T1 mapping from routine 3D T1-weighted inversion recovery sequences in clinical practice: comparison against reference inversion recovery fast field echo T1 scans and feasibility in multiple sclerosis.

BACKGROUND AND PURPOSE: Quantitative T1 mapping can be an essential tool for assessing tissue injury in multiple sclerosis (MS). We introduce T1-REQUIRE, a method that converts a single high-resolution anatomical 3D T1-weighted Turbo Field Echo (3DT1TFE) scan into a parametric T1 map that could be used for quantitative assessment of tissue damage. We present the accuracy and feasibility of this method in MS. METHODS: 14 subjects with relapsing-remitting MS and 10 healthy subjects were examined. T1 maps were generated from 3DT1TFE images using T1-REQUIRE, which estimates T1 values using MR signal equations and internal tissue reference T1 values. Estimated T1 of lesions, white, and gray matter regions were compared with reference Inversion-Recovery Fast Field Echo T1 values and analyzed via correlation and Bland-Altman (BA) statistics. RESULTS: 159 T1-weighted (T1W) hypointense MS lesions and 288 gray matter regions were examined. T1 values for MS lesions showed a Pearson's correlation of r = 0.81 (p < 0.000), R2 = 0.65, and Bias = 4.18%. BA statistics showed a mean difference of -53.95 ms and limits of agreement (LOA) of -344.20 and 236.30 ms. Non-lesional normal-appearing white matter had a correlation coefficient of r = 0.82 (p < 0.000), R2 = 0.67, Bias = 8.78%, mean difference of 73.87 ms, and LOA of -55.67 and 203.41 ms. CONCLUSIONS: We demonstrate the feasibility of retroactively derived high-resolution T1 maps from routinely acquired anatomical images, which could be used to quantify tissue pathology in MS. The results of this study will set the stage for testing this method in larger clinical studies for examining MS disease activity and progression.

Patient-specific tissue engineered vascular graft for aortic arch reconstruction.

Objectives: The complexity of aortic arch reconstruction due to diverse 3-dimensional geometrical abnormalities is a major challenge. This study introduces 3-dimensional printed tissue-engineered vascular grafts, which can fit patient-specific dimensions, optimize hemodynamics, exhibit antithrombotic and anti-infective properties, and accommodate growth. Methods: We procured cardiac magnetic resonance imaging with 4-dimensional flow for native porcine anatomy (n = 10), from which we designed tissue-engineered vascular grafts for the distal aortic arch, 4 weeks before surgery. An optimal shape of the curved vascular graft was designed using computer-aided design informed by computational fluid dynamics analysis. Grafts were manufactured and implanted into the distal aortic arch of porcine models, and postoperative cardiac magnetic resonance imaging data were collected. Pre- and postimplant hemodynamic data and histology were analyzed. Results: Postoperative magnetic resonance imaging of all pigs with 1:1 ratio of polycaprolactone and poly-L-lactide-co-ε-caprolactone demonstrated no specific dilatation or stenosis of the graft, revealing a positive growth trend in the graft area from the day after surgery to 3 months later, with maintaining a similar shape. The peak wall shear stress of the polycaprolactone/poly-L-lactide-co-ε-caprolactone graft portion did not change significantly between the day after surgery and 3 months later. Immunohistochemistry showed endothelization and smooth muscle layer formation without calcification of the polycaprolactone/poly-L-lactide-co-ε-caprolactone graft. Conclusions: Our patient-specific polycaprolactone/poly-L-lactide-co-ε-caprolactone tissue-engineered vascular grafts demonstrated optimal anatomical fit maintaining ideal hemodynamics and neotissue formation in a porcine model. This study provides a proof of concept of patient-specific tissue-engineered vascular grafts for aortic arch reconstruction.

In Situ Myocardial Regeneration With Tissue Engineered Cardiac Patch Using Spheroid-Based 3-Dimensional Tissue.

