Automatic in-line quantitative myocardial perfusion mapping: Processing algorithm and implementation

Deep learning-based rapid image reconstruction and motion correction for high-resolution cartesian first-pass myocardial perfusion imaging at 3T

PURPOSE

To develop and evaluate a deep learning (DL) -based rapid image reconstruction and motion correction technique for high-resolution Cartesian first-pass myocardial perfusion imaging at 3T with whole-heart coverage for both single-slice (SS) and simultaneous multi-slice (SMS) acquisitions.

METHODS

3D physics-driven unrolled network architectures were utilized for the reconstruction of high-resolution Cartesian perfusion imaging. The SS and SMS multiband (MB) = 2 networks were trained from 135 slices from 20 subjects. Structural similarity index (SSIM), peak SNR (PSNR), and normalized RMS error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5, excellent; 1, poor). For respiratory motion correction, a 2D U-Net based motion corrected network was proposed, and the temporal fidelity and second-order derivative were calculated to assess the performance of the motion correction.

RESULTS

Excellent performance was demonstrated in the proposed technique with high SSIM and PSNR, and low NRMSE. Image quality scores were (4.3 [4.3, 4.4], 4.5 [4.4, 4.6], 4.3 [4.3, 4.4], and 4.5 [4.3, 4.5]) for SS DL and SS L1-SENSE, MB = 2 DL and MB = 2 SMS-L1-SENSE, respectively, showing no statistically significant difference (p > 0.05 for SS and SMS) between (SMS)-L1-SENSE and the proposed DL technique. The network inference time was around 4 s per dynamic perfusion series with 40 frames while the time of (SMS)-L1-SENSE with GPU acceleration was approximately 30 min.

CONCLUSION

The proposed DL-based image reconstruction and motion correction technique enabled rapid and high-quality reconstruction for SS and SMS MB = 2 high-resolution Cartesian first-pass perfusion imaging at 3T.

Automatic calculation of myocardial perfusion reserve using deep learning with uncertainty quantification

Background

Myocardial perfusion reserve index (MPRI) in magnetic resonance imaging (MRI) is an important indicator of ischemia, and its measurement typically involves manual procedures. The purposes of this study were to develop a fully automatic method for estimating the MPRI and to evaluate its performance.

Methods

The method consisted of segmenting the myocardium in dynamic contrast-enhanced (DCE) myocardial perfusion MRI data using Monte Carlo dropout U-Net, dividing the myocardium into segments based on landmark localization with machine learning, and estimating the MPRI after the calculation of the left ventricular and myocardial contrast upslopes. The proposed method was compared with a reference method, which involved manual adjustments of the myocardial contours and upslope ranges.

Results

In test subjects, MPRIs measured by the proposed technique correlated with those by the manual reference in segmental assessment [intraclass correlation coefficient (ICC) =0.75, 95% CI: 0.70-0.79, P<0.001]. The automatic and reference MPRI values showed a mean difference of -0.02 and 95% limits of agreement of (-0.86, 0.82).

Conclusions

The proposed automatic method is based on deep learning segmentation and machine learning landmark detection for MPRI measurements in DCE perfusion MRI. It holds the potential to efficiently and quantitatively assess myocardial ischemia without any user's interaction.

Microstructural and Microvascular Phenotype of Sarcomere Mutation Carriers and Overt Hypertrophic Cardiomyopathy

BACKGROUND

In hypertrophic cardiomyopathy (HCM), myocyte disarray and microvascular disease (MVD) have been implicated in adverse events, and recent evidence suggests that these may occur early. As novel therapy provides promise for disease modification, detection of phenotype development is an emerging priority. To evaluate their utility as early and disease-specific biomarkers, we measured myocardial microstructure and MVD in 3 HCM groups-overt, either genotype-positive (G+LVH+) or genotype-negative (G-LVH+), and subclinical (G+LVH-) HCM-exploring relationships with electrical changes and genetic substrate.

METHODS

This was a multicenter collaboration to study 206 subjects: 101 patients with overt HCM (51 G+LVH+ and 50 G-LVH+), 77 patients with G+LVH-, and 28 matched healthy volunteers. All underwent 12-lead ECG, quantitative perfusion cardiac magnetic resonance imaging (measuring myocardial blood flow, myocardial perfusion reserve, and perfusion defects), and cardiac diffusion tensor imaging measuring fractional anisotropy (lower values expected with more disarray), mean diffusivity (reflecting myocyte packing/interstitial expansion), and second eigenvector angle (measuring sheetlet orientation).

