A simplified method to correct saturation of arterial input function for cardiac magnetic resonance first-pass perfusion imaging: validation with simultaneously acquired PET.

BACKGROUND: First-pass perfusion imaging in magnetic resonance imaging (MRI) is an established method to measure myocardial blood flow (MBF). An obstacle for accurate quantification of MBF is the saturation of blood pool signal intensity used for arterial input function (AIF). The objective of this project was to validate a new simplified method for AIF estimation obtained from single-bolus and single sequence perfusion measurements. The reference MBF was measured simultaneously on 13N-ammonia positron emission tomography (PET). METHODS: Sixteen patients with clinically confirmed myocardial ischemia were imaged in a clinical whole-body PET-MRI system. PET perfusion imaging was performed in a 10-min acquisition after the injection of 10 mCi of 13N-ammonia. The MRI perfusion acquisition started simultaneously with the start of the PET acquisition after the injection of a 0.075 mmol/kg gadolinium contrast agent. Cardiac stress imaging was initiated after the administration of regadenoson 20 s prior to PET-MRI scanning. The saturation part of the MRI AIF data was modeled as a gamma variate curve, which was then estimated for a true AIF by minimizing a cost function according to various boundary conditions. A standard AHA 16-segment model was used for comparative analysis of absolute MBF from PET and MRI. RESULTS: Overall, there were 256 segments in 16 patients, mean resting perfusion for PET was 1.06 ± 0.34 ml/min/g and 1.04 ± 0.30 ml/min/g for MRI (P = 0.05), whereas mean stress perfusion for PET was 2.00 ± 0.74 ml/min/g and 2.12 ± 0.76 ml/min/g for MRI (P < 0.01). Linear regression analysis in MBF revealed strong correlation (r = 0.91, slope = 0.96, P < 0.001) between PET and MRI. Myocardial perfusion reserve, calculated from the ratio of stress MBF over resting MBF, also showed a strong correlation between MRI and PET measurements (r = 0.82, slope = 0.81, P < 0.001). CONCLUSION: The results demonstrated the feasibility of the simplified AIF estimation method for the accurate quantification of MBF by MRI with single sequence and single contrast injection. The MRI MBF correlated strongly with PET MBF obtained simultaneously. This post-processing technique will allow easy transformation of clinical perfusion imaging data into quantitative information.

Intravenous contrast-free standardized exercise perfusion imaging in diabetic feet with ulcers.

BACKGROUND: Impaired foot perfusion is a primary contributor to foot ulcer formation. There is no existing device nor method that can be used to measure local foot perfusion during standardized foot muscle exercise in an MRI environment. PURPOSE: To develop a new MRI-compatible foot dynamometer and MRI methods to characterize local perfusion in diabetic feet with ulcers. STUDY TYPE: Prospective. POPULATION/SUBJECTS: Seven participants without diabetes and 10 participants with diabetic foot ulcers. FIELD STRENGTH/SEQUENCE: 3.0T, arterial spin labeling (ASL). ASSESSMENTS: Using a new MRI-compatible foot dynamometer, all participants underwent MRI ASL perfusion assessment at rest and during a standardized toe-flexion exercise. The participants without diabetes were scanned twice to assess the reproducibility of perfusion measurements. The absolute perfusion and perfusion reserve values were compared between two groups and between regions near ulcers (peri-ulcer) and away from ulcers (away-ulcer). STATISTICAL TESTS: Bland-Altman methods for the calculation of coefficient of repeatability (CR) and two-sided and unpaired Student's t-test to compare multiple differences. RESULTS: The perfusion reserves measured had the best reproducibility (CR in medial region: 1.6, lateral region: 0.9). The foot perfusion reserve was significantly lower in the participants with diabetes compared with the participants without diabetes (1.34 ± 0.32, 95% confidence interval [CI]: 1.1, 1.58 vs. 1.76 ± 0.31, 95% CI: 1.53, 1.98, P = 0.02). Both peri-ulcer exercise perfusion (8.7 ± 3.9 ml/min/100g) and perfusion reserve (1.07 ± 0.39, 95% CI: 0.78, 1.35) were significantly lower than away-ulcer exercise perfusion (12.7 ± 3.8 ml/min/100g, P = 0.02) and perfusion reserve (1.39 ± 0.37, 95% CI: 1.11, 1.66, P = 0.03), respectively. DATA CONCLUSION: This study demonstrates intravenous contrast-free methods for local perfusion in feet with ulcers by standardized exercise-based MRI. Ischemia regions around foot ulcers can be quantitatively distinguished from normal perfused muscle regions. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:474-480.

