Z-Spectral MRI Quantifies the Mass and Metabolic Activity of Adipose Tissues With Fat-Water-Fraction and Amide-Proton-Transfer Contrasts.

BACKGROUND: Brown adipose tissue (BAT) is metabolically activatable and plays an important role in obesity and metabolic diseases. With reduced fat-water-fraction (FWF) compared with white adipose tissue (WAT), BAT mass and its functional activation may be quantified with Z-spectra MRI, with built-in FWF and the metabolic amide proton transfer (APT) contrasts. PURPOSE: To investigate if Z-spectral MRI can quantify the mass and metabolic activity of adipose tissues. STUDY TYPE: Prospective. SUBJECTS: Seven groups of 8-week-old male rats, including two groups (n = 7 per group) for in vivo MRI study and five groups (n = 5 per group) for ex vivo validation; 12 young and healthy volunteers with 6 male and 6 female. FIELD STRENGTH/SEQUENCE: The 7 T small animal and 3 T clinical systems, T2-weighted imaging, Rapid Acquisition with Relaxation Enhancement (RARE) readout based chemical exchange saturation transfer (CEST) Z-spectral MRI sequence. ASSESSMENT: Quantified FWF and APT from Z-spectra in rats before and after norepinephrine (NE) stimulation and in healthy human subjects; ex vivo measurements of total proteins in BAT from rats. STATISTICAL TESTS: Two-tailed unpaired Student's t-tests and repeated measures ANOVA. P-value <0.05 was considered significant. RESULTS: Decreased FWF (from 39.6% ± 7.2% before NE injection to 16.4% ± 7.2% 120 minutes after NE injection, P < 0.0001) and elevated APT (from 1.1% ± 0.5% before NE injection to 2.9% ± 0.5% 120 minutes after NE injection, P < 0.0001) signals in BAT were observed with in vivo Z-spectral MRI in rats injected with NE at 7 T MRI. At clinical 3 T, Z-spectral MRI was used to quantify the FWF (58.5% ± 7.2% in BAT and 73.7% ± 6.5% in WAT with P < 0.0001) and APT (2.6% ± 0.8% in BAT and 0.9% ± 0.3% in WAT with P < 0.0001) signals in healthy volunteers. APT signals of BAT were negatively correlated with the BMI in humans (r = 0.71). DATA CONCLUSION: Endogenous Z-spectral MRI was demonstrated to simultaneously quantify BAT mass and function based on its FWF and APT contrasts. TECHNICAL EFFICACY STAGE: 1.

Amide proton transfer-weighted MRI for assessing rectal adenocarcinoma T-staging and perineural invasion: a prospective study.

OBJECTIVE: To investigate the value of the pre-operative amide proton transfer-weighted (APTw) MRI to assess the prognostic factors in rectal adenocarcinoma (RA). METHODS: This prospective study ran from January 2022 to September 2023 and consecutively enrolled participants with RA who underwent pre-operative MRI and radical surgery. The APTw signal intensity (SI) values of RA with various tumor (T), node (N) stages, perineural invasion (PNI), and tumor grade were compared by Mann-Whitney U-test or t-test. The receiver operating characteristic curve was used to evaluate the diagnostic performance of the APTw SI values. RESULTS: A total of 51 participants were enrolled (mean age, 58 years ± 10 [standard deviation], 26 men). There were 24 in the T1-T2 stage and 9 with positive PNI. The APTw SI max, 99th, and 95th values were significantly higher in T3-T4 stage tumor than in T1-T2; the median (interquartile range) (M (IQR)) was (4.0% (3.6-4.9%) vs 3.4% (2.9- 4.3%), p = 0.017), (3.7% (3.2-4.1%) vs 3.2% (2.8-3.8%), p = 0.013), and (3.3% (2.8-3.8%) vs 2.9% (2.3-3.5%), p = 0.033), respectively. These indicators also differed significantly between the PNI groups, with the M (IQR) (4.5% (3.6-5.7%) vs 3.7% (3.2-4.2%), p = 0.017), (4.1% (3.4-4.8%) vs 3.3% (3.0-3.9%), p = 0.022), and (3.7% (2.7-4.2%) vs 2.9% (2.6-3.5%), p = 0.045), respectively. CONCLUSION: Pre-operative APTw MRI has potential value in the assessment of T-staging and PNI determination in RA. CLINICAL RELEVANCE STATEMENT: Pre-operative amide proton transfer-weighted MRI provides a quantitative method for noninvasive assessment of T-staging and PNI in RA aiding in precision treatment planning. KEY POINTS: The efficacy of APTw MRI in RA needs further investigation. T3-T4 stage and PNI positive APTw signal intensities were higher than T1-T2 and non-PNI, respectively. APTw MRI provides a quantitative method for assessment of T staging and PNI in RA.

