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Background: Bone erosion in the sacroiliac joint (SIJ) is highly specific for the diagnosis of axial spondyloarthritis (axSpA) and may indicate early disease progression. The 3D ultrashort echo time (3D-UTE) technique excels in providing clear contrast between the articular cartilage and the bone cortex interface. Additionally, it is emerging as a promising quantitative tool for detecting early cartilage changes. Therefore, this study aimed to evaluate the diagnostic performance of 3D-UTE sequences in identifying bone erosion in the SIJ of patients with axSpA and to clarify the potential of cartilage T2* values as a quantitative biomarker for axSpA. Methods: This prospective study employed convenience and consecutive sampling methods to recruit patients diagnosed with axSpA in Peking University Third Hospital who met the Assessment of Spondyloarthritis International Society (ASAS) criteria and also an equal number of healthy volunteers. After providing informed consent, all participants underwent 3D-UTE sequences and conventional T2* mapping of the SIJs. Two radiologists separately interpreted the bone erosion of each SIJ on 3D-UTE sequences. Erosion detection of SIJs via computed tomography (CT) served as the standard of reference. The T2* values of the cartilage were measured and compared, and the diagnostic efficacy of the T2* value for axSpA diagnosis was evaluated. Results: A total of 32 patients and 32 healthy volunteers were included. The 3D-UTE sequence, as separately assessed by two reviewers in terms of its ability to detect erosions, exhibited a notable level of accuracy. For the two reviewers, the respective diagnostic sensitivities were 94.7% and 92.9%, the specificities were 97.4% and 96.5%, positive predictive values were 96.7% and 95.4%, the negative predictive values were 95.9% and 94.5%, the accuracies were 96.2% and 94.9%, and the areas under the curve (AUCs) were 96.1% and 94.7%. For the detection of erosions, the interreader κ value was 0.949. The T2* values of the SIJ cartilage were significantly higher in patients with axSpA than in healthy volunteers. The intraobserver intraclass correlation coefficients (ICCs) for T2* measurements ranged between 80.5% and 82.2%. Meanwhile, the interobserver ICCs for UTE-T2* and gradient echo T2* measurements were 81.5% and 80.8%, respectively. The AUCs of the UTE-T2* values for discriminating patients with axSpA from the healthy volunteers of the two readers were 73.3% and 71.6%, respectively. Conclusions: 3D-UTE sequences can be used as a reliable morphological imaging technique for detecting bone erosion in the SIJ. Additionally, UTE-T2* values of the cartilage may offer a quantitative method for identifying patients with axSpA.
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The first 1,000 days of human life lay the foundation for brain development and later cognitive growth. However, the developmental rules of the functional connectome during this critical period remain unclear. Using high-resolution, longitudinal, task-free functional magnetic resonance imaging data from 930 scans of 665 infants aged 28 postmenstrual weeks to 3 years, we report the early maturational process of connectome segregation and integration. We show the dominant development of local connections alongside a few global connections, the shift of brain hubs from primary regions to high-order association cortices, the developmental divergence of network segregation and integration along the anterior-posterior axis, the prediction of neurocognitive outcomes, and their associations with gene expression signatures of microstructural development and neuronal metabolic pathways. These findings advance our understanding of the principles of connectome remodeling during early life and its neurobiological underpinnings and have implications for studying typical and atypical development.
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Encéfalo , Conectoma , Imageamento por Ressonância Magnética , Humanos , Lactente , Masculino , Feminino , Encéfalo/metabolismo , Encéfalo/crescimento & desenvolvimento , Encéfalo/fisiologia , Pré-Escolar , Rede Nervosa/fisiologia , Recém-NascidoRESUMO
Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70 keV and water-iodine image sets (all p < .001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p > .005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70 keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70 keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited.