Oximetric angiosome imaging in diabetic feet.

PURPOSE: To develop a noncontrast oximetric angiosome imaging approach to assess skeletal muscle oxygenation in diabetic feet. MATERIALS AND METHODS: In four healthy and five subjects with diabetes, the feasibility of foot oximetry was examined using a 3T clinical magnetic resonance imaging (MRI) scanner. The subjects' feet were scanned at rest and during a toe-flexion isometric exercise. The oxygen extraction fraction of skeletal muscle was measured using a susceptibility-based MRI method. Our newly developed MR foot oximetric angiosome model was compared with the traditional angiosome model in the assessment of the distribution of oxygen extraction fraction. RESULTS: Using the traditional angiosome during the toe-flexion exercise, the oxygen extraction fraction in the medial foot of healthy subjects increased (4.9 ± 3%) and decreased (-2.7 ± 4.4%) in subjects with diabetes (difference = 7.6%; 95% confidence interval = -13.7 ± 1.4; P = 0.02). Using the oximetric angiosome, the percent difference in the areas of oxygen extraction fraction within the 0.7-1.0 range (expected oxygen extraction fraction during exercise) between rest and exercise was higher in healthy subjects (8 ± 4%) than in subjects with diabetes (4 ± 4%; P = 0.02). CONCLUSION: This study demonstrates the feasibility of measuring skeletal muscle oxygen extraction fraction in the foot muscle during a toe-flexion isometric exercise. Instead of assessing oxygen extraction fraction in a foot muscle region linked to a supplying artery (traditional angiosome), the foot oximetric angiosome model assesses oxygen extraction fraction by its different levels in all foot muscle regions and thus may be more appropriate for assessing local ischemia in ulcerated diabetic feet. J. Magn. Reson. Imaging 2016. J. MAGN. RESON. IMAGING 2016;44:940-946.

Morphological and Stress Vulnerability Indices for Human Coronary Plaques and Their Correlations with Cap Thickness and Lipid Percent: An IVUS-Based Fluid-Structure Interaction Multi-patient Study.

Plaque vulnerability, defined as the likelihood that a plaque would rupture, is difficult to quantify due to lack of in vivo plaque rupture data. Morphological and stress-based plaque vulnerability indices were introduced as alternatives to obtain quantitative vulnerability assessment. Correlations between these indices and key plaque features were investigated. In vivo intravascular ultrasound (IVUS) data were acquired from 14 patients and IVUS-based 3D fluid-structure interaction (FSI) coronary plaque models with cyclic bending were constructed to obtain plaque wall stress/strain and flow shear stress for analysis. For the 617 slices from the 14 patients, lipid percentage, min cap thickness, critical plaque wall stress (CPWS), strain (CPWSn) and flow shear stress (CFSS) were recorded, and cap index, lipid index and morphological index were assigned to each slice using methods consistent with American Heart Association (AHA) plaque classification schemes. A stress index was introduced based on CPWS. Linear Mixed-Effects (LME) models were used to analyze the correlations between the mechanical and morphological indices and key morphological factors associated with plaque rupture. Our results indicated that for all 617 slices, CPWS correlated with min cap thickness, cap index, morphological index with r = -0.6414, 0.7852, and 0.7411 respectively (p<0.0001). The correlation between CPWS and lipid percentage, lipid index were weaker (r = 0.2445, r = 0.2338, p<0.0001). Stress index correlated with cap index, lipid index, morphological index positively with r = 0.8185, 0.3067, and 0.7715, respectively, all with p<0.0001. For all 617 slices, the stress index has 66.77% agreement with morphological index. Morphological and stress indices may serve as quantitative plaque vulnerability assessment supported by their strong correlations with morphological features associated with plaque rupture. Differences between the two indices may lead to better plaque assessment schemes when both indices were jointly used with further validations from clinical studies.

Noncontrast skeletal muscle oximetry.

PURPOSE: The objective of this study was to develop a new noncontrast method to directly quantify regional skeletal muscle oxygenation. METHODS: The feasibility of the method was examined in five healthy volunteers using a 3 T clinical MRI scanner, at rest and during a sustained isometric contraction. The perfusion of skeletal muscle of the calf was measured using an arterial spin labeling method, whereas the oxygen extraction fraction of the muscle was measured using a susceptibility-based MRI technique. RESULTS: In all volunteers, the perfusion in soleus muscle increased significantly from 6.5 ± 2.0 mL (100 g min)(-1) at rest to 47.9 ± 7.7 mL (100 g min)(-1) during exercise (P < 0.05). Although the corresponding oxygen extraction fraction did not change significantly, the rate of oxygen consumption increased from 0.43 ± 0.13 to 4.2 ± 1.5 mL (100 g min)(-1) (P < 0.05). Similar results were observed in gastrocnemius muscle but with greater oxygen extraction fraction increase than the soleus muscle. CONCLUSION: This is the first MR oximetry developed for quantification of regional skeletal muscle oxygenation. A broad range of medical conditions could benefit from these techniques, including cardiology, gerontology, kinesiology, and physical therapy.

