance the resolution of MDCT and obtain micro-CT-like images, which may provide new diagnostic criteria and a predictive basis for osteoporosis and related fractures.

BackgroundTo investigate the value of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) to discriminate spinal metastasis from tuberculous spondylitis.MethodsThis study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 with tuberculous spondylitis. The IVIM parameters, including the single-index model (apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow, ADCfast, and f), and the stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α), were acquired. Receiver operating characteristic (ROC) and the area under the ROC curve (AUC) analysis was used to evaluate the diagnostic performance. Each parameter was substituted into a logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated.ResultsThe ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis (all p < 0.05). The logistic regression model results showed that ADCfast and f were independent factors affecting the outcome (P < 0.05). The AUC values of ADCfast and f were 0.823 (95% confidence interval (CI): 0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95% CI: 0.858 to 0.992).ConclusionsIVIM MR imaging might be helpful to differentiate spinal metastasis from tuberculous spondylitis, and provide guidance for clinical treatment.

Background: Appropriate monitoring and early recognition of postoperative cognitive improvement (POCI) are essential. Near-infrared spectroscopy (NIRS) showed the predictive potential of POCI. Non-linear dynamical analysis is a powerful approach for understanding intraoperative regional cerebral oxygen saturation (rSO2).Objective: We hypothesized that the sample entropy (SampEn) value of intraoperative rSO2 has the potential to predict POCI.Methods: This retrospective cohort study was conducted from June 2019 and December 2020 in a tertiary hospital in Beijing, China. A total of 126 consecutive patients who underwent carotid endarterectomy (CEA) were screened. 57 patients were included in this analysis. The primary outcome was the diagnostic accuracy of rSO2 for the prediction of POCI.Results: 33 patients (57.9%) developed POCI on postoperative day. The SampEn values of rSO2 were significantly higher in the POCI group (p < 0.05). SampEn remained an independent predictor of POCI in multivariate analysis. The area under the ROC curve (AUC) value of SampEn of rSO2 for POCI were 0.706 (95% CI, 0.569–0.843; p = 0.008). Addition of preoperative MoCA assessment and blood pressure-lowering treatment increased the AUC to 0.808 (95% CI, 0.697–0.919; p < 0.001).Conclusions: The SampEn value of rSO2 showed promise as a predictor of POCI. Non-linear analysis could be used as a supplementary method for intraoperative physiological signals.

Abstract Objectives To review the clinical and imaging data of spinal giant cell tumour of the tendon sheath (GCTTS) to improve our understanding of the disease. Methods The imaging findings, clinicopathological features and clinical outcomes of 14 patients with pathologically confirmed spinal GCTTS were analysed retrospectively. Results All 14 patients had a single spinal lesion, including ten cervical vertebra lesions and four thoracic vertebra lesions. CT scan findings: The lesions showed osteolytic bone destruction and were centred on the facet joint, eroding the surrounding bone with a paravertebral soft tissue mass. MRI scan findings: all the lesions manifested predominantly as isointense or hypointense on T1-weighted imaging (T1WI). On T2-weighted imaging (T2WI), eight lesions were hypointense, and four were isointense. The remaining two lesions showed slight hyperintensity. The enhanced scans of eight lesions showed moderate to marked homogeneous or heterogeneous enhancement. PET/CT findings: Among the five patients who underwent PET/CT, three presented lesions with well-defined, sclerotic borders, and the uptake of 18F-FDG was markedly increased. One lesion showed an ill-defined border and an uneven increase in 18F-FDG uptake with an SUVmax value of 8.9. A recurrent lesion was only found on PET/CT 45 months after surgery and the SUVmax was 5.1. Conclusions Spinal GCTTS is extremely rare. Osteolytic bone destruction in the area of the facet joint with a soft tissue mass and hypointensity on T2WI images are indicative of the spinal GCTTS. GCTTS shows high uptake of 18F-FDG, and PET/CT is helpful in detecting recurrent lesions.

