Impact of pretransplant T2DM on left ventricular deformation and myocardial perfusion in heart transplanted recipients: a 3.0 T cardiac magnetic resonance study.

BACKGROUND: Pretransplant type 2 diabetes mellitus (T2DM) is associated with increased cardiovascular and all-cause mortality after heart transplant (HT), but the underlying causes of this association remain unclear. The purpose of this research was to examine the impact of T2DM on left ventricular (LV) myocardial deformation and myocardial perfusion following heart transplantation using cardiovascular magnetic resonance imaging. METHODS: We investigated thirty-one HT recipients with pretransplant T2DM [HT(DM+)], thirty-four HT recipients without pretransplant T2DM [HT(DM-)] and thirty-six controls. LV myocardial strains, including the global longitudinal, radial, and circumferential strain (GLS, GRS and GCS, respectively), were calculated and compared among groups, as were resting myocardial perfusion indices, which included time to peak myocardial signal intensity (TTM), maximum signal intensity (MaxSI), and Upslope. The relationships between LV strain parameters or perfusion indices and biochemical indicators were determined through Spearman's analysis. The impact of T2DM on LV strains in HT recipients was assessed using multivariable linear regression analyses with backward stepwise selection. RESULTS: In the HT(DM+) group, the LV GLS, GRS, and GCS exhibited significantly lower magnitudes than those in both the HT(DM-) and control groups. TTM was higher in the HT(DM+) group than in both the HT(DM-) and control groups, while no significant differences were observed among the groups regarding Upslope and MaxSI. There was a negative correlation between glycated hemoglobin and the magnitude of strains (longitudinal, r = - 0.399; radial, r = - 0.362; circumferential, r = - 0.389) (all P < 0.05), and a positive correlation with TTM (r = 0.485, P < 0.001). Regression analyses that included both pretransplant T2DM and perfusion indices revealed that pretransplant T2DM, rather than perfusion indices, was an independent determinant of LV strain (ß = longitudinal, - 0.508; radial, - 0.370; circumferential, - 0.371) (all P < 0.05). CONCLUSION: In heart transplant recipients, pretransplant T2DM has a detrimental effect on subclinical left ventricular systolic function and could potentially impact myocardial microcirculation following HT.

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.

Ringlike late gadolinium enhancement provides incremental prognostic value in non-classical arrhythmogenic cardiomyopathy.

BACKGROUND: The 2019 arrhythmogenic right ventricular cardiomyopathy (ARVC) risk model has proved insufficient in the capability of predicting ventricular arrhythmia (VA) risk in non-classical arrhythmogenic cardiomyopathy (ACM). Furthermore, the prognostic value of ringlike late gadolinium enhancement (LGE) of the left ventricle in non-classical ACM remains unknown. We aimed to assess the incremental value of ringlike LGE over the 2019 ARVC risk model in predicting sustained VA in patients with non-classical ACM. METHODS: In this retrospective study, consecutive patients with non-classical ACM who underwent CMR from January 2011 to January 2022 were included. The pattern of LGE was categorized as no, non-ringlike, and ringlike LGE. The primary outcome was defined as the occurrence of sustained VA. Univariable and multivariable Cox regression analysis was used to evaluate the impact of LGE patterns on sustained VA and area under curve (AUC) was calculated for the incremental value of ringlike LGE. RESULTS: A total of 73 patients were collected in the final cohort (mean age, 39.3 ± 14.4 years, 51 male), of whom 10 (13.7%) had no LGE, 33 (45.2%) had non-ringlike LGE, and 30 (41.1%) had ringlike LGE. There was no statistically significant difference in the 5-year risk score among the three groups (P = 0.190). During a median follow-up of 34 (13-56) months, 34 (46.6%) patients experienced sustained VA, including 1 (10.0%), 13 (39.4%) and 20 (66.7%) of patients with no, non-ringlike and ringlike LGE, respectively. After multivariable adjustment, ringlike LGE remained independently associated with the presence of sustained VA (adjusted hazard ratio: 6.91, 95% confidence intervals: 1.89-54.60; P = 0.036). Adding ringlike LGE to the 2019 ARVC risk model showed significantly incremental prognostic value for sustained VA (AUC: 0.80 vs. 0.67; P = 0.024). CONCLUSION: Ringlike LGE provides independent and incremental prognostic value over the 2019 ARVC risk model in patients with non-classical ACM.

