Quantitative imaging of ultrasound backscattered signals with information entropy for bone microstructure characterization

Osteoporosis is a critical problem during aging. Ultrasound signals backscattered from bone contain information associated with microstructures. This study proposed using entropy imaging to collect the information in bone microstructures as a possible solution for ultrasound bone tissue characterization. Bone phantoms with different pounds per cubic foot (PCF) were used for ultrasound scanning by using single-element transducers of 1 (nonfocused) and 3.5 MHz (nonfocused and focused). Clinical measurements were also performed on lumbar vertebrae (L3 spinal segment) in participants with different ages (n = 34) and postmenopausal women with low or moderate-to-high risk of osteoporosis (n = 50; identified using the Osteoporosis Self-Assessment Tool for Taiwan). The signals backscattered from the bone phantoms and subjects were acquired for ultrasound entropy imaging by using sliding window processing. The independent t-test, one-way analysis of variance, Spearman correlation coefficient rs, and the receiver operating characteristic (ROC) curve were used for statistical analysis. The results indicated that ultrasound entropy imaging revealed changes in bone microstructures. Using the 3.5-MHz focused ultrasound, small-window entropy imaging (side length: one pulse length of the transducer) was found to have high performance and sensitivity in detecting variation among the PCFs (rs = − 0.83; p < 0.05). Small-window entropy imaging also performed well in discriminating young and old participants (p < 0.05) and postmenopausal women with low versus moderate-to-high osteoporosis risk (the area under the ROC curve = 0.80; cut-off value = 2.65; accuracy = 86.00%; sensitivity = 71.43%; specificity = 88.37%). Ultrasound small-window entropy imaging has great potential in bone tissue characterization and osteoporosis assessment.

The 2020 “Padua Criteria” for Diagnosis and Phenotype Characterization of Arrhythmogenic Cardiomyopathy in Clinical Practice

Arrhythmogenic Cardiomyopathy (ACM) is a heredo-familial cardiac disease characterized by fibro-fatty myocardial replacement and increased risk of sudden cardiac death. The diagnosis of ACM can be challenging due to the lack of a single gold-standard test: for this reason, it is required to satisfy a combination of multiple criteria from different categories including ventricular morpho-functional abnormalities, repolarization and depolarization ECG changes, ventricular arrhythmias, tissue characterization findings and positive family history/molecular genetics. The first diagnostic criteria were published by an International Task Force (ITF) of experts in 1994 and revised in 2010 with the aim to increase sensitivity for early diagnosis. Limitations of the 2010 ITF criteria include the absence of specific criteria for left ventricle (LV) involvement and the limited role of cardiac magnetic resonance (CMR) as the use of the late gadolinium enhancement technique for tissue characterization was not considered. In 2020, new diagnostic criteria (“the Padua criteria”) were proposed. The traditional organization in six categories of major/minor criteria was maintained. The criteria for identifying the right ventricular involvement were modified and a specific set of criteria for identifying LV involvement was created. Depending on the combination of criteria for right and LV involvement, a diagnosis of classic (right dominant) ACM, biventricular ACM or left-dominant ACM is then made. The article reviews the rationale of the Padua criteria, summarizes the main modifications compared to the previous 2010 ITF criteria and provides three examples of the application of the Padua criteria in clinical practice.

Effective Study: Development and Application of a Question‐Driven, Time‐Effective Cardiac Magnetic Resonance Scanning Protocol

Background Long scanning times impede cardiac magnetic resonance (CMR) clinical uptake. A “one‐size‐fits‐all” shortened, focused protocol (eg, only function and late‐gadolinium enhancement) reduces scanning time and costs, but provides less information. We developed 2 question‐driven CMR and stress‐CMR protocols, including tailored advanced tissue characterization, and tested their effectiveness in reducing scanning time while retaining the diagnostic performances of standard protocols. Methods and Results Eighty three consecutive patients with cardiomyopathy or ischemic heart disease underwent the tailored CMR. Each scan consisted of standard cines, late‐gadolinium enhancement imaging, native T1‐mapping, and extracellular volume. Fat/edema modules, right ventricle cine, and in‐line quantitative perfusion mapping were performed as clinically required. Workflow was optimized to avoid gaps. Time target was <30 minutes for a CMR and <35 minutes for a stress‐CMR. CMR was considered impactful when its results drove changes in diagnosis or management. Advanced tissue characterization was considered impactful when it changed the confidence level in the diagnosis. The quality of the images was assessed. A control group of 137 patients was identified among scans performed before February 2020. Compared with standard protocols, the average scan duration dropped by >30% (CMR: from 42±8 to 28±6 minutes; stress‐CMR: from 50±10 to 34±6 minutes, both P <0.0001). Independent on the protocol, CMR was impactful in ≈60% cases, and advanced tissue characterization was impactful in >45% of cases. Quality grading was similar between the 2 protocols. Tailored protocols did not require additional staff. Conclusions Tailored CMR and stress‐CMR protocols including advanced tissue characterization are accurate and time‐effective for cardiomyopathies and ischemic heart disease.