BACKGROUND: We have developed a tissue engineered cardiac patch derived from a 3-dimensional (3D) myocardial tissue reinforced with extracellular matrix in an effort to enhance in situ myocardial regeneration. The feasibility of the patch was evaluated in a porcine model by various modalities to assess both the constructive and functional aspects of regeneration. METHODS: A spheroid-based 3D multicellular tissue was created using a 3D net mold system that incorporated cardiomyocytes and embryonic fibroblast cells. The 3D multicellular tissue was incorporated with extracellular matrix sheets and surgically implanted into the right ventricle of a healthy porcine model (n = 4). After 60 days, the implanted patches were evaluated by cardiac magnetic resonance imaging and electroanatomic mapping studies as well as by post-euthanasia analyses, including measurements of mechanical viscoelasticity. RESULTS: Cardiac magnetic resonance imaging revealed improved regional tissue perfusion in the patch area. Electroanatomic mapping exhibited regenerated electrical conductivity in the patch, as evidenced by relatively preserved voltage regions (1.11 ± 0.8 mV) in comparison to the normal right ventricle (4.7 ± 2.8 mV). Histologic and tissue analyses confirmed repopulation of site-specific host cells, including premature cardiomyocytes and active vasculogenesis. These findings were supported by quantitative reverse transcription-polymerase chain reaction. CONCLUSIONS: The tissue engineered cardiac patch effectively facilitated in situ constructive and functional myocardial regeneration, characterized by increased regional tissue perfusion and positive electrical activity in the porcine model.

Transient susceptibility imaging as a measure of hemodynamic compromise: A pilot study.

BACKGROUND AND PURPOSE: The purpose of this study was to test the hypothesis that hemodynamically compromised brains exhibit transient changes in magnetic susceptibility throughout the cardiac cycle, and to model these changes using Linear System Theory to extract an index that reflects cerebrovascular reserve. MATERIALS AND METHODS: Eleven patients with angiographically-confirmed intracranial atherosclerotic disease with >50% stenosis were imaged with susceptibility weighted, cardiac-gated single shot images of cerebral Oxygen Extraction Fraction (OEF) at different timepoints of the cardiac cycle. Cardiac gating of the OEF acquisition allowed interrogation of oxygenated blood and the detection of changes throughout the cardiac cycle. Independent component analysis (ICA) of raw k-space data across the cardiac phase allowed MRI signal decomposition into dynamic and static components for image reconstruction. An asymmetry index score of the resultant parametric images were compared to test the hypothesis that variation in hemoglobin-induced susceptibility across the cardiac cycle indeed reflects pathophysiology of cerebrovascular disease. A mathematical model was derived to parameterize physiologic changes induced by the presence of a hemodynamically significant stenosis in the brain as a tissue impulse response parameter (ß). RESULTS: OEF was elevated in the affected hemisphere (50.34 ± 12.13% vs 46.93 ± 12.34%), but failed to reach statistical significance (p < .0796). Transient changes in the OEF signal showed significant distinction between healthy and compromised tissue (0.56 ± 0.067 vs 0.44 ± 0.067, p < .019)). The derived tissue impulse response function was found to be significant as well (10.72 ± 3.48 10-3 ms-1, 9.69 ± 3.51 10-3 ms-1; p < .037). CONCLUSION: In this pilot study, we found transient OEF and ß to be significant predictors of hemispheric compromise.

Detection of Meals and Physical Activity Events From Free-Living Data of People With Diabetes.

BACKGROUND: Predicting carbohydrate intake and physical activity in people with diabetes is crucial for improving blood glucose concentration regulation. Patterns of individual behavior can be detected from historical free-living data to predict meal and exercise times. Data collected in free-living may have missing values and forgotten manual entries. While machine learning (ML) can capture meal and exercise times, missing values, noise, and errors in data can reduce the accuracy of ML algorithms. METHODS: Two recurrent neural networks (RNNs) are developed with original and imputed data sets to assess detection accuracy of meal and exercise events. Continuous glucose monitoring (CGM) data, insulin infused from pump data, and manual meal and exercise entries from free-living data are used to predict meals, exercise, and their concurrent occurrence. They contain missing values of various lengths in time, noise, and outliers. RESULTS: The accuracy of RNN models range from 89.9% to 95.7% for identifying the state of event (meal, exercise, both, or neither) for various users. "No meal or exercise" state is determined with 94.58% accuracy by using the best RNN (long short-term memory [LSTM] with 1D Convolution). Detection accuracy with this RNN is 98.05% for meals, 93.42% for exercise, and 55.56% for concurrent meal-exercise events. CONCLUSIONS: The meal and exercise times detected by the RNN models can be used to warn people for entering meal and exercise information to hybrid closed-loop automated insulin delivery systems. Reliable accuracy for event detection necessitates powerful ML and large data sets. The use of additional sensors and algorithms for detecting these events and their characteristics provides a more accurate alternative.