RESULTS

Compared with healthy volunteers, patients with overt HCM had evidence of altered microstructure (lower fractional anisotropy, higher mean diffusivity, and higher second eigenvector angle; all P<0.001) and MVD (lower stress myocardial blood flow and myocardial perfusion reserve; both P<0.001). Patients with G-LVH+ were similar to those with G+LVH+ but had elevated second eigenvector angle (P<0.001 after adjustment for left ventricular hypertrophy and fibrosis). In overt disease, perfusion defects were found in all G+ but not all G- patients (100% [51/51] versus 82% [41/50]; P=0.001). Patients with G+LVH- compared with healthy volunteers similarly had altered microstructure, although to a lesser extent (all diffusion tensor imaging parameters; P<0.001), and MVD (reduced stress myocardial blood flow [P=0.015] with perfusion defects in 28% versus 0 healthy volunteers [P=0.002]). Disarray and MVD were independently associated with pathological electrocardiographic abnormalities in both overt and subclinical disease after adjustment for fibrosis and left ventricular hypertrophy (overt: fractional anisotropy: odds ratio for an abnormal ECG, 3.3, P=0.01; stress myocardial blood flow: odds ratio, 2.8, P=0.015; subclinical: fractional anisotropy odds ratio, 4.0, P=0.001; myocardial perfusion reserve odds ratio, 2.2, P=0.049).

CONCLUSIONS

Microstructural alteration and MVD occur in overt HCM and are different in G+ and G- patients. Both also occur in the absence of hypertrophy in sarcomeric mutation carriers, in whom changes are associated with electrocardiographic abnormalities. Measurable changes in myocardial microstructure and microvascular function are early-phenotype biomarkers in the emerging era of disease-modifying therapy.

Approach to Imaging Ischemia in Women

Approach to imaging ischemia in women Coronary artery disease in women tends to have a worse short- and long-term prognosis relative to men and remains the leading cause of mortality worldwide. Both clinical symptoms and diagnostic approach remain challenging in women due to lesser likelihood of women presenting with classic anginal symptoms on one hand and underperformance of conventional exercise treadmill testing in women on the other. Moreover, a higher proportion of women with signs and symptoms suggestive of ischemia are more likely to have nonobstructive coronary artery disease (CAD) that requires additional imaging and therapeutic considerations. New imaging techniques such as coronary computed tomography (CT) angiography, CT myocardial perfusion imaging, CT functional flow reserve assessment, and cardiac magnetic resonance imaging carry substantially better sensitivity and specificity for the detection of ischemia and coronary artery disease in women. Familiarity with various clinical subtypes of ischemic heart disease in women and with the major advantages and disadvantages of advanced imaging tests to ensure the decision to select one modality over another is one of the keys to successful diagnosis of CAD in women. This review compares the 2 major types of ischemic heart disease in women - obstructive and nonobstructive, while focusing on sex-specific elements of its pathophysiology.

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.

Sex- and age-specific normal values for automated quantitative pixel-wise myocardial perfusion cardiovascular magnetic resonance

AIMS

Recently developed in-line automated cardiovascular magnetic resonance (CMR) myocardial perfusion mapping has been shown to be reproducible and comparable with positron emission tomography (PET), and can be easily integrated into clinical workflows. Bringing quantitative myocardial perfusion CMR into routine clinical care requires knowledge of sex- and age-specific normal values in order to define thresholds for disease detection. This study aimed to establish sex- and age-specific normal values for stress and rest CMR myocardial blood flow (MBF) in healthy volunteers.

METHODS AND RESULTS

A total of 151 healthy volunteers recruited from two centres underwent adenosine stress and rest myocardial perfusion CMR. In-line automatic reconstruction and post processing of perfusion data were implemented within the Gadgetron software framework, creating pixel-wise perfusion maps. Rest and stress MBF were measured, deriving myocardial perfusion reserve (MPR) and were subdivided by sex and age. Mean MBF in all subjects was 0.62 ± 0.13 mL/g/min at rest and 2.24 ± 0.53 mL/g/min during stress. Mean MPR was 3.74 ± 1.00. Compared with males, females had higher rest (0.69 ± 0.13 vs. 0.58 ± 0.12 mL/g/min, P < 0.01) and stress MBF (2.41 ± 0.47 vs. 2.13 ± 0.54 mL/g/min, P = 0.001). Stress MBF and MPR showed significant negative correlations with increasing age (r = -0.43, P < 0.001 and r = -0.34, P < 0.001, respectively).