Sub-minute acquisition with deep learning-based image filter in the diagnosis of colorectal cancers using total-body 18F-FDG PET/CT.

BACKGROUND: This study aimed to retrospectively evaluate the feasibility of total-body 18F-FDG PET/CT ultrafast acquisition combined with a deep learning (DL) image filter in the diagnosis of colorectal cancers (CRCs). METHODS: The clinical and preoperative imaging data of patients with CRCs were collected. All patients underwent a 300-s list-mode total-body 18F-FDG PET/CT scan. The dataset was divided into groups with acquisition durations of 10, 20, 30, 60, and 120 s. PET images were reconstructed using ordered subset expectation maximisation, and post-processing filters, including a Gaussian smoothing filter with 3 mm full width at half maximum (3 mm FWHM) and a DL image filter. The effects of the Gaussian and DL image filters on image quality, detection rate, and uptake value of primary and liver metastases of CRCs at different acquisition durations were compared using a 5-point Likert scale and semi-quantitative analysis, with the 300-s image with a Gaussian filter as the standard. RESULTS: All 34 recruited patients with CRCs had single colorectal lesions, and the diagnosis was verified pathologically. Of the total patients, 11 had liver metastases, and 113 liver metastases were detected. The 10-s dataset could not be evaluated due to high noise, regardless of whether it was filtered by Gaussian or DL image filters. The signal-to-noise ratio (SNR) of the liver and mediastinal blood pool in the images acquired for 10, 20, 30, and 60 s with a Gaussian filter was lower than that of the 300-s images (P < 0.01). The DL filter significantly improved the SNR and visual image quality score compared to the Gaussian filter (P < 0.01). There was no statistical difference in the SNR of the liver and mediastinal blood pool, SUVmax and TBR of CRCs and liver metastases, and the number of detectable liver metastases between the 20- and 30-s DL image filter and 300-s images with the Gaussian filter (P > 0.05). CONCLUSIONS: The DL filter can significantly improve the image quality of total-body 18F-FDG PET/CT ultrafast acquisition. Deep learning-based image filtering methods can significantly reduce the noise of ultrafast acquisition, making them suitable for clinical diagnosis possible.

The comparison of diffusion tensor imaging in human hearts between 1.5 T and 3.0 T.

BACKGROUND: The aim was to compare the diffusion tensor imaging (DTI) indices derived from human hearts between 1.5 T and 3.0 T scanners. Additionally, the reproducibility of DTI indices was assessed between 1.5 T and 3.0 T scanners. METHODS: A total of 18 ex-vivo hearts were derived from patients who underwent heart transplantation. The DTI schemes were performed at 1.5 T and 3.0 T, respectively. Then, the same slices from each ex-vivo heart were selected for image analysis. The student's t-test or Wilcoxon-rank test was used to compare the statistical differences. The agreement of DTI indices was mainly reported as the interclass correlation coefficient (ICC). RESULTS: No significant differences (all P > 0.05) were found in the DTI indices between 1.5 T and 3.0 T scanners. Interestingly, the ICC of all DTI indices was relatively lower with a low b-value. The reproducibility of the helix angle (HA) was relatively lower when compared to the other DTI indices. CONCLUSION: The DTI indices of ex-vivo human hearts between 1.5 T and 3.0 T scanners had no significant differences. The consistency of DTI indices needed caution using a low b-value with different field strengths, and the relatively low reproducibility of HA should be considered.

Multiparametric cardiovascular magnetic resonance characteristics and dynamic changes in asymptomatic heart-transplanted patients.