Impact of wideband cardiac magnetic resonance on diagnosis, decision-making and outcomes in patients with implantable cardioverter defibrillators.

AIMS: Although myocardial scar assessment using late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is frequently indicated for patients with implantable cardioverter defibrillators (ICDs), metal artefact can degrade image quality. With the new wideband technique designed to mitigate device related artefact, CMR is increasingly used in this population. However, the common clinical indications for CMR referral and impact on clinical decision-making and prognosis are not well defined. Our study was designed to address these knowledge gaps. METHODS AND RESULTS: One hundred seventy-nine consecutive patients with an ICD (age 59 ± 13 years, 75% male) underwent CMR using cine and wideband pulse sequences for LGE imaging. Electronic medical records were reviewed to determine the reason for CMR referral, whether there was a change in clinical decision-making, and occurrence of major adverse cardiac events (MACEs). Referral indication was the most common evaluation of ventricular tachycardia (VT) substrate (n = 114, 64%), followed by cardiomyopathy (n = 53, 30%). Overall, CMR resulted in a new or changed diagnosis in 64 (36%) patients and impacted clinical management in 51 (28%). The effect on management change was highest in patients presenting with VT. A total of 77 patients (43%) experienced MACE during the follow-up period (median 1.7 years), including 65 in patients with evidence of LGE. Kaplan-Meier analysis showed that ICD patients with LGE had worse outcomes than those without LGE (P = 0.006). CONCLUSION: The clinical yield from LGE CMR is high and provides management changing and meaningful prognostic information in a significant proportion of patients with ICDs.

Relation of Myocardial Perfusion Reserve and Left Ventricular Ejection Fraction in Ischemic and Nonischemic Cardiomyopathy.

Quantification of myocardial perfusion reserve (MPR) using vasodilator stress cardiac magnetic resonance is increasingly used to detect coronary artery disease. However, MPR can also be altered because of changes in microvascular function. We aimed to determine whether MPR can distinguish between ischemic cardiomyopathy (IC) secondary to coronary artery disease and non-IC (NIC) with microvascular dysfunction and no underlying epicardial coronary disease. A total of 60 patients (mean age 65 ± 14 years, 30% women), including 31 with IC and 29 with NIC, were identified from a pre-existing vasodilator stress cardiac magnetic resonance registry. Short-axis cine slices were used to measure left ventricular ejection fraction (LVEF) using the Simpson method of disks. MPR index (MPRi) was determined from first-pass myocardial perfusion images during stress and rest using the upslope ratio, normalized for the arterial input and corrected for rate pressure product. Patients in both groups were divided into subgroups of LVEF ≤35% and LVEF >35%. Differences in MPRi between the subgroups were examined. MPRi was moderately correlated with LVEF in patients with NIC (r = 0.53, p = 0.03), whereas the correlation in patients with IC was lower (r = 0.32, p = 0.22). Average LVEF in NIC and IC was 34% ± 8% and 35% ± 8%, respectively (p = 0.63). MPRi was not significantly different in IC compared with NIC (1.17 [0.88 to 1.61] vs 1.23 [1.07 to 1.66], p = 0.41), including the subgroups of LVEF (IC: 1.20 ± 0.56 vs NIC: 1.15 ± 0.24, p = 0.75 for LVEF ≤35% and IC: 1.35 ± 0.44 vs NIC: 1.58 ± 0.50, p = 0.19 for LVEF >35%). However, MPRi was significantly lower in patients with LVEF ≤35% compared with those with LVEF>35% (1.17 ± 0.40 vs 1.47 ± 0.47, p = 0.01). Similar difference between LVEF groups was noted in the patients with NIC (1.15 ± 0.24 vs 1.58 ± 0.50, p = 0.006) but not in the patients with IC (1.20 ± 0.56 vs 1.35 ± 0.44, p = 0.42). MPRi can be abnormal in the presence of left ventricular dysfunction with nonischemic etiology. This is a potential pitfall to consider when using this approach to detect ischemia because of epicardial coronary disease using myocardial perfusion imaging.

Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence.

BACKGROUND: Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. METHODS: We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland-Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35-50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested. RESULTS: CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function. CONCLUSIONS: The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.

