Journal of Magnetic Resonance Imaging最新文献

Background: The brain aging in the general population and patients with neurological disorders is not well understood.

Purpose: To characterize brain aging in the above conditions and its clinical relevance.

Study type: Retrospective.

Population: A total of 2913 healthy controls (HC), with 1395 females; 331 multiple sclerosis (MS); 189 neuromyelitis optica spectrum disorder (NMOSD); 239 Alzheimer's disease (AD); 244 Parkinson's disease (PD); and 338 cerebral small vessel disease (cSVD).

Field strength/sequence: 3.0 T/Three-dimensional (3D) T1-weighted images.

Assessment: The brain age was estimated by our previously developed model, using a 3D convolutional neural network trained on 9794 3D T1-weighted images of healthy individuals. Brain age gap (BAG), the difference between chronological age and estimated brain age, was calculated to represent accelerated and resilient brain conditions. We compared MRI metrics between individuals with accelerated (BAG ≥ 5 years) and resilient brain age (BAG ≤ -5 years) in HC, and correlated BAG with MRI metrics, and cognitive and physical measures across neurological disorders.

Statistical tests: Student's t test, Wilcoxon test, chi-square test or Fisher's exact test, and correlation analysis. P < 0.05 was considered statistically significant.

Results: In HC, individuals with accelerated brain age exhibited significantly higher white matter hyperintensity (WMH) and lower regional brain volumes than those with resilient brain age. BAG was significantly higher in MS (10.30 ± 12.6 years), NMOSD (2.96 ± 7.8 years), AD (6.50 ± 6.6 years), PD (4.24 ± 4.8 years), and cSVD (3.24 ± 5.9 years) compared to HC. Increased BAG was significantly associated with regional brain atrophy, WMH burden, and cognitive impairment across neurological disorders. Increased BAG was significantly correlated with physical disability in MS (r = 0.17).

Data conclusion: Healthy individuals with accelerated brain age show high WMH burden and regional volume reduction. Neurological disorders exhibit distinct accelerated brain aging, correlated with impaired cognitive and physical function.

Level of evidence: 4 TECHNICAL EFFICACY: Stage 2.

Fetal cardiovascular magnetic resonance imaging (MRI) has emerged as a complementary modality for prenatal imaging in suspected congenital heart disease. Ongoing technical improvements extend the potential clinical value of fetal cardiovascular MRI. Ascertaining equivocal prenatal diagnostics obtained with ultrasonography allows for appropriate parental counseling and planning of postnatal surgery. This work summarizes current acquisition techniques and clinical applications of fetal cardiovascular MRI in the prenatal diagnosis and follow-up of fetuses with congenital heart disease. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Background: Epilepsy is the most common preoperative symptom in patients with supratentorial gliomas. Identifying tumor locations and clinical factors associated with preoperative epilepsy is important for understanding seizure risk.

Purpose: To investigate the key brain areas and risk factors associated with preoperative seizures in glioma patients.

Study type: Retrospective.

Population: A total of 735 patients with primary diffuse supratentorial gliomas (372 low grade; 363 high grade) with preoperative MRI and pathology data.

Field strength/sequence: Axial T2-weighted fast spin-echo sequence at 3.0 T.

Assessment: Seizure burden was defined as the number of preoperative seizures within 6 months. Tumor and high-signal edema areas on T2 images were considered involved regions. A voxel-based lesion-symptom mapping analysis was used to identify voxels associated with seizure burden. The involvement of peak voxels (those most associated with seizure burden) and clinical factors were assessed as risk factors for preoperative seizure.

Statistical tests: Univariable and multivariable binary and ordinal logistic regression analyses and chi-square tests were performed, with results reported as odds ratios (ORs) and 95% confidence intervals. A P-value <0.05 was considered significant.

Results: A total of 448 patients experienced preoperative seizures. Significant seizure burden-related voxels were located in the right hippocampus and left insular cortex (based on 1000 permutation tests), with significant differences observed in both low- and high-grade tumors. Tumor involvement in the peak voxel region was an independent risk factor for an increased burden of preoperative seizures (OR = 6.98). Additionally, multivariable binary logistic regression results indicated that 1p/19q codeletion (OR = 1.51), intermediate tumor volume (24.299-97.066 cm³), and involvement of the peak voxel (OR = 6.06) were independent risk factors for preoperative glioma-related epilepsy.

Conclusion: Voxel areas identified through voxel-based lesion-symptom mapping analysis, along with clinical factors, show associations with clinical seizure burden, offering insights for assessing seizure burden for glioma patients.

Level of evidence: 4 TECHNICAL EFFICACY: Stage 1.

Background: Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences.

Purpose: To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance.

Study type: Retrospective.

Population: Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa).

Field strength/sequence: 3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image.