Bone structure metrics are vital for the evaluation of vertebral bone strength. However, the gold standard for measuring bone structure metrics, micro-Computed Tomography (micro-CT), cannot be used in vivo, which hinders the early diagnosis of fragility fractures. This paper used an unpaired image-to-image translation method to capture the mapping between clinical multidetector computed tomography (MDCT) and micro-CT images and then generated micro-CT-like images to measure bone structure metrics. MDCT and micro-CT images were scanned from 75 human lumbar spine specimens and formed training and testing sets. The generator in the model focused on learning both the structure and detailed pattern of bone trabeculae and generating micro-CT-like images, and the discriminator determined whether the generated images were micro-CT images or not. Based on similarity metrics (i.e., SSIM and FID) and bone structure metrics (i.e., bone volume fraction, trabecular separation and trabecular thickness), a set of comparisons were performed. The results show that the proposed method can perform better in terms of both similarity metrics and bone structure metrics and the improvement is statistically significant. In particular, we compared the proposed method with the paired image-to-image method and analyzed the pros and cons of the method used.

PurposeTo examine the associations between carotid plaque characteristics and perioperative cerebral blood flow (CBF) by arterial spin labeling (ASL) imaging.Materials and MethodsPatients with unilateral moderate-to-severe carotid stenosis referred for carotid endarterectomy (CEA) were recruited and underwent carotid vessel wall and brain ASL magnetic resonance imaging. The following imaging features were measured: relative CBF (rCBF = CBFindex−hemisphere/CBFcontralateral−hemisphere) in the middle cerebral artery territory; plaque burden and the presence of lipid-rich necrotic core; intraplaque hemorrhage (IPH); calcification; ulcer and fibrous-cap rupture; and the volume and maximum plaque components' area percentages. The associations between plaque characteristics and perioperative CBF were analyzed.ResultsSixty-one patients (mean age, 66.6 ± 7.8 years; 55 males) were included. Univariate linear regression showed that rCBFpre−CEA was associated with stenosis [β, −0.462; 95% confidence interval (CI), from −0.797 to −0.126; p = 0.008], calcification (β, 0.103; 95% CI, 0.005–0.201; p = 0.040), maximum IPH area percentage (β, −0.127; 95% CI, from −0.223 to −0.030; p = 0.012), and ulcer (β, 0.069; 95% CI, 0.025–0.113; p = 0.005); rCBFpost−CEA was associated with the IPH volume (β, −0.060; 95% CI, from −0.107 to −0.014; p = 0.013). After adjusting for the confounding factors, the associations of calcification with rCBFpre−CEA (β, 0.099; 95% CI, from 0.004 to −0.194; p = 0.042) and IPH volume with rCBFpost−CEA (β, −0.060; 95% CI, from −0.109 to −0.011; p = 0.020) remained statistically significant, while those of rCBFpre−CEA with maximum IPH area percentage (β, −0.089; 95% CI, from −0.188 to 0.011; p = 0.080) and ulcer (β, 0.050; 95% CI, from −0.012 to 0.112; p = 0.100) did not remain statistically significant.ConclusionThe compositional characteristics of carotid atherosclerotic plaques, particularly IPH, were associated with perioperative CBF in patients with unilateral moderate-to-severe carotid stenosis undergoing CEA. Our findings indicated that the patients with larger carotid IPH could expect smaller improvement in CBF following CEA.

PurposeThis project aimed to assess the significance of vascular endothelial growth factor (VEGF) and p53 for predicting progression-free survival (PFS) in patients with spinal giant cell tumor of bone (GCTB) and to construct models for predicting these two biomarkers based on clinical and computer tomography (CT) radiomics to identify high-risk patients for improving treatment.Material and MethodsA retrospective study was performed from April 2009 to January 2019. A total of 80 patients with spinal GCTB who underwent surgery in our institution were identified. VEGF and p53 expression and clinical and general imaging information were collected. Multivariate Cox regression models were used to verify the prognostic factors. The radiomics features were extracted from the regions of interest (ROIs) in preoperative CT, and then important features were selected by the SVM to build classification models, evaluated by 10-fold crossvalidation. The clinical variables were processed using the same method to build a conventional model for comparison.ResultsThe immunohistochemistry of 80 patients was obtained: 49 with high-VEGF and 31 with low-VEGF, 68 with wild-type p53, and 12 with mutant p53. p53 and VEGF were independent prognostic factors affecting PFS found in multivariate Cox regression analysis. For VEGF, the Spinal Instability Neoplastic Score (SINS) was greater in the high than low groups, p