Meal and Physical Activity Detection from Free-living Data for Discovering Disturbance Patterns to Glucose Levels in People with Diabetes.

Objective: Interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID). Methods: Two challenges in time-series data collected in daily living are addressed: data quality improvement and detection of unannounced disturbances to BGC. CGM data have missing values for varying periods of time and outliers. People may neglect reporting their meal and physical activity information. In this work, novel methods for preprocessing real-world data collected from people with T1D and detection of meal and exercise events are presented. Four recurrent neural network (RNN) models are investigated to detect the occurrences of meals and physical activities disjointly or concurrently. Results: RNNs with long short-term memory (LSTM) with 1D convolution layers and bidirectional LSTM with 1D convolution layers have average accuracy scores of 92.32% and 92.29%, and outper-form other RNN models. The F1 scores for each individual range from 96.06% to 91.41% for these two RNNs. Conclusions: RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation.

Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images.

BACKGROUND: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms. METHODS: To train our DL and ML algorithms, we first established datasets by retrospectively selecting 200 CMR cases. The models were trained using two different cohorts (passively and actively curated) and applied data augmentation to enhance training. Once trained, the models were validated on an external dataset, consisting of 20 cases acquired at another center. We then compared accuracy metrics and applied class activation mapping (CAM) to visualize DL model performance. RESULTS: The DL and ML models trained with the passively-curated CMR cohort were 99.1% and 99.3% accurate on the validation set, respectively. However, when tested on the CMR cases with complex anatomy, both models performed poorly. After training and testing our models again on all 200 cases (active cohort), validation on the external dataset resulted in 95% and 90% accuracy, respectively. The CAM analysis depicted heat maps that demonstrated the importance of carefully curating the datasets to be used for training. CONCLUSIONS: Both DL and ML models can accurately classify CMR images, but DL outperformed ML when classifying images with complex heart anatomy.

AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors.

OBJECTIVES: The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert. BACKGROUND: Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images. METHODS: Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance. LVEF, LVM, and RVEF were determined using 3 fully automated commercial DL algorithms and by a clinical expert (CLIN) using conventional methodology. Additionally, LVEF values were classified according to clinically important ranges: <35%, 35% to 50%, and ≥50%. Both ejection fraction values and classifications made by the DL ejection fraction approaches were compared against CLIN ejection fraction reference. Receiver-operating characteristic curve analysis was performed to evaluate the ability of CLIN and each of the DL classifications to predict major adverse cardiovascular events. RESULTS: Excellent correlations were seen for each DL-LVEF compared with CLIN-LVEF (r = 0.83-0.93). Good correlations were present between DL-LVM and CLIN-LVM (r = 0.75-0.85). Modest correlations were observed between DL-RVEF and CLIN-RVEF (r = 0.59-0.68). A >10% error between CLIN and DL ejection fraction was present in 5% to 18% of cases for the left ventricle and 23% to 43% for the right ventricle. LVEF classification agreed with CLIN-LVEF classification in 86%, 80%, and 85% cases for the 3 DL-LVEF approaches. There were no differences among the 4 approaches in associations with major adverse cardiovascular events for LVEF, LVM, and RVEF. CONCLUSIONS: This study revealed good agreement between automated and expert-derived LVEF and similarly strong associations with outcomes, compared with an expert. However, the ability of these automated measurements to accurately classify left ventricular function for treatment decision remains limited. DL-LVM showed good agreement with CLIN-LVM. DL-RVEF approaches need further refinements.

Location matters: Offset in tissue-engineered vascular graft implantation location affects wall shear stress in porcine models.

Objective: Although surgical simulation using computational fluid dynamics has advanced, little is known about the accuracy of cardiac surgical procedures after patient-specific design. We evaluated the effects of discrepancies in location for patient-specific simulation and actual implantation on hemodynamic performance of patient-specific tissue-engineered vascular grafts (TEVGs) in porcine models. Methods: Magnetic resonance angiography and 4-dimensional (4D) flow data were acquired in porcine models (n = 11) to create individualized TEVGs. Graft shapes were optimized and manufactured by electrospinning bioresorbable material onto a metal mandrel. TEVGs were implanted 1 or 3 months postimaging, and postoperative magnetic resonance angiography and 4D flow data were obtained and segmented. Displacement between intended and observed TEVG position was determined through center of mass analysis. Hemodynamic data were obtained from 4D flow analysis. Displacement and hemodynamic data were compared using linear regression. Results: Patient-specific TEVGs were displaced between 1 and 8 mm during implantation compared with their surgically simulated, intended locations. Greater offset between intended and observed position correlated with greater wall shear stress (WSS) in postoperative vasculature (P < .01). Grafts that were implanted closer to their intended locations showed decreased WSS. Conclusions: Patient-specific TEVGs are designed for precise locations to help optimize hemodynamic performance. However, if TEVGs were implanted far from their intended location, worse WSS was observed. This underscores the importance of not only patient-specific design but also precision-guided implantation to optimize hemodynamics in cardiac surgery and increase reproducibility of surgical simulation.