OBJECTIVES: To describe the dynamic changes in cardiac deformation and tissue characteristics using cardiac magnetic resonance (CMR) in asymptomatic patients during 12 months after heart transplantation (HT). METHODS: From April 2020 to January 2021, 21 consecutive HT patients without clinical symptoms were included in this prospective study. Multiparametric CMR was performed at 3, 6, and 12 months after HT. Twenty-five healthy volunteers served as controls. RESULTS: During follow-up, a decline in left ventricular (LV) global radial strain (GRS) (p = 0.020) and right ventricular (RV) global longitudinal strain (GLS) (p < 0.001) and an increase in post-contrast T1 (p = 0.024) and T2 (p < 0.001) in asymptomatic HT patients occurred at 3 months, which normalized at 6 months postoperatively, compared with those in healthy controls. A decline in LVGLS (p < 0.001) and LV global circumferential strain (GCS) (p < 0.001) and an increase in native T1 (p < 0.001), T2 (p < 0.001), and extracellular volume (ECV) (p < 0.001) occurred at 3 months. Although most parameters improved gradually, LVGLS, native T1, and ECV remained abnormal compared with those in healthy controls at 12 months; only T2 and LVGCS were normalized at 6 months and 12 months, respectively. ECV was significantly correlated with LVGLS, LVGCS, and LVGRS. CONCLUSION: Cardiac deformation and tissue characteristics were abnormal early after HT, although the patients were clinically asymptomatic. The dynamic changes in CMR characteristics demonstrate a gradual recovery of myocardial injury associated with transplantation during the first 12 months after HT. KEY POINTS: • Multiparametric CMR can detect the dynamic changes of transplantation-associated myocardial injury. • Post-contrast T1, T2, LVGRS, and RVGLS values are normalized at 6 months after HT. • Native T1, ECV, and LVGLS values remain abnormal compared with those in healthy controls at 12 months after HT.

Identifying Depression in Parkinson's Disease by Using Combined Diffusion Tensor Imaging and Support Vector Machine.

Objective: To investigate white matter microstructural alterations in Parkinson's disease (PD) patients with depression using the whole-brain diffusion tensor imaging (DTI) method and to explore the DTI-based machine learning model in identifying depressed PD (dPD). Methods: The DTI data were collected from 37 patients with dPD and 35 patients with non-depressed PD (ndPD), and 25 healthy control (HC) subjects were collected as the reference. An atlas-based analysis method was used to compare fractional anisotropy (FA) and mean diffusivity (MD) among the three groups. A support vector machine (SVM) was trained to examine the probability of discriminating between dPD and ndPD. Results: As compared with ndPD, dPD group exhibited significantly decreased FA in the bilateral corticospinal tract, right cingulum (cingulate gyrus), left cingulum hippocampus, bilateral inferior longitudinal fasciculus, and bilateral superior longitudinal fasciculus, and increased MD in the right cingulum (cingulate gyrus) and left superior longitudinal fasciculus-temporal part. For discriminating between dPD and ndPD, the SVM model with DTI features exhibited an accuracy of 0.70 in the training set [area under the receiver operating characteristic curve (ROC) was 0.78] and an accuracy of 0.73 in the test set (area under the ROC was 0.71). Conclusion: Depression in PD is associated with white matter microstructural alterations. The SVM machine learning model based on DTI parameters could be valuable for the individualized diagnosis of dPD.

Machine learning to predict post-operative acute kidney injury stage 3 after heart transplantation.

BACKGROUND: Acute kidney injury (AKI) stage 3, one of the most severe complications in patients with heart transplantation (HT), is associated with substantial morbidity and mortality. We aimed to develop a machine learning (ML) model to predict post-transplant AKI stage 3 based on preoperative and perioperative features. METHODS: Data from 107 consecutive HT recipients in the provincial center between 2018 and 2020 were included for analysis. Logistic regression with L2 regularization was used for the ML model building. The predictive performance of the ML model was assessed using the area under the curve (AUC) in tenfold stratified cross-validation and was compared with that of the Cleveland-clinical model. RESULTS: Post-transplant AKI occurred in 76 (71.0%) patients including 15 (14.0%) stage 1, 18 (16.8%) stage 2, and 43 (40.2%) stage 3 cases. The top six features selected for the ML model to predicate AKI stage 3 were serum cystatin C, estimated glomerular filtration rate (eGFR), right atrial long-axis dimension, left atrial anteroposterior dimension, serum creatinine (SCr) and FVII. The predictive performance of the ML model (AUC: 0.821; 95% confidence interval [CI]: 0.740-0.901) was significantly higher compared with that of the Cleveland-clinical model (AUC: 0.654; 95% [CI]: 0.545-0.763, p < 0.05). CONCLUSIONS: The ML model, which achieved an effective predictive performance for post-transplant AKI stage 3, may be helpful for timely intervention to improve the patient's prognosis.