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Algoritmos , Vértebras Cervicais , Angiografia por Tomografia Computadorizada , Aprendizado Profundo , Disco Intervertebral , Humanos , Angiografia por Tomografia Computadorizada/métodos , Masculino , Feminino , Pessoa de Meia-Idade , Disco Intervertebral/diagnóstico por imagem , Estudos Retrospectivos , Adulto , Vértebras Cervicais/diagnóstico por imagem , Idoso , Artérias Carótidas/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodosRESUMO
PURPOSE: The objective of this study was to evaluate the feasibility of weight-based tube voltage and iodine delivery rate (IDR) for coronary artery CT angiography (CCTA). METHODS: A total of 193 patients (mean age: 58 ± 12 years) with suspected coronary heart disease indicated for CCTA between May and October 2022 were prospectively enrolled. The subjects were divided into five groups according to body weight: < 60 kg, 60 - 69 kg, 70 - 79 kg, 80 - 89 kg, and ≥ 90 kg. The tube voltage and IDR settings of each group were as follows: 70 kVp/0.8 gI/s, 80 kVp/1.0 gI/s, 80 kVp/1.1 gI/s, 100 kVp/1.5 gI/s, and 100 kVp/1.5 gI/s, respectively. Objective image quality data included the CT value and standard deviation (noise) of the aortic root (AR), the proximal left anterior descending branch (LAD), and the distal right coronary artery (RCA), as well as the signal-to-noise ratio and contrast-to-noise ratio of the LAD and RCA. Subjective image quality assessment was performed based on the 18-segment model. Contrast and radiation doses, as well as effective dose (ED), were recorded. All continuous variables were compared using either the one-way ANOVA or the Kruskal-Wallis rank sum test. RESULTS: No significant differences were observed in all objective and subjective parameters of image quality between the groups (P > 0.05). However, significant differences in contrast and radiation doses were observed (P < 0.05). The contrast doses across the weight groups were 27 mL, 35 mL, 38 mL, 53 mL, and 53 mL, respectively, while the ED were 1.567 (1.30, 2.197) mSv, 1.53 (1.373, 1.78) mSv, 2.113 (1.963, 2.256) mSv, 4.22 (3.771, 4.483) mSv, and 4.786 (4.339, 5.536) mSv, respectively. CONCLUSION: Weight-based tube voltage and IDR yielded consistently high image quality, and allowed for further reduction in contrast and radiation exposure during CCTA for coronary artery diseases.
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A dual Multi-Dimensional Integration (dMDI) method was proposed and demonstrated for T2* and R2* mapping. By constructing and jointly using both the original MDI term and an inversed MDI term, T2* and R2* mapping can be performed independently with intrinsic background noise suppression and spike elimination, allowing for high quantitative accuracy and robustness over a wide range of T2*. dMDI was compared to original MDI and curve fitting methods in terms of quantitative specificity, accuracy, reliability and computational efficiency. All methods were tested and compared via simulation and in vivo data. With high signal-to-noise-ratio (SNR), the proposed dMDI method yielded T2*and R2* values similar to curve fitting methods. For low SNR and background noise signals, the dMDI yielded low T2* and R2* values, thus effectively suppressing all background noise. Virtually zero spikes were observed in dMDI T2* and R2* maps in all simulation and imaging results. The dMDI method has the potential to provide improved and reliable T2* and R2* mapping results in routine and SNR-challenging scenarios.
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Algoritmos , Imageamento por Ressonância Magnética , Reprodutibilidade dos Testes , Imageamento por Ressonância Magnética/métodos , Simulação por Computador , Razão Sinal-Ruído , Imagens de FantasmasRESUMO
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.
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PURPOSE: MR arthrography (MRA) is the most accurate method for preoperatively diagnosing superior labrum anterior-posterior (SLAP) lesions, but diagnostic results can vary considerably due to factors such as experience. In this study, deep learning was used to facilitate the preliminary identification of SLAP lesions and compared with radiologists of different seniority. METHODS: MRA data from 636 patients were retrospectively collected, and all patients were classified as having/not having SLAP lesions according to shoulder arthroscopy. The SLAP-Net model was built and tested on 514 patients (dataset 1) and independently tested on data from two other MRI devices (122 patients, dataset 2). Manual diagnosis was performed by three radiologists with different seniority levels and compared with SLAP-Net outputs. Model performance was evaluated by the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), etc. McNemar's test was used to compare performance among models and between radiologists' models. The intraclass correlation coefficient (ICC) was used to assess the radiologists' reliability. p < 0.05 was considered statistically significant. RESULTS: SLAP-Net had AUC = 0.98 and accuracy = 0.96 for classification in dataset 1 and AUC = 0.92 and accuracy = 0.85 in dataset 2. In dataset 1, SLAP-Net had diagnostic performance similar to that of senior radiologists (p = 0.055) but higher than that of early- and mid-career radiologists (p = 0.025 and 0.011). In dataset 2, SLAP-Net had similar diagnostic performance to radiologists of all three seniority levels (p = 0.468, 0.289, and 0.495, respectively). CONCLUSIONS: Deep learning can be used to identify SLAP lesions upon initial MR arthrography examination. SLAP-Net performs comparably to senior radiologists.