Impact of Wideband Late Gadolinium Enhancement Cardiac Magnetic Resonance Imaging on Device-Related Artifacts in Different Implantable Cardioverter-Defibrillator Types.

BACKGROUND: Late gadolinium enhancement (LGE) imaging in patients with implantable cardioverter-defibrillators (ICD) is limited by device-related artifacts (DRA). The use of wideband (WB) LGE protocols improves LGE images, but their efficacy with different ICD types is not well known. PURPOSE: To assess the effects of WB LGE imaging on DRA in different non-MR conditional ICD subtypes. STUDY TYPE: Retrospective. POPULATION: A total of 113 patients undergoing cardiac magnetic resonance imaging with three ICD subtypes: transvenous (TV-ICD, N = 48), cardiac-resynchronization therapy device (CRT-D, N = 48), and subcutaneous (S-ICD, N = 17). FIELD STRENGTH/SEQUENCE: 5 T scanner, standard LGE, and WB LGE imaging with a phase-sensitive inversion recovery segmented gradient echo sequence. ASSESSMENT: DRA burden was defined as the number of artifact-positive short-axis LGE slices as percentage of the total number of short-axis slices covering the left ventricle from based to apex, and was determined for WB and standard LGE studies for each patient. Additionally, artifact area on each slice was quantified. STATISTICAL TESTS: Shapiro-Wilks, Kruskal-Wallis analysis of variance, Dunn tests with Bonferroni correction, and Mann-Whitney U-test. RESULTS: In patients with TV-ICD, DRA burden was significantly reduced and nearly eliminated with WB LGE compared to standard LGE imaging (median [interquartile range]: 0 [0-7]% vs. 18 [0-50]%, P < 0.05), but WB imaging had less of an impact on DRA in the CRT-D (8 [0-23]% vs. 16 [0-45]%, p = 0.12) and S-ICD (60 [15-71]% vs. 67 [50-92]%, P = 0.09) patients. Residual DRA was significantly greater (P < 0.05) for S-ICD compared to other device types with WB LGE imaging, despite the generators of all three ICD types having similar proximity to the heart. The area of S-ICD associated DRA was smaller with WB LGE (P < 0.001) than with standard LGE imaging and the artifacts had different characteristics (dark signal void instead of a bright hyperenhancement artifact). DATA CONCLUSION: Although WB LGE imaging reduced the burden of DRA caused by S-ICD, the residual artifact was greater than that observed with TV-ICD and CRT-D devices. Further developments are needed to better resolve S-ICD artifacts. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: STAGE: 5.

Cell-Laden Gradient Hydrogel Scaffolds for Neovascularization of Engineered Tissues.

Gradients in mechanical properties, physical architecture and biochemical composition exist in a variety of complex tissues, yet 3D in vitro models that enable investigation of these cues on cellular processes, especially those contributing to vascularization of engineered tissues are limited. Here, a photopolymerization approach to create cell-laden hydrogel biomaterials with decoupled and combined gradients in modulus, immobilized cell adhesive peptide (RGD) concentration, and proteolytic degradation enabling spatial encapsulation of vascular spheroids is reported to elucidate their impact on vascular sprouting in 3D culture. Vascular spheroids encapsulated in these gradient scaffolds exhibit spatial variations in total sprout length. Scaffolds presenting an immobilized RGD gradient promote biased vascular sprouting toward increasing RGD concentration. Importantly, biased sprouting is found to be dependent on immobilized RGD gradient characteristics, including magnitude and slope, with increases in these factors contributing to significant enhancements in biased sprouting responses. Conversely, reduction in biased sprouting responses is observed in combined gradient scaffolds possessing opposing gradients in RGD and modulus. The presented work is the first to demonstrate the use of a cell-laden biomaterial platform to systematically investigate the role of multiple scaffold gradients as well as gradient slope, magnitude and orientation on vascular sprouting responses in 3D culture.