CONCLUSION

Fully automated in-line CMR myocardial perfusion mapping produces similar normal values to the published CMR and PET literature. There is a significant increase in rest and stress MBF, but not MPR, in females and a reduction of stress MBF and MPR with advancing age, advocating the use of sex- and age-specific reference ranges for diagnostic use.

Left Ventricle Detection from Cardiac Magnetic Resonance Relaxometry Images Using Visual Transformer

Left Ventricle (LV) detection from Cardiac Magnetic Resonance (CMR) imaging is a fundamental step, preliminary to myocardium segmentation and characterization. This paper focuses on the application of a Visual Transformer (ViT), a novel neural network architecture, to automatically detect LV from CMR relaxometry sequences. We implemented an object detector based on the ViT model to identify LV from CMR multi-echo T2* sequences. We evaluated performances differentiated by slice location according to the American Heart Association model using 5-fold cross-validation and on an independent dataset of CMR T2*, T2, and T1 acquisitions. To the best of our knowledge, this is the first attempt to localize LV from relaxometry sequences and the first application of ViT for LV detection. We collected an Intersection over Union (IoU) index of 0.68 and a Correct Identification Rate (CIR) of blood pool centroid of 0.99, comparable with other state-of-the-art methods. IoU and CIR values were significantly lower in apical slices. No significant differences in performances were assessed on independent T2* dataset (IoU = 0.68, p = 0.405; CIR = 0.94, p = 0.066). Performances were significantly worse on the T2 and T1 independent datasets (T2: IoU = 0.62, CIR = 0.95; T1: IoU = 0.67, CIR = 0.98), but still encouraging considering the different types of acquisition. This study confirms the feasibility of the application of ViT architectures in LV detection and defines a benchmark for relaxometry imaging.

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.

Stress native T1 and native T2 mapping compared to myocardial perfusion reserve in long-term follow-up of severe Covid-19

Severe Covid-19 may cause a cascade of cardiovascular complications beyond viral pneumonia. The severe inflammation may affect the microcirculation which can be assessed by cardiovascular magnetic resonance (CMR) imaging using quantitative perfusion mapping and calculation of myocardial perfusion reserve (MPR). Furthermore, native T1 and T2 mapping have previously been shown to identify changes in myocardial perfusion by the change in native T1 and T2 during adenosine stress. However, the relationship between native T1, native T2, ΔT1 and ΔT2 with myocardial perfusion and MPR during long-term follow-up in severe Covid-19 is currently unknown. Therefore, patients with severe Covid-19 (n = 37, median age 57 years, 24% females) underwent 1.5 T CMR median 292 days following discharge. Quantitative myocardial perfusion (ml/min/g), and native T1 and T2 maps were acquired during adenosine stress, and rest, respectively. Both native T1 (R² = 0.35, p < 0.001) and native T2 (R² = 0.28, p < 0.001) correlated with myocardial perfusion. However, there was no correlation with ΔT1 or ΔT2 with MPR, respectively (p > 0.05 for both). Native T1 and native T2 correlate with myocardial perfusion during adenosine stress, reflecting the coronary circulation in patients during long-term follow-up of severe Covid-19. Neither ΔT1 nor ΔT2 can be used to assess MPR in patients with severe Covid-19.

Free-breathing motion-informed locally low-rank quantitative 3D myocardial perfusion imaging

Purpose

To propose respiratory motion‐informed locally low‐rank reconstruction (MI‐LLR) for robust free‐breathing single‐bolus quantitative 3D myocardial perfusion CMR imaging. Simulation and in‐vivo results are compared to locally low‐rank (LLR) and compressed sensing reconstructions (CS) for reference.