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Aprendizado Profundo , Lesões do Ombro , Articulação do Ombro , Humanos , Ombro/diagnóstico por imagem , Artrografia/métodos , Lesões do Ombro/diagnóstico por imagem , Estudos Retrospectivos , Reprodutibilidade dos Testes , Imageamento por Ressonância Magnética/métodos , Articulação do Ombro/diagnóstico por imagem , Articulação do Ombro/patologia , Artroscopia , Sensibilidade e EspecificidadeRESUMO
PURPOSE: To develop a deep learning model to accurately detect anterior cruciate ligament (ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the diagnostic accuracy and efficiency of clinicians. METHODS: A training dataset was built from MRIs acquired from January 2017 to June 2021, including patients with knee symptoms, irrespective of ACL ruptures. An external validation dataset was built from MRIs acquired from January 2021 to June 2022, including patients who underwent knee arthroscopy or arthroplasty. Patients with fractures or prior knee surgeries were excluded in both datasets. Subsequently, a deep learning model was developed and validated using these datasets. Clinicians of varying expertise levels in sports medicine and radiology were recruited, and their capacities in diagnosing ACL injuries in terms of accuracy and diagnosing time were evaluated both with and without artificial intelligence (AI) assistance. RESULTS: A deep learning model was developed based on the training dataset of 22,767 MRIs from 5 centers and verified with external validation dataset of 4,086 MRIs from 6 centers. The model achieved an area under the receiver operating characteristic curve of 0.987 and a sensitivity and specificity of 95.1%. Thirty-eight clinicians from 25 centers were recruited to diagnose 3,800 MRIs. The AI assistance significantly improved the accuracy of all clinicians, exceeding 96%. Additionally, a notable reduction in diagnostic time was observed. The most significant improvements in accuracy and time efficiency were observed in the trainee groups, suggesting that AI support is particularly beneficial for clinicians with moderately limited diagnostic expertise. CONCLUSIONS: This deep learning model demonstrated expert-level diagnostic performance for ACL ruptures, serving as a valuable tool to assist clinicians of various specialties and experience levels in making accurate and efficient diagnoses. LEVEL OF EVIDENCE: Level III, retrospective comparative case series.
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Lesões do Ligamento Cruzado Anterior , Aprendizado Profundo , Humanos , Lesões do Ligamento Cruzado Anterior/diagnóstico por imagem , Lesões do Ligamento Cruzado Anterior/cirurgia , Ligamento Cruzado Anterior , Estudos Retrospectivos , Inteligência Artificial , Imageamento por Ressonância Magnética/métodosRESUMO
BACKGROUND: Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). PURPOSE: To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. STUDY TYPE: Prospective. POPULATION: Post-surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). FIELD STRENGTH/SEQUENCE: 3.0 T/Turbo spin echo T1/T2-weighted, T2*-weighted multiecho gradient echo and dMRI. ASSESSMENT: Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single-factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed-effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. STATISTICAL TESTS: Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single-factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. RESULTS: The single-factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10-1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27-6.04), which was stronger than the other variables. DATA CONCLUSION: The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.
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Doenças da Medula Espinal , Espondilose , Humanos , Masculino , Feminino , Adulto , Pessoa de Meia-Idade , Estudos Prospectivos , Imagem de Tensor de Difusão/métodos , Espondilose/diagnóstico por imagem , Espondilose/cirurgia , Espondilose/patologia , Doenças da Medula Espinal/diagnóstico por imagem , Doenças da Medula Espinal/cirurgia , Imageamento por Ressonância Magnética , Imagem de Difusão por Ressonância Magnética , Vértebras Cervicais/diagnóstico por imagem , Vértebras Cervicais/cirurgia , Resultado do TratamentoRESUMO
Introduction: Sacral 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. Methods: In 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. Results: Between 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 1,052â mm2 (average 906.21 ± 84.04â 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. Conclusions: One-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.
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OBJECTIVE: To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. METHODS: Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. RESULTS: The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses. CONCLUSION: ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. CLINICAL RELEVANCE STATEMENT: 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. KEY POINTS: ⢠AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. ⢠3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. ⢠Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.