Myocardial strain analysis of the right ventricle: comparison of different cardiovascular magnetic resonance and echocardiographic techniques.

BACKGROUND: Right ventricular (RV) strain is a useful predictor of prognosis in various cardiovascular diseases, including those traditionally believed to impact only the left ventricle. We aimed to determine inter-modality and inter-technique agreement in RV longitudinal strain (LS) measurements between currently available cardiovascular magnetic resonance (CMR) and echocardiographic techniques, as well as their reproducibility and the impact of layer-specific strain measurements. METHODS: RV-LS was determined in 62 patients using 2D speckle tracking echocardiography (STE, Epsilon) and two CMR techniques: feature tracking (FT) and strain-encoding (SENC), and in 17 healthy subjects using FT and SENC only. Measurements included global and free-wall LS (GLS, FWLS). Inter-technique agreement was assessed using linear regression and Bland-Altman analysis. Reproducibility was quantified using intraclass correlation (ICC) and coefficients of variation (CoV). RESULTS: We found similar moderate agreement between both CMR techniques and STE in patients: r = 0.57-0.63 for SENC; r = 0.50-0.62 for FT. The correlation between SENC and STE was better for GLS (r = 0.63) than for FWLS (r = 0.57). Conversely, the correlation between FT and STE was higher for FWLS (r = 0.60-0.62) than GLS (r = 0.50-0.54). FT-midmyocardial strain correlated better with SENC and STE than FT-subendocardial strain. The agreement between SENC and FT was fair (r = 0.36-0.41, bias: - 6.4 to - 10.4%) in the entire study group. All techniques except FT showed excellent reproducibility (ICC: 0.62-0.96, CoV: 0.04-0.30). CONCLUSIONS: We found only moderate inter-modality agreement with STE in RV-LS for both FT and SENC and poor agreement when comparing between the CMR techniques. Different modalities and techniques should not be used interchangeably to determine and monitor RV strain.

Immobilized RGD concentration and proteolytic degradation synergistically enhance vascular sprouting within hydrogel scaffolds of varying modulus.

Insufficient vascularization limits the volume and complexity of engineered tissue. The formation of new blood vessels (neovascularization) is regulated by a complex interplay of cellular interactions with biochemical and biophysical signals provided by the extracellular matrix (ECM) necessitating the development of biomaterial approaches that enable systematic modulation in matrix properties. To address this need poly(ethylene) glycol-based hydrogel scaffolds were engineered with a range of decoupled and combined variations in integrin-binding peptide (RGD) ligand concentration, elastic modulus and proteolytic degradation rate using free-radical polymerization chemistry. The modularity of this system enabled a full factorial experimental design to simultaneously investigate the individual and interaction effects of these matrix cues on vascular sprout formation in 3 D culture. Enhancements in scaffold proteolytic degradation rate promoted significant increases in vascular sprout length and junction number while increases in modulus significantly and negatively impacted vascular sprouting. We also observed that individual variations in immobilized RGD concentration did not significantly impact 3 D vascular sprouting. Our findings revealed a previously unidentified and optimized combination whereby increases in both immobilized RGD concentration and proteolytic degradation rate resulted in significant and synergistic enhancements in 3 D vascular spouting. The above-mentioned findings would have been challenging to uncover using one-factor-at-time experimental analyses.

Echocardiography and cardiovascular magnetic resonance based evaluation of myocardial strain and relationship with late gadolinium enhancement.

