Multi-Modality Imaging of Atheromatous Plaques in Peripheral Arterial Disease: Integrating Molecular and Imaging Markers.

Peripheral artery disease (PAD) is a common and debilitating condition characterized by the narrowing of the limb arteries, primarily due to atherosclerosis. Non-invasive multi-modality imaging approaches using computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging have emerged as valuable tools for assessing PAD atheromatous plaques and vessel walls. This review provides an overview of these different imaging techniques, their advantages, limitations, and recent advancements. In addition, this review highlights the importance of molecular markers, including those related to inflammation, endothelial dysfunction, and oxidative stress, in PAD pathophysiology. The potential of integrating molecular and imaging markers for an improved understanding of PAD is also discussed. Despite the promise of this integrative approach, there remain several challenges, including technical limitations in imaging modalities and the need for novel molecular marker discovery and validation. Addressing these challenges and embracing future directions in the field will be essential for maximizing the potential of molecular and imaging markers for improving PAD patient outcomes.

Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load.

BACKGROUND: Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist. PURPOSE: To evaluate 1) the utility of a fully automated volume-based morphometry (Vol-BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol-BM model construct in classifying the PIGD subtype. STUDY TYPE: Prospective. SUBJECTS: In all, 23 PIGD, 21 PD, and 20 age-matched healthy controls (HC) underwent MRI brain scans and clinical assessments. FIELD STRENGTH/SEQUENCE: 3.0T, sagittal 3D-magnetization-prepared rapid gradient echo (MPRAGE), and fluid-attenuated inversion recovery imaging (FLAIR) sequences. ASSESSMENT: Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol-BM algorithm (MorphoBox) and reviewed by two authors independently. STATISTICAL TESTING: Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis. RESULTS: Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84). DATA CONCLUSION: Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol-BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:748-756.

Assessing exercise cardiac reserve using real-time cardiovascular magnetic resonance.

BACKGROUND: Exercise cardiovascular magnetic resonance (ExCMR) has great potential for clinical use but its development has been limited by a lack of compatible equipment and robust real-time imaging techniques. We developed an exCMR protocol using an in-scanner cycle ergometer and assessed its performance in differentiating athletes from non-athletes. METHODS: Free-breathing real-time CMR (1.5T Aera, Siemens) was performed in 11 athletes (5 males; median age 29 [IQR: 28-39] years) and 16 age- and sex-matched healthy volunteers (7 males; median age 26 [interquartile range (IQR): 25-33] years). All participants underwent an in-scanner exercise protocol on a CMR compatible cycle ergometer (Lode BV, the Netherlands), with an initial workload of 25W followed by 25W-increment every minute. In 20 individuals, exercise capacity was also evaluated by cardiopulmonary exercise test (CPET). Scan-rescan reproducibility was assessed in 10 individuals, at least 7 days apart. RESULTS: The exCMR protocol demonstrated excellent scan-rescan (cardiac index (CI): 0.2 ± 0.5L/min/m2) and inter-observer (ventricular volumes: 1.2 ± 5.3mL) reproducibility. CI derived from exCMR and CPET had excellent correlation (r = 0.83, p < 0.001) and agreement (1.7 ± 1.8L/min/m2). Despite similar values at rest (P = 0.87), athletes had increased exercise CI compared to healthy individuals (at peak exercise: 12.2 [IQR: 10.2-13.5] L/min/m2 versus 8.9 [IQR: 7.5-10.1] L/min/m2, respectively; P < 0.001). Peak exercise CI, where image acquisition lasted 13-17 s, outperformed that at rest (c-statistics = 0.95 [95% confidence interval: 0.87-1.00] versus 0.48 [95% confidence interval: 0.23-0.72], respectively; P < 0.0001 for comparison) in differentiating athletes from healthy volunteers; and had similar performance as VO2max (c-statistics = 0.84 [95% confidence interval = 0.62-1.00]; P = 0.29 for comparison). CONCLUSIONS: We have developed a novel in-scanner exCMR protocol using real-time CMR that is highly reproducible. It may now be developed for clinical use for physiological studies of the heart and circulation.

Methods of Machine Learning-Based Chimeric Antigen Receptor Immunological Synapse Quality Quantification.

Chimeric Antigen Receptor (CAR)-mediated immunotherapy shows promising results for refractory blood cancers. Currently, six CAR-T drugs have been approved by U.S. Food and Drug Administration (FDA). Theoretically, CAR-T cells must form an effective immunological synapse (IS, an interface between effective cells and their target cells) with their susceptible tumor cells to eliminate tumor cells. Previous studies show that CAR IS quality can be used as a predictive functional biomarker for CAR-T immunotherapies. However, quantification of CAR-T IS quality is clinically challenging. Machine learning (ML)-based CAR-T IS quality quantification has been proposed previously.Here, we show an easy-to-use, step-by-step approach to predicting the efficacy of CAR-modified cells using ML-based CAR IS quality quantification. This approach will guide the users on how to use ML-based CAR IS quality quantification in detail, which include: how to image CAR IS on the glass-supported planar lipid bilayer, how to define the CAR IS focal plane, how to segment the CAR IS images, and how to quantify the IS quality using ML-based algorithms.This approach will significantly enhance the accuracy and proficiency of CAR IS prediction in research.

Effects of fitting methods, high b-values and image quality on diffusion and perfusion quantification and reproducibility in the calf.

PURPOSES: The study aimed to optimize diffusion-weighted imaging (DWI) image acquisition and analysis protocols in calf muscles by investigating the effects of different model-fitting methods, image quality, and use of high b-value and constraints on parameters of interest (POIs). The optimized modeling methods were used to select the optimal combinations of b-values, which will allow shorter acquisition time while achieving the same reliability as that obtained using 16 b-values. METHODS: Test-retest baseline and high-quality DWI images of ten healthy volunteers were acquired on a 3T MR scanner, using 16 b-values, including a high b-value of 1200 s/mm2, and structural T1-weighted images for calf muscle delineation. Three and six different fitting methods were used to derive ADC from monoexponential (ME) model and Dd, fp, and Dp from intravoxel incoherent motion (IVIM) model, with or without the high b-value. The optimized ME and IVIM models were then used to determine the optimal combinations of b-values, obtainable with the least number of b-values, using the selection criteria of coefficient of variance (CV) ≤10% for all POIs. RESULTS: The find minimum multivariate algorithm was more flexible and yielded smaller fitting errors. The 2-steps fitting method, with fixed Dd, performed the best for IVIM model. The inclusion of high b-value reduced outliers, while constraints improved 2-steps fitting only. CONCLUSIONS: The optimal numbers of b-values for ME and IVIM models were nine and six b-values respectively. Test-retest reliability analyses showed that only ADC and Dd were reliable for calf diffusion evaluation, with CVs of 7.22% and 4.09%.