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OBJECTIVE: To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries. METHODS: According to arthroscopy findings, 3087 patients were divided into normal, degenerative, and tear groups (groups 0-2). Group 2 was further divided into bursal-side, articular-side, intratendinous, and full-thickness tear groups (groups 2.1-2.4), and external validation was performed with 573 patients. Visual geometry group network 16 (VGG16) was used for preliminary image screening. Then, the rotator cuff multitask learning (RC-MTL) model performed multitask classification (classifiers 1-4). A multistage decision model produced the final output. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis and calculation of related parameters. McNemar's test was used to compare the differences in the diagnostic effects between radiologists and the model. The intraclass correlation coefficient (ICC) was used to assess the radiologists' reliability. p < 0.05 indicated statistical significance. RESULTS: In the in-group dataset, the area under the ROC curve (AUC) of VGG16 was 0.92, and the average AUCs of RC-MTL classifiers 1-4 were 0.99, 0.98, 0.97, and 0.97, respectively. The average AUC of the automated multitask deep learning system for groups 0-2.4 was 0.98 and 0.97 in the in-group and out-group datasets, respectively. The ICCs of the radiologists were 0.97-0.99. The automated multitask deep learning system outperformed the radiologists in classifying groups 0-2.4 in both the in-group and out-group datasets (p < 0.001). CONCLUSION: The MRI-based automated multitask deep learning system performed well in diagnosing SST injuries and is comparable to experienced radiologists. CLINICAL RELEVANCE STATEMENT: Our study established an automated multitask deep learning system to evaluate supraspinatus tendon (SST) injuries and further determine the location of SST tears. The model can potentially improve radiologists' diagnostic efficiency, reduce diagnostic variability, and accurately assess SST injuries. KEY POINTS: ⢠A detailed classification of supraspinatus tendon tears can help clinical decision-making. ⢠Deep learning enables the detailed classification of supraspinatus tendon injuries. ⢠The proposed automated multitask deep learning system is comparable to radiologists.
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BACKGROUND: The predictive value of carotid plaque characteristics for silent stroke (SS) after carotid endarterectomy (CEA) is unclear. OBJECTIVE: To investigate the associations between carotid plaque characteristics and postoperative SS in patients undergoing CEA. STUDY TYPE: Prospective. POPULATION: One hundred fifty-three patients (mean age: 65.4 ± 7.9 years; 126 males) with unilateral moderate-to-severe carotid stenosis (evaluated by CT angiography) referred for CEA. FIELD STRENGTH/SEQUENCE: 3 T, brain-MRI:T2-PROPELLER, T1-/T2-FLAIR, diffusion weighted imaging (DWI) and T2*, carotid-MRI:black-blood T1-/T2W, 3D TOF, Simultaneous Non-contrast Angiography intraplaque hemorrhage. ASSESSMENT: Patients underwent carotid-MRI within 1-week before CEA, and brain-MRI within 48-hours pre-/post-CEA. The presence and size (volume, maximum-area-percentage) of carotid lipid-rich necrotic core (LRNC), intraplaque hemorrhage (Type-I/Type-II IPH) and calcification were evaluated on carotid-MR images. Postoperative SS was assessed from pre-/post-CEA brain DWI. Patients were divided into moderate-carotid-stenosis (50%-69%) and severe-carotid-stenosis (70%-99%) groups and the associations between carotid plaque characteristics and SS were analyzed. STATISTICAL TESTS: Independent t test, Mann-Whitney U-test, chi-square test and logistic regressions (OR: odds ratio, CI: confidence interval). P value <0.05 was considered statistically significant. RESULTS: SS was found in 8 (16.3%) of the 49 patients with moderate-carotid-stenosis and 21 (20.2%) of the 104 patients with severe-carotid-stenosis. In patients with severe-carotid-stenosis, those with SS had significantly higher IPH (66.7% vs. 39.8%) and Type-I IPH (66.7% vs. 38.6%) than those without. The presence of IPH (OR 3.030, 95% CI 1.106-8.305) and Type-I IPH (OR 3.187, 95% CI 1.162-8.745) was significantly associated with SS. After adjustment, the associations of SS with presence of IPH (OR 3.294, 95% CI 1.122-9.669) and Type-I IPH (OR 3.633, 95% CI 1.216-10.859) remained significant. Moreover, the volume of Type-II IPH (OR 1.014, 95% CI 1.001-1.028), and maximum-area-percentage of Type-II IPH (OR 1.070, 95% CI 1.002-1.142) and LRNC (OR 1.030, 95% CI 1.000-1.061) were significantly associated with SS after adjustment. No significant (P range: 0.203-0.980) associations were found between carotid plaque characteristics and SS in patients with moderate-carotid-stenosis. DATA CONCLUSIONS: In patients with unilateral severe-carotid-stenosis, carotid vulnerable plaque MR features, particularly presence and size of IPH, might be effective predictors for SS after CEA. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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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 BlandâAltman 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.