OBJECTIVES: We sought to: (1) determine the agreement in cardiovascular magnetic resonance (CMR) and speckle tracking echocardiography (STE) derived strain measurements, (2) compare their reproducibility, (3) determine which approach is best related to CMR late gadolinium enhancement (LGE). BACKGROUND: While STE-derived strain is routinely used to assess left ventricular (LV) function, CMR strain measurements are not yet standardized. Strain can be measured using dedicated pulse sequences (strain-encoding, SENC), or post-processing of cine images (feature tracking, FT). It is unclear whether these measurements are interchangeable, and whether strain can be used as an alternative to LGE. METHODS: Fifty patients underwent 2D echocardiography and 1.5 T CMR. Global longitudinal strain (GLS) was measured by STE (Epsilon), FT (NeoSoft) and SENC (Myocardial Solutions) and circumferential strain (GCS) by FT and SENC. RESULTS: GLS showed good inter-modality agreement (r-values: 0.71-0.75), small biases (< 1%) but considerable limits of agreement (- 7 to 8%). The agreement between the CMR techniques was better for GLS than GCS (r = 0.81 vs 0.67; smaller bias). Repeated measurements showed low intra- and inter-observer variability for both GLS and GCS (intraclass correlations 0.86-0.99; coefficients of variation 3-13%). LGE was present in 22 (44%) of patients. Both SENC- and FT-derived GLS and GCS were associated with LGE, while STE-GLS was not. Irrespective of CMR technique, this association was stronger for GCS (AUC 0.77-0.78) than GLS (AUC 0.67-0.72) and STE-GLS (AUC = 0.58). CONCLUSION: There is good inter-technique agreement in strain measurements, which were highly reproducible, irrespective of modality or analysis technique. GCS may better reflect the presence of underlying LGE than GLS.

Improved visualization of the coronary arteries using motion correction during vasodilator stress CT myocardial perfusion imaging.

BACKGROUND: Vasodilator stress computed tomography perfusion (sCTP) imaging is complementary to coronary CT angiography (CCTA), used to determine the hemodynamic significance of coronary artery disease. However, it requires a separate image acquisition due to motion artifacts caused by higher heart rates during stress, resulting in increased iodine contrast dose and radiation. We sought to determine whether a novel motion correction algorithm applied to stress images would improve the visualization of the coronary arteries to potentially allow CCTA + sCTP evaluation in a single scan. METHODS: 28 patients referred for clinically indicated CCTA (iCT, Philips) underwent sCTP imaging (retrospective-gating with dose modulation; 100 kVp and 250 mA; 5.2 ± 4.3 mSv) after regadenoson (0.4 mg, Astellas). Stress images were reconstructed using standard filtered back-projection (FBP) and also processed to generate interaction-free coronary motion-compensated back-projection reconstructions (MCR). Each coronary artery from standard FBP and MCR images was viewed side-by-side by a reader blinded to the reconstruction technique, who graded severity of motion artifact by segment (scale 0-5, with 3 as the threshold for diagnostic quality) and to measure signal-to-noise and contrast-to-noise ratios (SNR, CNR). RESULTS: Visualization scores were higher with MCR for all coronary segments, including 14/86 (16%) segments deemed as non-diagnostic on FBP images. SNR (7 ± 2) and CNR (15 ± 8) were unchanged by motion-correction (7 ± 3, p = 0.88 and 15 ± 5, p = 0.94, respectively). CONCLUSIONS: MCR improves the visualization of coronary anatomy on sCTP images without degrading image characteristics. This algorithm is an important step towards the combined assessment of coronary anatomy and myocardial perfusion in a single scan, which will reduce study time, radiation exposure and contrast dose.

Strain-encoded cardiac magnetic resonance imaging: a new approach for fast estimation of left ventricular function.

BACKGROUND: Recently introduced fast strain-encoded (SENC) cardiac magnetic resonance (CMR) imaging (fast-SENC) provides real-time acquisition of myocardial performance in a single heartbeat. We aimed to test the ability and accuracy of real-time strain-encoded CMR imaging to estimate left ventricular volumes, ejection fraction and mass. METHODS: Thirty-five subjects (12 healthy volunteers and 23 patients with known or suspected coronary artery disease) were investigated. All study participants were imaged at 1.5 Tesla MRI scanner (Achieva, Philips) using an advanced CMR study protocol which included conventional cine and fast-SENC imaging. A newly developed real-time free-breathing SENC imaging technique based on the acquisition of two images with different frequency modulation was employed. RESULTS: All parameters were successfully derived from fast-SENC images with total study time of 105 s (a 15 s scan time and a 90 s post-processing time). There was no significant difference between fast-SENC and cine imaging in the estimation of LV volumes and EF, whereas fast-SENC underestimated LV end-diastolic mass by 7%. CONCLUSION: The single heartbeat fast-SENC technique can be used as a good alternative to cine imaging for the precise calculation of LV volumes and ejection fraction while the technique significantly underestimates LV end-diastolic mass.