BackgroundRecently, the Turing test has been used to investigate whether machines have intelligence similar to humans. Our study aimed to assess the ability of an artificial intelligence (AI) system for spine tumor detection using the Turing test.MethodsOur retrospective study data included 12179 images from 321 patients for developing AI detection systems and 6635 images from 187 patients for the Turing test. We utilized a deep learning-based tumor detection system with Faster R-CNN architecture, which generates region proposals by Region Proposal Network in the first stage and corrects the position and the size of the bounding box of the lesion area in the second stage. Each choice question featured four bounding boxes enclosing an identical tumor. Three were detected by the proposed deep learning model, whereas the other was annotated by a doctor; the results were shown to six doctors as respondents. If the respondent did not correctly identify the image annotated by a human, his answer was considered a misclassification. If all misclassification rates were >30%, the respondents were considered unable to distinguish the AI-detected tumor from the human-annotated one, which indicated that the AI system passed the Turing test.ResultsThe average misclassification rates in the Turing test were 51.2% (95% CI: 45.7%–57.5%) in the axial view (maximum of 62%, minimum of 44%) and 44.5% (95% CI: 38.2%–51.8%) in the sagittal view (maximum of 59%, minimum of 36%). The misclassification rates of all six respondents were >30%; therefore, our AI system passed the Turing test.ConclusionOur proposed intelligent spine tumor detection system has a similar detection ability to annotation doctors and may be an efficient tool to assist radiologists or orthopedists in primary spine tumor detection.

Objectives: To determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine. Methods: In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7–152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation. Results: Of the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78. Conclusions: The radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.

Abstract Background Widespread cortical gray matter alternations in people with schizophrenia are correlated with both psychotic symptoms and cognitive/behavioral abnormalities, including the impairments of exploratory eye movement (EEM). Particularly, the loss of gray matter density is specifically related to deficits of the responsive search score (RSS) of EEM in schizophrenia. It is unknown, however, whether the schizophrenia-related RSS deficits are associated with certain psychotic symptoms, such as hallucinations. Methods In 33 participants with schizophrenia, the measurement of EEM, assessment of the hallucination severity using Positive and Negative Syndrome Scale (PANSS) and a voxel-based morphometric analysis of cortical gray matter volume (GMV) were conducted to investigate the relationships between the RSS of EEM, symptom severity, and GMV. In 29 matched healthy controls, the measurement of EEM and a voxel-based morphometric analysis of cortical GMV were also conducted to investigate the relationship between the RSS of EEM and GMV. Results In participants with schizophrenia, the hallucination severity was significantly negatively correlated with both the RSS and the GMV of a large number of brain regions in the frontal, temporal, parietal, orbitofrontal, calcarine, cingulate, and insular cortices, and rolandic operculum, hippocampus, parahippocampal gyrus, and thalamus. Also in participants with schizophrenia, the RSS was significantly positively correlated with the GMV in the left supplementary motor area (SMA), left superior frontal cortex (SFG), bilateral precentral gyri, bilateral postcentral gyri, and bilateral middle frontal cortices. More importantly, the GMV of the SMA, SFG, and precentral gyrus in the left hemisphere was not only significantly negatively correlated with the hallucination severity but also significantly positively correlated with the RSS. No significant correlation could be revealed between the RSS and the GMV of any brain regions in healthy controls. Conclusions There was a significantly negative association between the hallucination severity and the RSS of EEM, suggesting that the RSS may be a potential biomarker for predicting the hallucination severity of schizophrenia. Also, the GMV of the left SMA, SFG, and precentral gyrus may be the common substrates underlying both hallucination induction and the RSS in people with schizophrenia.