Case report: epipericardial fat necrosis, a rare cause of chest pain

Background Epipericardial fat necrosis (EFN) is a rare cause of chest pain which is often unrecognized. Case summary A 58 year-old male previously known with a transient ischaemic attack presented with a sharp, substernal chest pain. Pulmonary embolism was ruled out by computed tomography (CT) angiography. However, CT angiography revealed an inhomogeneous epipericardial mass. On cardiovascular magnetic resonance imaging (CMR) the mass had an inhomogeneous signal intensity without infiltration of surrounding tissue. Late gadolinium enhancement imaging showed subtle hyperenhancement. Tissue characterization by means of parametric mapping revealed very low native T1 relaxation times and increased T2 relaxation times. In conclusion, the epipericardial mass showed fibro-fatty inflammatory markers, suggestive of EFN. The chest pain resolved spontaneously. Follow up CT 3 months later showed a marked regression of the mass which confirmed the diagnosis EFN. Discussion EFN is a benign and self-limiting inflammatory cause of chest pain which can be diagnosed with multi-modality imaging and must not be overlooked in the differential diagnosis of patients with acute pleuritic chest pain.

The Role of AI in Characterizing the DCM Phenotype

Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.

Cross-Tissue Characterization of Heterogeneities of Mesenchymal Stem Cells and Their Differentiation Potentials

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.

Cardiac Amyloidosis: A Review of Current Imaging Techniques

Systemic amyloidosis is a rare, heterogenous group of diseases characterized by extracellular infiltration and deposition of amyloid fibrils. Cardiac amyloidosis (CA) occurs when these fibrils deposit within the myocardium. Untreated, this inevitably leads to progressive heart failure and fatality. Historically, treatment has remained supportive, however, there are now targeted disease-modifying therapeutics available to patients with CA. Advances in echocardiography, cardiac magnetic resonance (CMR) and repurposed bone scintigraphy have led to a surge in diagnoses of CA and diagnosis at an earlier stage of the disease natural history. CMR has inherent advantages in tissue characterization which has allowed us to better understand the pathological disease process behind CA. Combined with specialist assessment and repurposed bone scintigraphy, diagnosis of CA can be made without the need for invasive histology in a significant proportion of patients. With existing targeted therapeutics, and novel agents being developed, understanding these imaging modalities is crucial to achieving early diagnosis for patients with CA. This will allow for early treatment intervention, accurate monitoring of disease course over time, and thereby improve the length and quality of life of patients with a disease that historically had an extremely poor prognosis. In this review, we discuss key radiological features of CA, focusing on the two most common types; immunoglobulin light chain (AL) and transthyretin (ATTR) CA. We highlight recent advances in imaging techniques particularly in respect of their clinical application and utility in diagnosis of CA as well as for tracking disease change over time.

66 Heart and lung tissue characterization of COVID-19 pneumonia by T1 and T2 mapping magnetic resonance imaging

Aims Coronavirus disease 2019 (COVID-19) is a respiratory tract infection which can lead to systemic involvement including myocardial injury, severe respiratory failure and death. Magnetic resonance imaging (MRI) could potentially offer advantages in providing tissue characterization of lung parenchyma and heart muscle in COVID-19. The aim of the present study was to describe data on heart and lung MRI in a cohort of patients hospitalized due to COVID-19 pneumonia. Methods and results n = 11 patients hospitalized with COVID-19 pneumonia underwent a comprehensive MRI examinations including lung and heart tissue mapping, findings were compared to those of an age- and sex-matched cohort of n = 11 individuals. Lung native T1 and T2 mapping assessments were performed by drawing a circular region of interest (ROI) with diameter of 2 cm in the parenchyma visualized from the cardiac four chamber long axis-oriented slice; vessels and areas of pleural effusion were carefully excluded. Myocardial native T1 and T2 mapping were assessed by drawing a ROI within the midventricular left ventricular (LV) septum. No patients had previous history of cardiovascular disease (including known coronary artery disease, heart failure, cardiomyopathy, atrial fibrillation). As compared to controls, patients with COVID-19 had similar cardiac function, higher mid-septum myocardial native T1 (1028 ms vs. 985, P = 0.05) and significantly higher lung native T1 and T2 within affected areas (1375 ms vs. 1201 ms, P = 0.016 and 70 ms vs. 30 ms, P &lt; 0.001 respectively), whereas non-significant differences were observed between remote lung areas of patients and controls (1238 ms vs. 1152 ms, P = 0.088 and 29 ms vs. 33 ms, P = 0.797 respectively). No significant associations were observed between cardiac and lung mapping findings. Conclusions In our cohort of patients with COVID-19, T1 and T2 mapping lung MRI identified pneumonia related abnormalities as compared to healthy controls, likely representing oedema and ongoing inflammation at the lung site. Myocardial native T1 was elevated suggesting the presence of cardiac involvement. A comprehensive MRI examination can be potentially used to assess multiorgan involvement in COVID-19.