Human coronary plaque wall thickness correlated positively with flow shear stress and negatively with plaque wall stress: an IVUS-based fluid-structure interaction multi-patient study.

BACKGROUND: Atherosclerotic plaque progression and rupture are believed to be associated with mechanical stress conditions. In this paper, patient-specific in vivo intravascular ultrasound (IVUS) coronary plaque image data were used to construct computational models with fluid-structure interaction (FSI) and cyclic bending to investigate correlations between plaque wall thickness and both flow shear stress and plaque wall stress conditions. METHODS: IVUS data were acquired from 10 patients after voluntary informed consent. The X-ray angiogram was obtained prior to the pullback of the IVUS catheter to determine the location of the coronary artery stenosis, vessel curvature and cardiac motion. Cyclic bending was specified in the model representing the effect by heart contraction. 3D anisotropic FSI models were constructed and solved to obtain flow shear stress (FSS) and plaque wall stress (PWS) values. FSS and PWS values were obtained for statistical analysis. Correlations with p < 0.05 were deemed significant. RESULTS: Nine out of the 10 patients showed positive correlation between wall thickness and flow shear stress. The mean Pearson correlation r-value was 0.278 ± 0.181. Similarly, 9 out of the 10 patients showed negative correlation between wall thickness and plaque wall stress. The mean Pearson correlation r-value was -0.530 ± 0.210. CONCLUSION: Our results showed that plaque vessel wall thickness correlated positively with FSS and negatively with PWS. The patient-specific IVUS-based modeling approach has the potential to be used to investigate and identify possible mechanisms governing plaque progression and rupture and assist in diagnosis and intervention procedures. This represents a new direction of research. Further investigations using more patient follow-up data are warranted.

T(2) preparation method for measuring hyperemic myocardial O(2) consumption: in vivo validation by positron emission tomography.

PURPOSE: To validate a new T(2) -prepared method for the quantification of regional myocardial O(2) consumption during pharmacologic stress with positron emission tomography (PET). MATERIALS AND METHODS: A T(2) prepared gradient-echo sequence was modified to measure myocardial T(2) within a single breath-hold. Six beagle dogs were randomly selected for the induction of coronary artery stenosis. Magnetic resonance imaging (MRI) experiments were performed with the T(2) imaging and first-pass perfusion imaging at rest and during either dobutamine- or dipyridamole-induced hyperemia. Myocardial blood flow (MBF) was quantified using a previously developed model-free algorithm. Hyperemic myocardial O(2) extraction fraction (OEF) and consumption (MVO(2) ) were calculated using a two-compartment model developed previously. PET imaging using (11) C-acetate and (15) O-water was performed in the same day to validate OEF, MBF, and MVO(2) measurements. RESULTS: The T(2) -prepared mapping sequence measured regional myocardial T(2) with a repeatability of 2.3%. By myocardial segment-basis analysis, MBF measured by MRI is closely correlated with that measured by PET (R(2) = 0.85, n = 22). Similar correlation coefficients were observed for hyperemic OEF (R(2) = 0.90, n = 9, mean difference of PET - MRI = -2.4%) and MVO(2) (R(2) = 0.83, n = 7, mean difference = 4.2%). CONCLUSION: The T(2) -prepared imaging method may allow quantitative estimation of regional myocardial oxygenation with relatively good accuracy. The precision of the method remains to be improved.

Using intravascular ultrasound image-based fluid-structure interaction models and machine learning methods to predict human coronary plaque vulnerability change.

Plaque vulnerability prediction is of great importance in cardiovascular research. In vivo follow-up intravascular ultrasound (IVUS) coronary plaque data were acquired from nine patients to construct fluid-structure interaction models to obtain plaque biomechanical conditions. Morphological plaque vulnerability index (MPVI) was defined to measure plaque vulnerability. The generalized linear mixed regression model (GLMM), support vector machine (SVM) and random forest (RF) were introduced to predict MPVI change (ΔMPVI = MPVIfollow-up‒MPVIbaseline) using ten risk factors at baseline. The combination of mean wall thickness, lumen area, plaque area, critical plaque wall stress, and MPVI was the best predictor using RF with the highest prediction accuracy 91.47%, compared to 90.78% from SVM, and 85.56% from GLMM. Machine learning method (RF) improved the prediction accuracy by 5.91% over that from GLMM. MPVI was the best single risk factor using both GLMM (82.09%) and RF (78.53%) while plaque area was the best using SVM (81.29%).