Multiphoton microscopy providing pathological-level quantification of myocardial fibrosis in transplanted human heart.

Multiphoton microscopy (MPM), a high-resolution laser scanning technique, has been shown to provide detailed real-time information on fibrosis assessment in animal models. But the value of MPM in human histology, especially in heart tissue, has not been fully explored. We aimed to evaluate the association between myocardial fibrosis measured by MPM and that measured by histological staining in the transplanted human heart. One hundred and twenty samples of heart tissue were obtained from 20 patients consisting of 10 dilated cardiomyopathies (DCM) and 10 ischemic cardiomyopathies (ICM). MPM and picrosirius red staining were performed to quantify collagen volume fraction (CVF) in explanted hearts postoperatively. Cardiomyocyte and myocardial fibrosis could be clearly visualized by MPM. Although patients with ICM had significantly greater MPM-derived CVF than patients with DCM (25.33  ± 12.65 % vs. 19.82  ± 8.62 %, p = 0.006), there was a substantial overlap of CVF values between them. MPM-derived CVF was comparable to that derived from picrosirius red staining based on all samples (22.58 ± 11.13% vs. 21.19 ± 11.79%, p = 0.348), as well as in DCM samples and ICM samples. MPM-derived CVF was correlated strongly with the magnitude of staining-derived CVF in both all samples and DCM samples and ICM samples (r = 0.972, r = 0.963, r = 0.973, respectively; all p < 0.001). Intra- and inter-observer reproducibility for MPM-derived CVF and staining-derived CVF were 0.995, 0.989, 0.995, and 0.985, respectively. Our data demonstrated that MPM can provide a pathological-level assessment of myocardial microstructure in transplanted human heart.

Langerhans cell histiocytosis presenting as an isolated brain tumour: A case report.

BACKGROUND: Langerhans cell histiocytosis (LCH) is a rare proliferative histiocyte disorder. It can affect any organ or system, especially the bone, skin, lung, and central nervous system (CNS). In the CNS, the hypothalamic-pituitary is predominantly affected, whereas the brain parenchyma is rarely affected. LCH occurring in the brain parenchyma can be easily confused with glioblastoma or brain metastases. Thus, multimodal imaging is useful for the differential diagnosis of these intracerebral lesions and detection of lesions in the other organs. CASE SUMMARY: A 47-year-old man presented with a headache for one week and sudden syncope. Brain computed tomography (CT) and magnetic resonance imaging showed an irregularly shaped nodule with heterogeneous enhancement. On 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/CT, a nodule with 18F-FDG uptake and multiple cysts in the upper lobes of both lungs were noted, which was also confirmed by high-resolution CT. Thus, the patient underwent surgical resection of the brain lesion for further examination. Postoperative pathology confirmed LCH. The patient received chemotherapy after surgery. No recurrence was observed in the brain at the 12-mo follow-up. CONCLUSION: Multimodal imaging is useful for evaluating the systemic condition of LCH, developing treatment plans, and designing post-treatment strategies.

Left atrial remodeling and the prognostic value of feature tracking derived left atrial strain in patients with light-chain amyloidosis: a cardiovascular magnetic resonance study.

Systemic light-chain (AL) amyloidosis is characterized by the aggregation of misfolded immunoglobulin light chain, predominantly infiltrating in the heart, including left atrium (LA). LA remodeling, such as increased interatrial septal thickness and enlarged size, has been observed. However, LA strain assessed by cardiac magnetic resonance feature tracking (CMR-FT) and its prognostic role remains to be further determined. Using CMR, the current study sought to investigate the characteristic of LA remodeling and the prognostic value of LA strain in patients with AL. Eighty-seven consecutive patients who underwent CMR with histologically confirmed systemic light-chain amyloidosis were retrospectively enrolled. LA strain parameters were analyzed based on CMR-FT algorithm. Amyloid infiltration and burden loads were assessed with CMR late gadolinium enhancement (LGE) and extracellular volume (ECV). Patients were categorized according to the extent of amyloid infiltration in cardiac myocardium. The primary endpoint was defined as all-cause mortality. The prognosis value of LA strain indices was evaluated using Cox proportional hazards regression and Kaplan-Meier curves. Interatrial septal thickness (3 [2-5] vs. 4 [3-5] mm, p = 0.007) and indexed LA volume (34.6 [26.9-44.6] vs. 50.5 [36.1-58.5] ml/m2, p = 0.001) were significantly higher in patients with atrial involvement (LA-LGE). Compared with patients with low amyloid burden loads (ECV group I), those at moderate and high (ECV group II and III) show progressive impairment in LA reservoir, conduit, and booster strains and strain rates. A total of 44 patients died during a median follow-up of 12 months. In multivariate analysis, LA reservoir strain, New York Heart Association (NYHA), and ECV remained independently associated with survival. On Kaplan-Meier analyses, low LA reservoir strain (< 8.6%) increased the risk of mortality. In moderate amyloid burden loads patients, low LA reservoir strain provides additive prognosis value. Progress left atrial remodeling and dysfunction are common findings in AL cardiac amyloidosis. The CMR-FT-derived LA reservoir strain provides independent and additive prognostic value for all-cause mortality in patients with AL cardiac amyloidosis.