GABAergic imbalance in Parkinson's disease-related depression determined with MEGA-PRESS.

OBJECTIVE: The pathogenesis of depression in patients with Parkinson's disease (PD) is poorly understood. Therefore, this study aimed to explore the changes in γ-aminobutyric acid (GABA) and glutamate plus glutamine (Glx) levels in patients with PD with or without depression determined using MEscher-GArwood Point Resolved Spectroscopy (MEGA-PRESS). MATERIALS AND METHODS: A total of 83 patients with primary PD and 24 healthy controls were included. Patients with PD were categorized into depressed PD (DPD, n = 19) and nondepressed PD (NDPD, n = 64) based on the 17-item Hamilton Depression Rating Scale. All participants underwent T1-weighted imaging and MEGA-PRESS sequence to acquire GABA+ and Glx values. The MEGA-PRESS sequence was conducted using 18.48 mL voxels in the left thalamus and medial frontal cortex. The GABA+, Glx, and creatine values were quantified using Gannet 3.1 software. RESULTS: The GABA+ and Glx values were not significantly disparate between patients with PD and controls in the thalamus and medial frontal cortex. However, the levels of N-acetyl aspartate/creatine and choline/creatine in the left thalamus were significantly lower in patients with PD than in controls (P = .031, P = .009). The GABA+/Water and GABA+/Creatine in the medial frontal cortex were higher in DPD than in NDPD (P = .001, P = .004). The effects of depression on Glx or other metabolite levels were not evident, and no significant difference in metabolite values was noted in the left thalamus among all groups (P > .05). CONCLUSIONS: GABA+ levels increased in the medial frontal cortex in DPD, which may be more closely related to depressive pathology. Thus, alterations in GABAergic function in special brain structures may be related to the clinical manifestations of PD symptoms, and hence mediating this function might help in treating depression in PD.

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.

BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC. MATERIALS AND METHODS: Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined. RESULTS: The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer-Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56). CONCLUSION: This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients' survival, utilizing intratumoral and peritumoral radiomics features.

Second-Order Motion-Compensated Echo-Planar Cardiac Diffusion-Weighted MRI: Usefulness of Compressed Sensitivity Encoding.

