Individualized driver amplitude in liver MR elastography: a linear regression study.

BACKGROUND: Current liver magnetic resonance elastography (MRE) scans often require adjustments to driver amplitude to produce acceptable images. This could lead to time wastage and the potential loss of an opportunity to capture a high-quality image. PURPOSE: To construct a linear regression model of individualized driver amplitude to improve liver MRE image quality. MATERIAL AND METHODS: Data from 95 liver MRE scans of 61 participants, including abdominal missing volume ratio (AMVR), breath-holding status, the distance from the passive driver on the skin surface to the liver edge (Dd-l), body mass index (BMI), and lateral deflection of the passive driver with respect to the human sagittal plane (Angle α), were continuously collected. The Spearman correlation analysis and lasso regression were conducted to screen the independent variables. Multiple linear regression equations were developed to determine the optimal amplitude prediction model. RESULTS: The optimal formula for linear regression models: driver amplitude (%) = -16.80 + 78.59 × AMVR - 11.12 × breath-holding (end of expiration = 1, end of inspiration = 0) + 3.16 × Dd-l + 1.94 × BMI + 0.34 × angle α, with the model passing the F test (F = 22.455, P <0.001) and R2 value of 0.558. CONCLUSION: The individualized amplitude prediction model based on AMVR, breath-holding status, Dd-l, BMI, and angle α is a valuable tool in liver MRE examination.

Synthetic relaxometry combined with MUSE DWI and 3D-pCASL improves detection of hippocampal sclerosis.

PURPOSE: To investigate the diagnostic value of synthetic MRI combined MUSE DWI and 3D-pCASL in hippocampal sclerosis (HS). METHOD: A total of 30 HS patients participated in the study. At the same time, 51 healthy volunteers were collected as the control group. All patients and healthy volunteers underwent epilepsy MR scanning protocol (including oblique coronal MAGiC, MUSE DWI, and axial 3D-pCASL) at 3.0 T MR scanner.The independent samples T test and Mann-Whitney U test were used to compare the differences of the apparent dispersion coefficient(ADC), cerebral blood flow(CBF) and quantitative parameters, including T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) values, in the hippocampus of the affected side of HS and the contralateral and control groups, respectively. The diagnostic performance was evaluated using binary logistic regression analysis and area under the receiver operating characteristic (ROC) curves (AUC). RESULTS: Significant statistical differences in T1, T2, CBF, and ADC values were observed between the affected hippocampus of HS patients and contralateral and control hippocampus (all P < 0.005). The T2 has higher discrimination abilities compared with other univariable parameters, with the AUC of 0.899. The combined T2, ADC and CBF model had the best diagnostic performance of HS in MTLE patients with AUC, sensitivity and specificity of 0.946, 86.67 %, 93.33 %, respectively. CONCLUSIONS: Relaxometry parameters derived from synthetic MRI contributed to diagnosis of HS. The proposed approach combining T2, ADC and CBF showed a strong diagnostic capability.

Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging.

BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM. METHODS: Seventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity. RESULTS: No clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI]: 0.45,0.91), 78.36%(95%CI: 0.56,1.00) and 61.33%(95%CI: 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists' assessment were66.23%(95% CI: 0.55,0.76), 61.50%(95% CI: 0.43,0.78) and 68.62%(95% CI: 0.55,0.80); 55.84%(95% CI: 0.45,0.66),69.25%(95% CI: 0.50,0.84) and 49.13%(95% CI: 0.36,0.62); 55.84%(95% CI: 0.45,0.66), 69.23%(95% CI: 0.50,0.84) and 47.06%(95% CI: 0.34,0.61), respectively. CONCLUSION: T1CE-based radiomics showed better classification performance compared with radiologists' assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

BACKGROUND: Our study aims to reveal whether the low b-values distribution, high b-values upper limit, and the number of excitation (NEX) influence the accuracy of the intravoxel incoherent motion (IVIM) parameter derived from multi-b-value diffusion-weighted imaging (DWI) in the brain. METHODS: This prospective study was approved by the local Ethics Committee and informed consent was obtained from each participant. The five consecutive multi-b DWI with different b-value protocols (0-3500 s/mm2) were performed in 22 male healthy volunteers on a 3.0-T MRI system. The IVIM parameters from normal white matter (WM) and gray matter (GM) including slow diffusion coefficient (D), fast perfusion coefficient (D*) and perfusion fraction (f) were compared for differences among defined groups with different IVIM protocols by one-way ANOVA. RESULTS: The D* and f value of WM or GM in groups with less low b-values distribution (less than or equal to 5 b-values) were significantly lower than ones in any other group with more low b-values distribution (all P <  0.05), but no significant differences among groups with more low b-values distribution (P > 0.05). In addition, no significant differences in the D, D* and f value of WM or GM were found between group with one and more NEX of low b-values distribution (all P > 0.05). IVIM parameters in normal WM and GM strongly depended on the choice of the high b-value upper limit. CONCLUSIONS: Metrics of IVIM parameters can be affected by low and high b value distribution. Eight low b-values distribution with high b-value upper limit of 800-1000 s/mm2 may be the relatively proper set when performing brain IVIM studies.

Multi-modal MRI Reveals the Neurovascular Coupling Dysfunction in Chronic Migraine.

Previous studies reported that long-term nociceptive stimulation could result in neurovascular coupling (NVC) dysfunction in brain, but these studies were based mainly on unimodal imaging biomarkers, thus could not comprehensively reflect NVC dysfunction. We investigated the potential NVC dysfunction in chronic migraine by exploring the relationship between neuronal activity and cerebral perfusion maps. The Pearson correlation coefficients between these 2 maps were defined as the NVC biomarkers. NVC biomarkers in migraineurs were significantly lower in left inferior parietal gyrus (IPG), left superior marginal gyrus (SMG) and left angular gyrus (AG), but significantly higher in right superior occipital gyrus (SOG), right superior parietal gyrus (SPG), and precuneus. These brain regions were located mainly in parietal or occipital lobes and were related to visual or sensory information processing. ALFF-CBF in right SPG was positively correlated with disease history and that in right precuneus was negatively correlated with migraine persisting time. fALFF-CBF in left SMG and AG were negatively related to headache frequency and positively related to health condition and disease history. In conclusion, multi-modal MRI could be used to detect NVC dysfunction in chronic migraine patients, which is a new method to assess the impact of chronic pain on the brain.