Methods

Data were acquired using a 3D Cartesian pseudo‐spiral in‐out k‐t undersampling scheme (R = 10) and reconstructed using MI‐LLR, which encompasses two stages. In the first stage, approximate displacement fields are derived from an initial LLR reconstruction to feed a motion‐compensated reference system to a second reconstruction stage, which reduces the rank of the inverse problem. For comparison, data were also reconstructed with LLR and frame‐by‐frame CS using wavelets as sparsifying transform (ℓ1‐wavelet).

Reconstruction accuracy relative to ground truth was assessed using synthetic data for realistic ranges of breathing motion, heart rates, and SNRs. In‐vivo experiments were conducted in healthy subjects at rest and during adenosine stress. Myocardial blood flow (MBF) maps were derived using a Fermi model.

Results

Improved uniformity of MBF maps with reduced local variations was achieved with MI‐LLR. For rest and stress, intra‐volunteer variation of absolute and relative MBF was lower in MI‐LLR (±0.17 mL/g/min [26%] and ±1.07 mL/g/min [33%]) versus LLR (±0.19 mL/g/min [28%] and ±1.22 mL/g/min [36%]) and versus ℓ1‐wavelet (±1.17 mL/g/min [113%] and ±6.87 mL/g/min [115%]). At rest, intra‐subject MBF variation was reduced significantly with MI‐LLR.

Conclusion

The combination of pseudo‐spiral Cartesian undersampling and dual‐stage MI‐LLR reconstruction improves free‐breathing quantitative 3D myocardial perfusion CMR imaging under rest and stress condition.

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Quantitative Myocardial Perfusion Predicts Outcomes in Patients With Prior Surgical Revascularization

BACKGROUND

Patients with previous coronary artery bypass graft (CABG) surgery typically have complex coronary disease and remain at high risk of adverse events. Quantitative myocardial perfusion indices predict outcomes in native vessel disease, but their prognostic performance in patients with prior CABG is unknown.

OBJECTIVES

In this study, we sought to evaluate whether global stress myocardial blood flow (MBF) and perfusion reserve (MPR) derived from perfusion mapping cardiac magnetic resonance (CMR) independently predict adverse outcomes in patients with prior CABG.

METHODS

This was a retrospective analysis of consecutive patients with prior CABG referred for adenosine stress perfusion CMR. Perfusion mapping was performed in-line with automated quantification of MBF. The primary outcome was a composite of all-cause mortality and major adverse cardiovascular events defined as nonfatal myocardial infarction and unplanned revascularization. Associations were evaluated with the use of Cox proportional hazards models after adjusting for comorbidities and CMR parameters.

RESULTS

A total of 341 patients (median age 67 years, 86% male) were included. Over a median follow-up of 638 days (IQR: 367-976 days), 81 patients (24%) reached the primary outcome. Both stress MBF and MPR independently predicted outcomes after adjusting for known prognostic factors (regional ischemia, infarction). The adjusted hazard ratio (HR) for 1 mL/g/min of decrease in stress MBF was 2.56 (95% CI: 1.45-4.35) and for 1 unit of decrease in MPR was 1.61 (95% CI: 1.08-2.38).

CONCLUSIONS

Global stress MBF and MPR derived from perfusion CMR independently predict adverse outcomes in patients with previous CABG. This effect is independent from the presence of regional ischemia on visual assessment and the extent of previous infarction.

Cardiac MR: From Theory to Practice

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.

Stress Cardiac Magnetic Resonance Myocardial Perfusion Imaging: JACC Review Topic of the Week

Stress cardiovascular magnetic resonance imaging (CMR) is a cost-effective, noninvasive test that accurately assesses myocardial ischemia, myocardial viability, and cardiac function without the need for ionizing radiation. There is a large body of literature, including randomized controlled trials, validating its diagnostic performance, risk stratification capabilities, and ability to guide appropriate use of coronary intervention. Specifically, stress CMR has shown higher diagnostic sensitivity than single-photon emission computed tomography imaging in detecting angiographically significant coronary artery disease. Stress CMR is particularly valuable for the evaluation of patients with moderate to high pretest probability of having stable ischemic heart disease and for patients known to have challenging imaging characteristics, including women, individuals with prior revascularization, and those with left ventricular dysfunction. This paper reviews the basics principles of stress CMR, the data supporting its clinical use, the added-value of myocardial blood flow quantification, and the assessment of myocardial function and viability routinely obtained during a stress CMR study.