BACKGROUND: Cardiac diffusion-weighted imaging (DWI) using second-order motion-compensated spin echo (M2C) can provide noninvasive in-vivo microstructural assessment, but limited by relatively low signal-to-noise ratio (SNR). Echo-planar imaging (EPI) with compressed sensitivity encoding (EPICS) could address these issues. PURPOSE: To combine M2C DWI and EPCIS (M2C EPICS DWI), and compare image quality for M2C DWI. STUDY TYPE: Prospective. POPULATION: Ten ex-vivo hearts, 10 healthy volunteers (females, 5 [50%]; mean ± SD of age, 25 ± 4 years), and 12 patients with diseased hearts (female, 1 [8.3%]; mean ± SD of age, 44 ± 16 years; including coronary artery heart disease, congenital heart disease, dilated cardiomyopathy, amyloidosis, and myocarditis). FIELD STRENGTH/SEQUENCE: 3-T, M2C EPICS DWI, and M2C DWI. ASSESSMENT: The apparent SNR (aSNR) and the rating scores were used to evaluate and compared image quality of all three groups. The aSNR was calculated using aSNR = Mean intensity myocardium / Standard deviation myocardium $$ \mathrm{aSNR}={\mathrm{Mean}\ \mathrm{intensity}}_{\mathrm{myocardium}}/{\mathrm{Standard}\ \mathrm{deviation}}_{\mathrm{myocardium}} $$ , and the myocardium was segmented manually. Three observers independently rated subjective image quality using a 5-point Likert scale. STATISTICAL TESTS: Bland-Altman analysis and paired t-tests. The threshold for statistical significance was set at P < 0.05. RESULTS: In healthy volunteers, the aSNR with a b-value of 450 s/mm2 acquired by M2C EPICS DWI was significantly higher than M2C DWI at in-plane resolutions of 3.0 × 3.0, 2.5 × 2.5, and 2.0 × 2.0 mm2. In patients with diseased hearts, the aSNR ofM2C EPICS DWI was also significantly higher than that for M2C DWI (bias of M2C EPICS-M2C = 1.999, 95% limits of agreement, 0.362 to 3.636; mean ± SD, 7.80 ± 1.37 vs. 5.80 ± 0.81). The ADC values of M2C EPICS was significantly higher than M2C DWI in in-vivo hearts. Over 80% of the images with rating scores for M2C EPICS DWI were higher than M2C DWI in in-vivo hearts. DATA CONCLUSION: Cardiac imaging by M2C EPICS DWI may demonstrate better overall image quality and higher aSNR than M2C DWI. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in distinguishing TNBC versus non-TNBC.

Objective.To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model.Approach.466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively analyzed. Radiomics features were extracted from whole tumor on T2WI, diffusion-weighted imaging, ADC maps and the 2nd phase of dynamic contrast-enhanced MRI. Four models including the RFFmodel (fused features from all MRI sequences), RADCmodel (ADC radiomics feature), StratifiedADCmodel (tumor habitas defined on stratified ADC parameters) and combinational RFF-StratifiedADCmodel were constructed to distinguish TNBC versus non-TNBC. All cases were randomly divided into a training (n= 337) and test set (n= 129). The four competing models were validated using the area under the curve (AUC), sensitivity, specificity and accuracy.Main results.Both the RFFand StratifiedADCmodels demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. StratifiedADCmodel revealed significant different tumor habitats (necrosis/cysts habitat, chaotic habitat or proliferative tumor core) between TNBC and non-TNBC with its top three discriminative parameters (p <0.05). The integrated RFF-StratifiedADCmodel demonstrated superior accuracy over the other three models, with higher AUCs of 0.832 and 0.784 in the training and test set, respectively (p <0.05).Significance.The RFF-StratifiedADCmodel through integrating various tumor habitats' information from whole-tumor ADC maps-based StratifiedADCmodel and radiomics information from mpMRI-based RFFmodel, exhibits tremendous promise for identifying TNBC.

Integrated Diffusion Tensor Imaging and Renal Parenchymal Volume for Early Detection and Grading of Split Renal Functional Impairment in Lupus Nephritis.