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Aprendizado Profundo , Humanos , Vértebras Cervicais/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Tomografia , Coluna VertebralRESUMO
Background: Arterial 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. Methods: A 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. Results: There 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 < 0.001). Using CTP as the reference standard, smaller bias can be achieved in measuring rCBF by ASL2.0 (-0.02) than ASL1.5 (-0.07) before CEA. In addition, the same bias (0.03) was obtained by ASL2.0 and ASL1.5 after CEA. The bias of ASL2.0_2.0 (0.31) and ASL2.0_1.5 (0.32) on DRCBF measurement was similar, and both were smaller than that of ASL1.5_1.5 (0.60) and ASL1.5_2.0 (0.60). Conclusion: Strong correlation can be found in assessing perioperative cerebral perfusion between ASL and CTP. During perioperative ASL imaging, the PLD of 2.0 s is better than 1.5 s for preoperative scan, and both 1.5 and 2.0 s are suitable for postoperative scan.
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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.
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Articulação do Tornozelo , Tornozelo , Humanos , Articulação do Tornozelo/diagnóstico por imagem , Inteligência Artificial , Projetos Piloto , Imageamento por Ressonância Magnética/métodosRESUMO
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.
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Objectives: Neuromyelitis 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. Methods: Spinal 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. Results: The 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%). Conclusion: By analyzing the number, length, location, and form of the gadolinium enhancements, NMOSD and long-segment DCM could be well-differentiated.
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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.
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OBJECTIVES: To evaluate the image quality and diagnostic performance of AI-assisted compressed sensing (ACS) accelerated two-dimensional fast spin-echo MRI compared with standard parallel imaging (PI) in clinical 3.0T rapid knee scans. METHODS: This prospective study enrolled 130 consecutive participants between March and September 2022. The MRI scan procedure included one 8.0-min PI protocol and two ACS protocols (3.5 min and 2.0 min). Quantitative image quality assessments were performed by evaluating edge rise distance (ERD) and signal-to-noise ratio (SNR). Shapiro-Wilk tests were performed and investigated by the Friedman test and post hoc analyses. Three radiologists independently evaluated structural disorders for each participant. Fleiss κ analysis was used to compare inter-reader and inter-protocol agreements. The diagnostic performance of each protocol was investigated and compared by DeLong's test. The threshold for statistical significance was set at p < 0.05. RESULTS: A total of 150 knee MRI examinations constituted the study cohort. For the quantitative assessment of four conventional sequences with ACS protocols, SNR improved significantly (p < 0.001), and ERD was significantly reduced or equivalent to the PI protocol. For the abnormality evaluated, the intraclass correlation coefficient ranged from moderate to substantial between readers (κ = 0.75-0.98) and between protocols (κ = 0.73-0.98). For meniscal tears, cruciate ligament tears, and cartilage defects, the diagnostic performance of ACS protocols was considered equivalent to PI protocol (Delong test, p > 0.05). CONCLUSIONS: Compared with the conventional PI acquisition, the novel ACS protocol demonstrated superior image quality and was feasible for achieving equivalent detection of structural abnormalities while reducing acquisition time by half. CLINICAL RELEVANCE STATEMENT: Artificial intelligence-assisted compressed sensing (ACS) providing excellent quality and a 75% reduction in scanning time presents significant clinical advantages in improving the efficiency and accessibility of knee MRI for more patients. KEY POINTS: ⢠The prospective multi-reader study showed no difference in diagnostic performance between parallel imaging and AI-assisted compression sensing (ACS) was found. ⢠Reduced scan time, sharper delineation, and less noise with ACS reconstruction. ⢠Improved efficiency of the clinical knee MRI examination by the ACS acceleration.