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.

Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease.

Aim: Patients with ischemic stroke (IS), transient ischemic attack (TIA), and/or peripheral artery disease (PAD) represent a population with an increased risk of coronary artery disease. Prognostic risk assessment to identify those with the highest risk that may benefit from more intensified treatment remains challenging. To explore the feasibility and capability of machine learning (ML) to predict long-term adverse cardiac-related prognosis in patients with IS, TIA, and/or PAD. Methods: We analyzed 636 consecutive patients with a history of IS, TIA, and/or PAD. All patients underwent a coronary CT angiography (CCTA) scan. Thirty-five clinical data and 34 CCTA metrics underwent automated feature selection for ML model boosting. The clinical outcome included all-cause mortality (ACM) and major adverse cardiac events (MACE) (ACM, unstable angina requiring hospitalization, non-fatal myocardial infarction (MI), and revascularization 90 days after the index CCTA). Results: During the follow-up of 3.9 ± 1.6 years, 21 patients had unstable angina requiring hospitalization, eight had a MI, 23 had revascularization and 13 deaths. ML demonstrated a significant higher area-under-curve compared with the modified Duke index (MDI), segment stenosis score (SSS), segment involvement score (SIS), and Framingham risk score (FRS) for the prediction of ACM (ML:0.92 vs. MDI:0.66, SSS:0.68, SIS:0.67, FRS:0.51, all P < 0.001) and MACE (ML:0.84 vs. MDI:0.82, SSS:0.76, SIS:0.73, FRS:0.53, all P < 0.05). Conclusion: Among the patients with IS, TIA, and/or PAD, ML demonstrated a better capability of predicting ACM and MCAE than clinical scores and CCTA metrics.

Adding DSC PWI and DWI to BT-RADS can help identify postoperative recurrence in patients with high-grade gliomas.

BACKGROUND: The Brain Tumor Reporting and Data System (BT-RADS) category 3 is suitable for identifying cases with intermediate probability of tumor recurrence that do not meet the Response Assessment in Neuro-Oncology (RANO) criteria for progression. The aim of this study was to evaluate the added value of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC PWI) and diffusion-weighted imaging (DWI) to BT-RADS for differentiating tumor recurrence from non-recurrence in postoperative high-grade glioma (HGG) patients with category 3 lesions. METHODS: Patients with BT-RADS category 3 lesions were included. The maximal relative cerebral blood volume (rCBVmax) and the mean apparent diffusion coefficient (ADCmean) values were measured. The added value of DSC PWI and DWI to BT-RADS was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: Fifty-one of 91 patients had tumor recurrence, and 40 patients did not. There were significant differences in rCBVmax and ADCmean between the tumor recurrence group and non-recurrence group. Compared to BT-RADS alone, the addition of DSC PWI to BT-RADS increased the area under curve (AUC) from 0.76 (95% confidence interval [CI] 0.66-0.84) to 0.90 (95% CI 0.81-0.95) for differentiating tumor recurrence from non-recurrence. The addition of DWI to BT-RADS increased the AUC from 0.76 (95% CI 0.66-0.84) to 0.88 (95% CI 0.80-0.94). The combination of BT-RADS, DSC PWI, and DWI exhibited the best diagnostic performance (AUC = 0.95; 95% CI 0.88-0.98) for differentiating tumor recurrence from non-recurrence. CONCLUSION: Adding DSC PWI and DWI to BT-RADS can significantly improve the diagnostic performance for differentiating tumor recurrence from non-recurrence in BT-RADS category 3 lesions.

