Journal of Magnetic Resonance Imaging最新文献

Background: Diffusion-weighted imaging (DWI) is routinely used in brain tumor surgery guided by intraoperative MRI (IoMRI). However, conventional echo planar imaging DWI (EPI-DWI) is susceptible to distortion and artifacts that affect image quality. Turbo spin echo DWI (TSE-DWI) is an alternative technique with minimal spatial distortions that has the potential to be the radiologically preferred sequence.

Purpose: To compare via single- and multisequence assessment EPI-DWI and TSE-DWI in the IoMRI setting to determine whether there is a radiological preference for either sequence.

Study type: Retrospective.

Population: Thirty-four patients (22 female) aged 2-61 years (24 under 18 years) undergoing IoMRI during surgical resection of intracranial tumors.

Field strength/sequence: 3-T, EPI-DWI, and TSE-DWI.

Assessment: Patients were scanned with EPI- and TSE-DWI as part of the standard IoMRI scanning protocol. A single-sequence assessment of spatial distortion and image artifact was performed by three neuroradiologists blinded to the sequence type. Images were scored regarding distortion and artifacts, around and remote to the resection cavity. A multisequence radiological assessment was performed by three neuroradiologists in full radiological context including all other IoMRI sequences from each case. The DWI images were directly compared with scorings of the radiologists on which they preferred with respect to anatomy, abnormality, artifact, and overall preference.

Statistical tests: Wilcoxon signed-rank tests for single-sequence assessment, weighted kappa for single and multisequence assessment. A P-value <0.001 was considered statistically significant.

Results: For the blinded single-sequence assessment, the TSE-DWI sequence was scored equal to or superior to the EPI-DWI sequence for distortion and artifacts, around and remote to the resection cavity for every case. In the multisequence assessment, all radiologists independently expressed a preference for TSE-DWI over EPI-DWI sequences on viewing brain anatomy, abnormalities, and artifacts.

Data conclusion: The TSE-DWI sequences may be favored over EPI-DWI for IoMRI in patients with intracranial tumors.

Level of evidence: 2 TECHNICAL EFFICACY: Stage 5.

Background: No study has assessed myocardial T1 and T2 values in patients with beta-thalassemia intermedia (β-TI).

Purpose: To assess the prevalence of myocardial involvement in β-TI patients by T2* relaxometry and native T1 and T2 mapping and to determine the correlation of myocardial relaxation times with demographic and clinical parameters.

Study type: Prospective matched-cohort study.

Subjects: 42 β-TI patients (27 females, 39.65 ± 12.32 years), enrolled in the Extension-Myocardial Iron Overload in Thalassaemia Network, and 42 age- and sex-matched healthy volunteers (27 females, 40.01 ± 11.36 years) and thalassemia major (TM) patients (27 females, 39.27 ± 11.57 years).

Field strength/sequence: 1.5 T/multi-echo gradient echo, modified Look-Locker inversion recovery, multi-echo fast-spin-echo, cine balanced steady-state-free precession, and late gadolinium enhancement (LGE) sequences.

Assessment: Hepatic, pancreatic, and left ventricular (LV) T2* values, LV native T1 and T2 values, biventricular ejection fractions and volumes, and presence and extent of replacement myocardial fibrosis.

Statistical tests: Comparisons between two groups were performed with two-sample t tests, Wilcoxon's signed rank tests, or χ² testing. Correlation analysis was performed using Pearson's or Spearman's test. P < 0.05 was considered statistically significant.

Results: β-TI patients had significantly higher LV T2 values than healthy subjects (56.84 ± 4.03 vs. 52.46 ± 2.50 msec, P < 0.0001) and significantly higher LV T1 values than TM patients (1018.32 ± 48.94 vs. 966.66 ± 66.47 msec, P < 0.0001). In β-TI, female gender was associated with significantly increased LV T1 (P = 0.041) and T2 values (P < 0.0001), while splenectomy and presence of regular transfusions were associated with significantly lower LV T1 values (P = 0.014 and P = 0.001, respectively). In β-TI patients, all LV relaxation times were significantly correlated with each other (T2*-T1: P = 0.003; T2*-T2: P = 0.003; T1-T2: P < 0.0001). Two patients with a reduced LV T2* also had a reduced LV T1, while only one had a reduced LV T2. Three patients had a reduced LV T1 but a normal LV T2*; 66.7% of the patients had an increased LV T2. All LV relaxation times were significantly correlated with pancreas T2* values (T2*: P = 0.033; T1: P < 0.0001; T2: P = 0.014). No LV relaxation time was associated (P > 0.05) with hepatic iron concentration, biventricular function parameters, or LGE presence.

Conclusion: The combined use of all three myocardial relaxation times has potential to improve sensitivity in the detection of early/subclinical myocardial involvement in β-Tl patients.

Level of evidence: 2 TECHNICAL EFFICACY: Stage 2.

Background: Automated approaches may allow for fast, reproducible clinical assessment of cardiovascular diseases from MRI.

Purpose: To develop an MRI-based deep learning (DL) disease classification algorithm to distinguish among normal subjects (NORM), patients with dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and ischemic heart disease (IHD).

Study type: Retrospective.

Population: A total of 1337 subjects (55% female), comprising normal subjects (N = 568), and patients with DCM (N = 151), HCM (N = 177), and IHD (N = 441).