RATIONALE AND OBJECTIVES: To investigate the effectiveness of combining split diffusion tensor imaging (DTI) measurements with split renal parenchymal volume (RPV) for assessing split renal functional impairment in patients with lupus nephritis (LN). MATERIALS AND METHODS: Seventy-four participants [48 LN patients and 26 healthy volunteers (HV)] were included in the study. All participant underwent conventional MR and DTI (b = 0, 400, and 600 s/mm2) examinations using a 3.0 T MRI scanner to determine the split renal DTI measurements and split RPV. In LN patients, renography glomerular filtration rate (rGFR) was measured using 99mTc-DTPA scintigraphy based on Gates' method, serving as the reference standard to categorize all split kidneys of LN patients into LN with mild impairment (LNm, n = 65 kidneys) and LN with moderate to severe (LNms, n = 31 kidneys) groups according to the threshold of 30 ml/min in spilt rGFR. All statistical analyses were performed using SPSS 25.0 and MedCalc 20.0 software packages. RESULTS: Only split medullary fractional anisotropy (FA) and the product of split medullary FA and RPV could distinguish pairwise subgroups among the HV and each LN subgroup (all p < 0.05). ROC curve analysis demonstrated that split medullary FA (AUC = 0.866) significantly outperformed other parameters in differentiating HV from LNm groups, while the product of split medullary FA and split RPV was superior in distinguishing LNm and LNms groups (AUC = 0.793) than other parameters. The combination of split medullary FA and split RPV showed best correlation with split rGFR (r = 0.534, p < 0.001). CONCLUSION: Split medullary FA, and its combination with split RPV, are valuable biomarkers for detecting early functional changes in renal alterations and predicting disease progression in patients with LN.

Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists' diagnosis.

Objective: To investigate the performance of a novel feature fusion radiomics (RFF) model that incorporates features from multiparametric MRIs (mpMRI) in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively. Methods: 460 patients with 466 pathology-confirmed BCs who underwent breast mpMRI at 1.5T in our center were retrospectively included hormone receptor (HR) positive (HR+) (n=336) and HR negative (HR-) (n=130). The HR- patients were further categorized into human epidermal growth factor receptor 2 (HER-2) enriched BC (HEBC) (n=76) and triple negative BC (TNBC) (n=54). All lesions were divided into a training/validation cohort (n=337) and a test cohort (n=129). Volumes of interest (VOIs) delineation, followed by radiomics feature extraction, was performed on T2WI, DWI600 (b=600 s/mm2), DWI800 (b=800 s/mm2), ADC map, and DCE1-6 (six continuous DCE-MRI) images of each lesion. Simulating a radiologist's work pattern, 150 classification base models were constructed and analyzed to determine the top four optimum sequences for classifying HR+ vs. HR-, TNBC vs. HEBC, TNBC vs. non-TNBC in a random selected training cohort (n=337). Building upon these findings, the optimal single sequence models (Rss) and combined sequences models (RFF) were developed. The AUC, sensitivity, accuracy and specificity of each model for subtype differentiation were evaluated. The paired samples Wilcoxon signed rank test was used for performance comparison. Results: During the three classification tasks, the optimal single sequence for classifying HR+ vs. HR- was DWI600, while the ADC map, derived from DWI800 performed the best in distinguishing TNBC vs. HEBC, as well as identifying TNBC vs. non-TNBC, with corresponding training AUC values of 0.787, 0.788, and 0.809, respectively. Furthermore, the integration of the top four sequences in RFF models yielded improved performance, achieving AUC values of 0.809, 0.805 and 0.847, respectively. Consistent results was observed in both the training/validation and testing cohorts, with AUC values of 0.778, 0.787, 0.818 and 0.726, 0.773, 0.773, respectively (all p < 0.05 except HR+ vs. HR-). Conclusion: The RFF model, integrating mpMRI radiomics features, demonstrated promising ability to mimic radiologists' diagnosis for preoperative identification of molecular receptors of BC.

Using the non-distortion IVIM to reduce the need for contrast agents in nasopharyngeal MRI.