Fluid-structure interaction models based on patient-specific IVUS at baseline and follow-up for prediction of coronary plaque progression by morphological and biomechanical factors: A preliminary study.

Plaque morphology and biomechanics are believed to be closely associated with plaque progression. In this paper, we test the hypothesis that integrating morphological and biomechanical risk factors would result in better predictive power for plaque progression prediction. A sample size of 374 intravascular ultrasound (IVUS) slices was obtained from 9 patients with IVUS follow-up data. 3D fluid-structure interaction models were constructed to obtain both structural stress/strain and fluid biomechanical conditions. Data for eight morphological and biomechanical risk factors were extracted for each slice. Plaque area increase (PAI) and wall thickness increase (WTI) were chosen as two measures for plaque progression. Progression measure and risk factors were fed to generalized linear mixed models and linear mixed-effect models to perform prediction and correlation analysis, respectively. All combinations of eight risk factors were exhausted to identify the optimal predictor(s) with highest prediction accuracy defined as sum of sensitivity and specificity. When using a single risk factor, plaque wall stress (PWS) at baseline was the best predictor for plaque progression (PAI and WTI). The optimal predictor among all possible combinations for PAI was PWS + PWSn + Lipid percent + Min cap thickness + Plaque Area (PA) + Plaque Burden (PB) (prediction accuracy = 1.5928) while Wall Thickness (WT) + Plaque Wall Strain (PWSn) + Plaque Area (PA) was the best for WTI (1.2589). This indicated that PAI was a more predictable measure than WTI. The combination including both morphological and biomechanical parameters had improved prediction accuracy, compared to predictions using only morphological features.

A non-contrast CMR index for assessing myocardial fibrosis.

PURPOSE: Safe, sensitive, and non-invasive imaging methods to assess the presence, extent, and turnover of myocardial fibrosis are needed for early stratification of risk in patients who might develop heart failure after myocardial infarction. We describe a non-contrast cardiac magnetic resonance (CMR) approach for sensitive detection of myocardial fibrosis using a canine model of myocardial infarction and reperfusion. METHODS: Seven dogs had coronary thrombotic occlusion of the left anterior descending coronary arteries followed by fibrinolytic reperfusion. CMR studies were performed at 7days after reperfusion. A CMR spin-locking T1ρ mapping sequence was used to acquire T1ρ dispersion data with spin-lock frequencies of 0 and 511Hz. A fibrosis index map was derived on a pixel-by-pixel basis. CMR native T1 mapping, first-pass myocardial perfusion imaging, and post-contrast late gadolinium enhancement imaging were also performed for assessing myocardial ischemia and fibrosis. Hearts were dissected after CMR for histopathological staining and two myocardial tissue segments from the septal regions of adjacent left ventricular slices were qualitatively assessed to grade the extent of myocardial fibrosis. RESULTS: Histopathology of 14 myocardial tissue segments from septal regions was graded as grade 1 (fibrosis area, <20% of a low power field, n=9), grade 2 (fibrosis area, 20-50% of field, n=4), or grade 3 (fibrosis area, >50% of field, n=1). A dramatic difference in fibrosis index (183%, P<0.001) was observed by CMR from grade 1 to 2, whereas differences were much smaller for T1ρ (9%, P=0.14), native T1 (5.5%, P=0.12), and perfusion (-21%, P=0.05). CONCLUSION: A non-contrast CMR index based on T1ρ dispersion contrast was shown in preliminary studies to detect and correlate with the extent of myocardial fibrosis identified histopathologically. A non-contrast approach may have important implications for managing cardiac patients with heart failure, particularly in the presence of impaired renal function.

3D MRI-based multicomponent thin layer structure only plaque models for atherosclerotic plaques.