A pilot randomised controlled trial of a structured, home-based exercise programme on cardiovascular structure and function in kidney transplant recipients: the ECSERT study design and methods

BACKGROUND

Cardiovascular disease (CVD) is a major cause of morbidity and mortality in kidney transplant recipients (KTRs). CVD risk scores underestimate risk in this population as CVD is driven by clustering of traditional and non-traditional risk factors, which lead to prognostic pathological changes in cardiovascular structure and function. While exercise may mitigate CVD in this population, evidence is limited, and physical activity levels and patient activation towards exercise and self-management are low. This pilot study will assess the feasibility of delivering a structured, home-based exercise intervention in a population of KTRs at increased cardiometabolic risk and evaluate the putative effects on cardiovascular structural and functional changes, cardiorespiratory fitness, quality of life, patient activation, healthcare utilisation and engagement with the prescribed exercise programme.

METHODS AND ANALYSIS

Fifty KTRs will be randomised 1:1 to: (1) the intervention; a 12week, home-based combined resistance and aerobic exercise intervention; or (2) the control; usual care. Intervention participants will have one introductory session for instruction and practice of the recommended exercises prior to receiving an exercise diary, dumbbells, resistance bands and access to instructional videos. The study will evaluate the feasibility of recruitment, randomisation, retention, assessment procedures and the intervention implementation. Outcomes, to be assessed prior to randomisation and postintervention, include: cardiac structure and function with stress perfusion cardiac MRI, cardiorespiratory fitness, physical function, blood biomarkers of cardiometabolic health, quality of life and patient activation. These data will be used to inform the power calculations for future definitive trials.

ETHICS AND DISSEMINATION

The protocol was reviewed and given favourable opinion by the East Midlands-Nottingham 2 Research Ethics Committee (reference: 19/EM/0209; 14 October 2019). Results will be published in peer-reviewed academic journals and will be disseminated to the patient and public community via social media, newsletter articles and presentations at conferences.

TRIAL REGISTRATION NUMBER

NCT04123951.

Influence of motion correction on the visual analysis of cardiac magnetic resonance stress perfusion imaging

OBJECTIVE

Image post-processing corrects for cardiac and respiratory motion (MoCo) during cardiovascular magnetic resonance (CMR) stress perfusion. The study analyzed its influence on visual image evaluation.

MATERIALS AND METHODS

Sixty-two patients with (suspected) coronary artery disease underwent a standard CMR stress perfusion exam during free-breathing. Image post-processing was performed without (non-MoCo) and with MoCo (image intensity normalization; motion extraction with iterative non-rigid registration; motion warping with the combined displacement field). Images were evaluated regarding the perfusion pattern (perfusion deficit, dark rim artifact, uncertain signal loss, and normal perfusion), the general image quality (non-diagnostic, imperfect, good, and excellent), and the reader's subjective confidence to assess the images (not confident, confident, very confident).

RESULTS

Fifty-three (non-MoCo) and 52 (MoCo) myocardial segments were rated as 'perfusion deficit', 113 vs. 109 as 'dark rim artifacts', 9 vs. 7 as 'uncertain signal loss', and 817 vs. 824 as 'normal'. Agreement between non-MoCo and MoCo was high with no diagnostic difference per-patient. The image quality of MoCo was rated more often as 'good' or 'excellent' (92 vs. 63%), and the diagnostic confidence more often as "very confident" (71 vs. 45%) compared to non-MoCo.

CONCLUSIONS

The comparison of perfusion images acquired during free-breathing and post-processed with and without motion correction demonstrated that both methods led to a consistent evaluation of the perfusion pattern, while the image quality and the reader's subjective confidence to assess the images were rated more favorably for MoCo.