Background: Langerhans cell histiocytosis (LCH) may affect atlas and axis, and there were very few published cases describing a characteristic of LCH of atlantoaxial. Objective: The objective of the study is to investigate the image manifestations of atlantoaxial LCH to improve the in-depth comprehension on it. Materials and Methods: A retrospective study was done of computed tomography (CT) and magnetic resonance imaging in atlas and axis and prognosis was analyzed. Results: The study included 41 patients (average age 12.9 years and median age 8 years) diagnosed with LCH, with 75.6% under 15 years old. Eighty-four lesions of LCH were identified including 47 in the atlas and 37 in the axis. The osteolytic bone destructions in the atlas and axis were characterized, 22% accompanied by sclerotic margins. Thirteen patients had a compression fracture, 11 in the lateral mass of the atlas and 2 in the C2 vertebral body. Sixteen and three patients had atlantoaxial malalignment and dislocation, respectively. On T2-weighted images, 68.9% showed iso- or low-signal intensity, 27.6% showed hyperintensity signal, and 3.4% showed heterogeneous signal. On postcontrast images, 81.9% showed significant enhancement, 12.5% showed moderate enhancement, and 6.3% showed mild enhancement. CT reexamination of 14 patients indicated atlantoaxial bone destruction relatively repaired in 12 patients. Thirty-three patients were a follow-up, 81.8% had no significant symptoms and 18.2% with remaining symptoms. Conclusions: The atlas and axis were affected by LCH, mainly in children. The lateral mass was easily affected and compressed, destruction of the atlas and axis could lead to atlantoaxial joint instability. The prognosis was good in most of the patients.

BACKGROUND: Shared neuropathological features between schizophrenic patients and their first-degree relatives have potential as indicators of genetic vulnerability to schizophrenia. We sought to explore genetic influences on brain morphology and function in schizophrenic patients and their relatives. METHODS: Using a multimodal imaging strategy, we studied 33 schizophrenic patients, 55 of their unaffected parents, 30 healthy controls for patients, and 29 healthy controls for parents with voxel-based morphometry of structural MRI scans and functional connectivity analysis of resting-state functional MRI data. RESULTS: Schizophrenic patients showed widespread gray matter reductions in the bilateral frontal cortices, bilateral insulae, bilateral occipital cortices, left amygdala and right thalamus, whereas their parents showed more localized reductions in the left amygdala, left thalamus and right orbitofrontal cortex. Patients and their parents shared gray matter loss in the left amygdala. Further investigation of the resting-state functional connectivity of the amygdala in the patients showed abnormal functional connectivity with the bilateral orbitofrontal cortices, bilateral precunei, bilateral dorsolateral frontal cortices and right insula. Their parents showed slightly less, but similar changes in the pattern in the amygdala connectivity. Co-occurrences of abnormal connectivity of the left amygdala with the left orbitofrontal cortex, right dorsolateral frontal cortex and right precuneus were observed in schizophrenic patients and their parents. CONCLUSIONS: Our findings suggest a potential genetic influence on structural and functional abnormalities of the amygdala in schizophrenia. Such information could help future efforts to identify the endophenotypes that characterize the complex disorder of schizophrenia.

Schizophrenic patients present abnormalities in a variety of eye movement tasks. Exploratory eye movement (EEM) dysfunction appears to be particularly specific to schizophrenia. However, the underlying mechanisms of EEM dysfunction in schizophrenia are not clearly understood. To assess the potential neuroanatomical substrates of EEM, we recorded EEM performance and conducted a voxel-based morphometric analysis of gray matter in 33 schizophrenic patients and 29 well matched healthy controls. In schizophrenic patients, decreased responsive search score (RSS) and widespread gray matter density (GMD) reductions were observed. Moreover, the RSS was positively correlated with GMD in distributed brain regions in schizophrenic patients. Furthermore, in schizophrenic patients, some brain regions with neuroanatomical deficits overlapped with some ones associated with RSS. These brain regions constituted an occipito-tempro-frontal circuitry involved in visual information processing and eye movement control, including the left calcarine cortex [Brodmann area (BA) 17], the left cuneus (BA 18), the left superior occipital cortex (BA 18/19), the left superior frontal gyrus (BA 6), the left cerebellum, the right lingual cortex (BA 17/18), the right middle occipital cortex (BA19), the right inferior temporal cortex (BA 37), the right dorsolateral prefrontal cortex (BA 46) and bilateral precentral gyri (BA 6) extending to the frontal eye fields (FEF, BA 8). To our knowledge, we firstly reported empirical evidence that gray matter loss in the occipito-tempro-frontal neuroanatomical circuitry of visual processing system was associated with EEM performance in schizophrenia, which may be helpful for the future effort to reveal the underlying neural mechanisms for EEM disturbances in schizophrenia.