MRI-based fluid-structure interactions (FSI) models for atherosclerotic plaques have been developed to perform mechanical analysis to investigate the association of plaque wall stress (PWS) with cardiovascular disease. However, the time consuming 3D FSI model construction process is a great hinder for its clinical implementations. In this study, a 3D thin-layer structure only (TLS) plaque model was proposed as an approximation with much less computational cost to 3D FSI models for better clinical implementation potential. 192 TLS models were constructed based on 192 ex vivo MRI Images of 12 human coronary atherosclerotic plaques. Plaque stresses were extracted from all lumen nodal points. The maximum value of Plaque wall stress (MPWS) and average value of plaque wall stress (APWS) of each slice were used to compare with those from corresponding FSI models. The relative errors for MPWS and APWS were 9.76% and 9.89%, respectively. Both MPWS and APWS values obtained from TLS models showed very good correlation with those from 3D FSI models. Correlation results from TLS models were in consistent with FSI models. Our results indicated that the proposed 3D TLS plaque models may be used as a good approximation to 3D FSI models with much less computational cost. With further validation, 3D TLS models may be possibly used to replace FSI models to save time and perform mechanical analysis for atherosclerotic plaques for clinical implementation.

IVUS-based FSI models for human coronary plaque progression study: components, correlation and predictive analysis.

Atherosclerotic plaque progression is believed to be associated with mechanical stress conditions. Patient follow-up in vivo intravascular ultrasound coronary plaque data were acquired to construct fluid-structure interaction (FSI) models with cyclic bending to obtain flow wall shear stress (WSS), plaque wall stress (PWS) and strain (PWSn) data and investigate correlations between plaque progression measured by wall thickness increase (WTI), cap thickness increase (CTI), lipid depth increase (LDI) and risk factors including wall thickness (WT), WSS, PWS, and PWSn. Quarter average values (n = 178-1016) of morphological and mechanical factors from all slices were obtained for analysis. A predictive method was introduced to assess prediction accuracy of risk factors and identify the optimal predictor(s) for plaque progression. A combination of WT and PWS was identified as the best predictor for plaque progression measured by WTI. Plaque WT had best overall correlation with WTI (r = -0.7363, p < 1E-10), cap thickness (r = 0.4541, p < 1E-10), CTI (r = -0.4217, p < 1E-8), LD (r = 0.4160, p < 1E-10), and LDI (r = -0.4491, p < 1E-10), followed by PWS (with WTI: (r = -0.3208, p < 1E-10); cap thickness: (r = 0.4541, p < 1E-10); CTI: (r = -0.1719, p = 0.0190); LD: (r = -0.2206, p < 1E-10); LDI: r = 0.1775, p < 0.0001). WSS had mixed correlation results.

Higher critical plaque wall stress in patients who died of coronary artery disease compared with those who died of other causes: a 3D FSI study based on ex vivo MRI of coronary plaques.

Mechanical forces play an important role in the rupture of vulnerable plaques. This process is often associated with cardiovascular syndromes, such as heart attack and stroke. In this study, magnetic resonance imaging (MRI)-based models were used to investigate the association between plaque wall stress (PWS) and coronary artery disease (CAD). Ex vivo MRI data of coronary plaques from 12 patients were used to construct 12 three-dimensional (3D) fluid-structure interaction (FSI) computational models. Six of the patients had died from CAD and six had died from non-CAD causes. PWS was assessed using all nodal points on the lumen surface of each plaque. The maximum PWS from all possible vulnerable sites of each plaque was defined as the 3D critical plaque wall stress (CPWS). Mean 3D CPWS in the CAD group was 94.3% higher than that in the non-CAD group (265.6 vs. 136.7 kPa, P=0.0029). There was no statistically significant difference in global maximum plaque wall stress (GMPWS) between the two groups (P=0.347). There was also no statistically significant difference in plaque burden between the CAD group (84.4±5%) and the non-CAD group (82.0±8%, P=0.552). The results indicate that plaques from patients who died from CAD were associated with higher CPWS compared with those from patients who died from non-CAD causes. With further validation, analysis of CPWS may prove to be an important component in assessment of plaque vulnerability.

IVUS-based computational modeling and planar biaxial artery material properties for human coronary plaque vulnerability assessment.

Image-based computational modeling has been introduced for vulnerable atherosclerotic plaques to identify critical mechanical conditions which may be used for better plaque assessment and rupture predictions. In vivo patient-specific coronary plaque models are lagging due to limitations on non-invasive image resolution, flow data, and vessel material properties. A framework is proposed to combine intravascular ultrasound (IVUS) imaging, biaxial mechanical testing and computational modeling with fluid-structure interactions and anisotropic material properties to acquire better and more complete plaque data and make more accurate plaque vulnerability assessment and predictions. Impact of pre-shrink-stretch process, vessel curvature and high blood pressure on stress, strain, flow velocity and flow maximum principal shear stress was investigated.