Automated Quantitative Stress Perfusion Cardiac Magnetic Resonance in Pediatric Patients

Background: Myocardial ischemia occurs in pediatrics, as a result of both congenital and acquired heart diseases, and can lead to further adverse cardiac events if untreated. The aim of this work is to assess the feasibility of fully automated, high resolution, quantitative stress myocardial perfusion cardiac magnetic resonance (CMR) in a cohort of pediatric patients and to evaluate its agreement with the coronary anatomical status of the patients. Methods: Fourteen pediatric patients, with 16 scans, who underwent dual-bolus stress perfusion CMR were retrospectively analyzed. All patients also had anatomical coronary assessment with either CMR, CT, or X-ray angiography. The perfusion CMR images were automatically processed and quantified using an analysis pipeline previously developed in adults. Results: Automated perfusion quantification was successful in 15/16 cases. The coronary perfusion territories supplied by vessels affected by a medium/large aneurysm or stenosis (according to the AHA guidelines), induced by Kawasaki disease, an anomalous origin, or interarterial course had significantly reduced myocardial blood flow (MBF) (median (interquartile range), 1.26 (1.05, 1.67) ml/min/g) as compared to territories supplied by unaffected coronaries [2.57 (2.02, 2.69) ml/min/g, p < 0.001] and territories supplied by vessels with a small aneurysm [2.52 (2.45, 2.83) ml/min/g, p = 0.002]. Conclusion: Automatic CMR-derived MBF quantification is feasible in pediatric patients, and the technology could be potentially used for objective non-invasive assessment of ischemia in children with congenital and acquired heart diseases.

Landmark Detection in Cardiac MRI by Using a Convolutional Neural Network

PURPOSE

To develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI (CMR).

MATERIALS AND METHODS

This retrospective study included cine, late gadolinium enhancement (LGE), and T1 mapping examinations from two hospitals. The training set included 2329 patients (34 089 images; mean age, 54.1 years; 1471 men; December 2017 to March 2020). A hold-out test set included 531 patients (7723 images; mean age, 51.5 years; 323 men; May 2020 to July 2020). CNN models were developed to detect two mitral valve plane and apical points on long-axis images. On short-axis images, anterior and posterior right ventricular (RV) insertion points and left ventricular (LV) center points were detected. Model outputs were compared with manual labels assigned by two readers. The trained model was deployed to MRI scanners.

RESULTS

For the long-axis images, successful detection of cardiac landmarks ranged from 99.7% to 100% for cine images and from 99.2% to 99.5% for LGE images. For the short-axis images, detection rates were 96.6% for cine, 97.6% for LGE, and 98.7% for T1 mapping. The Euclidean distances between model-assigned and manually assigned labels ranged from 2 to 3.5 mm for different landmarks, indicating close agreement between model-derived landmarks and manually assigned labels. For all views and imaging sequences, no differences between the models' assessment of images and the readers' assessment of images were found for the anterior RV insertion angle or LV length. Model inference for a typical cardiac cine series took 610 msec with the graphics processing unit and 5.6 seconds with central processing unit.

CONCLUSION

A CNN was developed for landmark detection on both long- and short-axis CMR images acquired with cine, LGE, and T1 mapping sequences, and the accuracy of the CNN was comparable with the interreader variation.Keywords: Cardiac, Heart, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Feature Detection, Quantification, Supervised Learning, MR Imaging Supplemental material is available for this article. Published under a CC BY 4.0 license.

Myocardial Perfusion Defects in Hypertrophic Cardiomyopathy Mutation Carriers

Background

Impaired myocardial blood flow (MBF) in the absence of epicardial coronary disease is a feature of hypertrophic cardiomyopathy (HCM). Although most evident in hypertrophied or scarred segments, reduced MBF can occur in apparently normal segments. We hypothesized that impaired MBF and myocardial perfusion reserve, quantified using perfusion mapping cardiac magnetic resonance, might occur in the absence of overt left ventricular hypertrophy (LVH) and late gadolinium enhancement, in mutation carriers without LVH criteria for HCM (genotype‐positive, left ventricular hypertrophy‐negative).