Field strength/sequence: Balanced steady-state free precession cine sequence at 1.5/3.0 T.

Assessment: Bi-ventricular morphological and functional features and global and segmental left ventricular strain features were automatically extracted from short- and long-axis cine images. Variational autoencoder models were trained on the extracted features and compared against consensus disease label provided by two expert readers (13 and 14 years of experience). Adding unlabeled, normal data to the training was explored to increase specificity of NORM class.

Statistical tests: Tenfold cross-validation for model development; mean, standard deviation (SD) for measurements; classification metrics: area under the curve (AUC), confusion matrix, accuracy, specificity, precision, recall; 95% confidence intervals; Mann-Whitney U test for significance.

Results: AUCs of 0.952 for NORM, 0.881 for DCM, 0.908 for HCM, and 0.856 for IHD and overall accuracy of 0.778 were obtained, with specificity of 0.908 for the NORM class using both SAX and LAX features. Longitudinal strain features slightly improved classification metrics by 0.001 to 0.03 points, except for HCM-AUC. Differences in accuracy, metrics for NORM class and HCM-AUC were statistically significant. Cotraining using unlabeled data increased the specificity for the NORM class to 0.961.

Data conclusion: Cardiac function features automatically extracted from cine MRI have potential to be used for disease classification, especially for normal-abnormal classification. Feature analyses showed that strain features were important for disease labeling. Cotraining using unlabeled data may help to increase specificity for normal-abnormal classification.

Level of evidence: 3 TECHNICAL EFFICACY: Stage 1.

Nuclear overhauser enhancement is a confounding factor arising from the in vivo application of a chemical exchange saturation transfer technique in which two nuclei in close proximity undergo dipole cross-relaxation. Several studies have shown applicability and efficacy of nuclear overhauser enhancement in observing tumors and other lesions in vivo. Thus, this effect could become an emerging molecular imaging research tool for many diseases. Moreover, nuclear overhauser enhancement has the advantages of simplicity, noninvasiveness, and high resolution and has become a major focus of current research. In this review, we summarize the principles and applications of nuclear overhauser enhancement. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

An increasing number of evidence suggests that bidirectional communication between the cardiovascular system and the central nervous system (CNS), known as the heart-brain interaction, is crucial in understanding the impact of coronary artery disease (CAD) on brain health. The multifactorial role of CAD in the brain involves processes such as inflammation, oxidative stress, neuronal activity, neuroendocrine imbalances, and reduced cerebral perfusion, leading to various cerebral abnormalities. The mechanisms underlying the relationship between CAD and brain injury are complex and involve parallel pathways in the CNS, endocrine system, and immune system. Although the exact mechanisms remain partially understood, neuroimaging techniques offer valuable insights into subtle cerebral abnormalities in CAD patients. Neuroimaging techniques, including assessment of neural function, brain metabolism, white matter microstructure, and brain volume, provide information on the evolving nature of CAD-related cerebral abnormalities over time. This review provides an overview of the pathophysiological mechanisms of CAD in the heart-brain interaction and summarizes recent neuroimaging studies utilizing multiparametric techniques to investigate brain abnormalities associated with CAD. The application of advanced neuroimaging, particularly functional, diffusion, and perfusion advanced techniques, offers high resolution, multiparametric capabilities, and high contrast, thereby allowing for the early detection of changes in brain structure and function, facilitating further exploration of the intricate relationship between CAD and brain health. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Background: Traditional neuroimaging studies have primarily emphasized analysis at the group level, often neglecting the specificity at the individual level. Recently, there has been a growing interest in individual differences in brain connectivity. Investigating individual-specific connectivity is important for understanding the mechanisms of major depressive disorder (MDD) and the variations among individuals.

Purpose: To integrate individualized functional connectivity and structural connectivity with machine learning techniques to distinguish people with MDD and healthy controls (HCs).

Study type: Prospective.

Subjects: A total of 182 patients with MDD and 157 HCs and a verification cohort including 54 patients and 46 HCs.

Field strength/sequence: 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and diffusion tensor imaging with single-shot spin echo.

Assessment: Functional and structural brain networks from rs-fMRI and DTI data were constructed, respectively. Based on these networks, individualized functional connectivity (IFC) and individualized structural connectivity (ISC) were extracted using common orthogonal basis extraction (COBE). Subsequently, multimodal canonical correlation analysis combined with joint independent component analysis (mCCA + jICA) was conducted to fusion analysis to identify the joint and unique independent components (ICs) across multiple modes. These ICs were utilized to generate features, and a support vector machine (SVM) model was implemented for the classification of MDD.

Statistical tests: The differences in individualized connectivity between patients and controls were compared using two-sample t test, with a significance threshold set at P < 0.05. The established model was tested and evaluated using the receiver operating characteristic (ROC) curve.

Results: The classification performance of the constructed individualized connectivity feature model after multisequence fusion increased from 72.2% to 90.3%. Furthermore, the prediction model showed significant predictive power for assessing the severity of depression in patients with MDD (r = 0.544).

Data conclusion: The integration of IFC and ISC through multisequence fusion enhances our capacity to identify MDD, highlighting the advantages of the individualized approach and underscoring its significance in MDD research.

Level of evidence: 1 TECHNICAL EFFICACY: Stage 2.