Cortical hypoperfusion in patients with idiopathic rapid eye movement sleep behavior disorder detected with arterial spin-labeled perfusion MRI.

BACKGROUND: Idiopathic rapid eye movement sleep behavior disorder (iRBD) is an important risk factor for α-synucleinopathy. OBJECTIVE: We investigated alterations in the cerebral blood flow (CBF) based on arterial spin-labeled (ASL) imaging in patients with iRBD to determine brain perfusion changes associated with the disorder. METHODS: Fifteen patients with iRBD and twenty age-gender-matched healthy controls were enrolled. Cortical perfusions were compared between the two groups after the ASL data was co-registered to the high-resolution T1-weighted images. RESULTS: No significant differences were detected between the groups in regard to age, gender, education, or UPDRS-III score. The iRBD group showed a lower MMSE score than the healthy controls (27.07 ± 2.25 vs. 28.55 ± 1.23, p < 0.05). Compared with the healthy controls, the iRBD group showed significantly decreased CBF values in the right inferior frontal gyrus, right middle frontal gyrus, and right insula (p < 0.05 corrected). CONCLUSION: The cortical hypoperfusion areas in patients with iRBD were similar to the patterns in patients with α -synucleinopathies. ASL perfusion MRI is a potential approach to find biomarkers in preclinical stages of α -synucleinopathies.

Ultra-low-dose multiphase CT angiography derived from CT perfusion data in patients with middle cerebral artery stenosis.

PURPOSE: Computed tomography (CT) perfusion (CTP) source images contain both brain perfusion and cerebrovascular information, and may allow a dynamic assessment of collaterals. The purpose of the study was to compare the image quality and the collaterals identified on multiphase CT angiography (CTA) derived from CTP datasets (hereafter called CTPA) reconstructed with iterative model reconstruction (IMR) algorithm in patients with middle cerebral artery (MCA) steno-occlusion with those of routine CTA. METHODS: Consecutive patients with a unilateral MCA steno-occlusion underwent non-contrast CT (NCCT), CTP, and CTA. CTPA images were reconstructed from CTP datasets. The vascular attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of routine CTA and CTPA were measured and analyzed by Student's t test. Subjective image quality and collaterals were scored and compared using the Wilcoxon signed-rank test. RESULTS: Fifty-eight patients (mean age 61.7 years, 78% males, median National Institutes of Health Stroke Scale score = 12) were included. The effective radiation dose of CTP was 1.28 mSv. The vascular attenuation, SNR, CNR, and the image quality of CTPA were considerably higher than that of CTA (all, p < 0.001). Collaterals were rated higher on CTPA compared with CTA (1.79 ± 0.64 vs. 1.22 ± 0.84, p < 0.001). Fifty-three percent of patients with poor collaterals assessed on single-phase CTA had good collaterals on CTPA. CONCLUSION: CTPA derived from CTP datasets reconstructed with IMR algorithm offers image quality comparable to routine CTA and provides time-resolved evaluation of collaterals in patients with MCA ischemic disease.

[A spatial-distance analysis approach of multi-spectrum feature distribution for remote sensing image land use/cover].

Aiming at the problem that a convenient multivariate statistical model is in general not available for the multi-spectrum feature of land use/cover (LUC) class in remote sensing (RS) image, because the class is made of multiple covered species, a spatial-distance analysis approach of multi-spectrum feature distribution for RS image LUC is present, with the mean vector of samples as LUC class center, with max-min clustering algorithm forming the class multi-clustering-centers, the spatial-distances from the class center to these multi-clustering-centers were calculated. With the distance as abscissa and the percentage of the clustering-center pixels to the whole sample pixels as ordinate, the intra- and inter-classes distance distribution charts were constructed to analyze the multi-spectrum feature distribution of RS image LUC. The results of these samples classification tally with the conclusions of spatial distance analysis, indicating that this approach is feasible. In this approach the multi-dimensional spectrum information is turned into one dimensional distance information, the spatial-distance calculation and clustering threshold confirmation are realized easily, and the multi-spectrum feature of LUC class is clear, so it is a better approach to solving the multivariate distributing problem of multi-spectrum feature.