BACKGROUND: Patients with nasopharyngeal carcinoma (NPC) who undergo longitudinal follow-up contrast-enhanced MRI are at risk of developing gadolinium deposition in their neural tissue, which may potentially harm them. Therefore, for these patients, a non-contrast-enhanced method is potentially beneficial as an alternative approach to predict enhancement in T1-weighted imaging (CE-T1WI). The traditional intravoxel incoherent motion (IVIM) is one of the non-contrast-enhanced methods; however, the severe distortion and signal loss limit its application in patients with NPC. The present study aimed to investigate whether non-distortion IVIM could reduce the need of CE-T1WI in the follow-up of patients with NPC. METHODS: The patients with NPC underwent Turbo Spin-echo MVXD diffusion-weighted imaging-based IVIM (non-distortion IVIM) from November 2021 to May 2022. Firstly, thirty patients with NPC were underwent both non-distortion IVIM and traditional IVIM. The distortion rate (DR) of the non-distortion IVIM was compared with the traditional IVIM. Then, twenty-one NPC patients with tumors (areas >50mm2) were included and correlation coefficient analysis was used to assess the relationship between their non-distortion IVIM and CE-T1WI. Linear regression analysis was performed to determine whether non-distortion IVIM predictors could predict CE-T1WI. RESULTS: The correlation was observed between the parameter f of the non-distortion IVIM and the enhancement ratio of CE-T1WI (r = 0.543, P = 0.011). Moreover, the linear regression analysis revealed that f was an independent IVIM predictor of CE-T1WI in patients with NPC (P = 0.011). The DR of the non-distortion IVIM was significantly smaller than that of the traditional IVIM (0.12 ± 0.05 vs 0.48 ± 0.16, P < 0.001). CONCLUSIONS: In patients with NPC, non-distortion IVIM showed potential clinical benefits to reduce the need for contrast agents, and it can independently predict the enhancement ratio.

The prognostic value of the left atrial strain rate determined using cardiovascular magnetic resonance feature tracking imaging in patients with severe idiopathic dilated cardiomyopathy.

Background: Left atrial strain rate (LASR) is rarely used as an indicator of cardiovascular disorder prognosis in dilated cardiomyopathy (DCM). In the present study, we aimed to explore the prognostic value of the LASR in patients with severe idiopathic DCM [left ventricle ejection fraction (LVEF) <35%] using cardiac magnetic resonance feature tracking (CMR-FT). Methods: A retrospective cohort study was performed. At the time of enrollment, patients who underwent cardiovascular magnetic resonance (CMR) imaging were selected for the study. Left atrium (LA) strain rate was measured in patients with severe idiopathic DCM from four-chamber and two-chamber views using a 3.0-T CMR scanner. The study endpoint was defined as a combination of all-cause death, implantable cardioverter-defibrillator (ICD), hospitalization due to cardiac failure, and heart transplantation. Cox model analyses were used to assess the statistical significance of the risk factors. Results: Fifty-eight patients with a confirmed diagnosis of severe idiopathic DCM were ultimately included in the analysis [mean age of 46 years; 41 (73%) men]. The median follow-up was 43 months (13-72 months). Adverse cardiovascular events occurred in 29 patients: all-cause death occurred in 15 participants (25.8%), ICD was performed in 5 participants (8.6%), heart failure hospitalization occurred in 8 participants (13.8%), and 1 participant (1.7%) underwent heart transplantation. From univariate analysis, the left atrium reservoir strain rate (LASRs) [HR 0.12; 95% CI (0.02, 0.55), P=0.007] and the left atrium booster pump strain rate (LASRa) [HR 3.21; 95% CI (1.08, 9.58), P=0.036] significantly predicted the association with cardiovascular events. In model 1 after adjusting for basic cardiovascular risk factors, including age, hypertension, diabetes mellitus, and N-terminal pro-brain natriuretic peptide (NT-proBNP), the LASRs [HR 0.12, 95% CI (0.02, 0.58), P=0.009] was assessed. The mitral valve regurgitation, LA maximum volume index, and LA diastatic volume index were added in model 2 based on model 1. LASRs [HR 0.13, 95% CI (0.02. 0.82) P=0.030] was the independent prediction factor after adjustment for model 1 and model 2. The LASRs had a good prognostic value with an area under the curve of receiver operating characteristic of 0.697 (0.617, 0.777) at 1 year, 0.716 (0.643, 0.788) at 3 years, 0.716 (0.651, 0.798) at 5 years and 0.703 (0.597, 0.809) at 7 years. Conclusions: LA reservoir strain rate and active strain rate were powerful prognostic markers. The LASRs by MRI-FT provided independent prognostic value in patients with severe idiopathic DCM.