Assessment: CsPCa regions based on biopsy results served as the reference standard. Ten less-experienced (<500 prostate MRIs) and six experienced (>1000 prostate MRIs) radiologists reviewed each case twice using Prostate Imaging Reporting and Data System v2.1, with and without AI, separated by 4-week intervals. Cases were equally distributed among less-experienced radiologists, and 90 cases were randomly assigned to each experienced radiologist. Reading time and diagnostic confidence were assessed.

Statistical tests: Area under the curve (AUC), sensitivity, specificity, reading time, and diagnostic confidence were compared using the DeLong test, Chi-squared test, Fisher exact test, or Wilcoxon rank-sum test between the two sessions. A P-value <0.05 was considered significant. Adjusting threshold using Bonferroni correction was performed for multiple comparisons.

Results: For less-experienced radiologists, AI assistance significantly improved lesion-level sensitivity (0.78 vs. 0.88), sextant-level AUC (0.84 vs. 0.93), and patient-level AUC (0.84 vs. 0.89). For experienced radiologists, AI assistance only improved sextant-level AUC (0.82 vs. 0.91). AI assistance significantly reduced median reading time (250 s [interquartile range, IQR: 157, 402] vs. 130 s [IQR: 88, 209]) and increased diagnostic confidence (5 [IQR: 4, 5] vs. 5 [IQR: 4, 5]) irrespective of experience and enhanced consistency among experienced radiologists (Fleiss κ: 0.53 vs. 0.61).

Data conclusion: AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.

THE CLINICAL APPLICATION OF VARIOUS IMAGING METHODS IN HS DETECTION. A 39-YEAR-OLD FEMALE WITH EPILEPSY SEIZURE FOR MORE THAN 10 YEARS. V-EEG SHOWED MANY EPILEPTIC WAVES IN THE RIGHT PREFRONTAL AND ANTERIOR MIDDLE TEMPORAL REGIONS. (A) MRI T1WI SHOWED MARKED ATROPHY OF THE RIGHT HIPPOCAMPUS. (B, C) MRI T2WI AND FLAIR SHOWED THE RIGHT HIPPOCAMPUS HYPERINTENSITY. (D) INTERICTAL PET SCAN SHOWED HYPOMETABOLIC AREAS IN THE RIGHT TEMPORAL HIPPOCAMPUS. (E) PET/MRI SHOWED THE LESION IN THE RIGHT HIPPOCAMPUS. (F) UAI BY SHANGHAI UNITED IMAGING INTELLIGENT MEDICAL TECHNOLOGY CO. ALSO SHOWED MARKED ATROPHY OF THE RIGHT HIPPOCAMPUS. THE PATIENT UNDERWENT RIGHT ATL. THE PATHOLOGY CONFIRMED HS. BY YAO ET AL. (2309-2331)

Background: The risk of biochemical recurrence (BCR) in prostate cancer (PCa) is typically assessed using D'Amico score. However, iron and fat content in PCa are closely related to tumor cell proliferation and the risk of BCR may be estimated using multiparametric MRI (mpMRI).

Purpose: To noninvasively estimate fat and iron content in PCa and to evaluate their utility in enhancing D'Amico scores for predicting BCR in PCa patients.

Study type: Prospective.

Subjects: Forty-eight male patients in the BCR group (age 71.31 ± 5.74 years) and 27 male patients in the non-BCR group (age 70.3 ± 6.04 years).

Field strength/sequence: 3.0 T, Turbo-spin echo T2-weighted imaging, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) imaging, Gradient echo Q-Dixon sequence.

Assessment: The mean fat fraction (FF) and T2* values of lesions were extracted from the FF map and the T2* map. Additionally, prostate volume, mean apparent diffusion coefficient (ADC) value, periprostatic fat thickness (PPFT), subcutaneous fat thickness (SFT), blood lipid content, pre- and post-operative prostate-specific antigen (PSA) values were collected.

Statistical tests: Stepwise-COX regression analysis was employed to identify the significant predictors of BCR, which led to the construction of an improvement-adjusted (IA) model. Then the IA model as well as the D'Amico score were evaluated using C-index and time-dependent AUC, decision-curve analysis, and Kaplan-Meier curve. P < 0.05 was statistically significant.

Results: Significant differences were observed in PSA, D'Amico score, ISUP grade, T2*, FF, and ADC values of the lesions in the BCR group compared with the non-BCR group. Mean T2*, FF, and ADC values of the lesions were screened to construct the IA model incorporated into the D'Amico score (IA Model: C-index = 0.749; AUC = 0.812; D'Amico score: C-index = 0.672; AUC = 0.723).

Data conclusion: This study demonstrated that mpMRI can quantitatively estimate fat and iron within PCa lesions. By integrating ADC, FF, and T2* values into the D'Amico score, the preoperative-risk assessment for BCR can be improved.

Evidence level: 2 TECHNICAL EFFICACY: Stage 2.