Methods and Results

A single center, case‐control study investigated MBF and myocardial perfusion reserve (the ratio of MBF at stress:rest), along with other pre‐phenotypic features of HCM. Individuals with genotype‐positive, left ventricular hypertrophy‐negative (n=50) with likely pathogenic/pathogenic variants and no evidence of LVH, and matched controls (n=28) underwent cardiac magnetic resonance. Cardiac magnetic resonance identified LVH‐fulfilling criteria for HCM in 5 patients who were excluded. Individuals with genotype‐positive, left ventricular hypertrophy‐negative had longer indexed anterior mitral valve leaflet length (12.52±2.1 versus 11.55±1.6 mm/m², P=0.03), lower left ventricular end‐systolic volume (21.0±6.9 versus 26.7±6.2 mm/m², P≤0.005) and higher left ventricular ejection fraction (71.9±5.5 versus 65.8±4.4%, P≤0.005). Maximum wall thickness was not significantly different (9.03±1.95 versus 8.37±1.2 mm, P=0.075), and no subject had significant late gadolinium enhancement (minor right ventricle‒insertion point late gadolinium enhancement only). Perfusion mapping demonstrated visual perfusion defects in 9 (20%) carriers versus 0 controls (P=0.011). These were almost all septal or near right ventricle insertion points. Globally, myocardial perfusion reserve was lower in carriers (2.77±0.83 versus 3.24±0.63, P=0.009), with a subendocardial:subepicardial myocardial perfusion reserve gradient (2.55±0.75 versus 3.2±0.65, P=<0.005; 3.01±0.96 versus 3.47±0.75, P=0.026) but equivalent MBF (2.75±0.82 versus 2.65±0.69 mL/g per min, P=0.826).

Conclusions

Regional and global impaired myocardial perfusion can occur in HCM mutation carriers, in the absence of significant hypertrophy or scarring.

Use of quantitative cardiovascular magnetic resonance myocardial perfusion mapping for characterization of ischemia in patients with left internal mammary coronary artery bypass grafts

BACKGROUND

Quantitative myocardial perfusion mapping using cardiovascular magnetic resonance (CMR) is validated for myocardial blood flow (MBF) estimation in native vessel coronary artery disease (CAD). Following coronary artery bypass graft (CABG) surgery, perfusion defects are often detected in territories supplied by the left internal mammary artery (LIMA) graft, but their interpretation and subsequent clinical management is variable.

METHODS

We assessed myocardial perfusion using quantitative CMR perfusion mapping in 38 patients with prior CABG surgery, all with angiographically-proven patent LIMA grafts to the left anterior descending coronary artery (LAD) and no prior infarction in the LAD territory. Factors potentially determining MBF in the LIMA-LAD myocardial territory, including the impact of delayed contrast arrival through the LIMA graft were evaluated.

RESULTS

Perfusion defects were reported on blinded visual analysis in the LIMA-LAD territory in 27 (71%) cases, despite LIMA graft patency and no LAD infarction. Native LAD chronic total occlusion (CTO) was a strong independent predictor of stress MBF (B = - 0.41, p = 0.014) and myocardial perfusion reserve (MPR) (B = - 0.56, p = 0.005), and was associated with reduced stress MBF in the basal (1.47 vs 2.07 ml/g/min; p = 0.002) but not the apical myocardial segments (1.52 vs 1.87 ml/g/min; p = 0.057). Extending the maximum arterial time delay incorporated in the quantitative perfusion algorithm, resulted only in a small increase (3.4%) of estimated stress MBF.

CONCLUSIONS

Perfusion defects are frequently detected in LIMA-LAD subtended territories post CABG despite LIMA patency. Although delayed contrast arrival through LIMA grafts causes a small underestimation of MBF, perfusion defects are likely to reflect true reductions in myocardial blood flow, largely due to proximal native LAD disease.

A comparison of standard and high dose adenosine protocols in routine vasodilator stress cardiovascular magnetic resonance: dosage affects hyperaemic myocardial blood flow in patients with severe left ventricular systolic impairment

BACKGROUND

Adenosine stress perfusion cardiovascular magnetic resonance (CMR) is commonly used in the assessment of patients with suspected ischaemia. Accepted protocols recommend administration of adenosine at a dose of 140 µg/kg/min increased up to 210 µg/kg/min if required. Conventionally, adequate stress has been assessed using change in heart rate, however, recent studies have suggested that these peripheral measurements may not reflect hyperaemia and can be blunted, in particular, in patients with heart failure. This study looked to compare stress myocardial blood flow (MBF) and haemodynamic response with different dosing regimens of adenosine during stress perfusion CMR in patients and healthy controls.

METHODS

20 healthy adult subjects were recruited as controls to compare 3 adenosine perfusion protocols: standard dose (140 µg/kg/min for 4 min), high dose (210 µg/kg/min for 4 min) and long dose (140 µg/kg/min for 8 min). 60 patients with either known or suspected coronary artery disease (CAD) or with heart failure and different degrees of left ventricular (LV) dysfunction underwent adenosine stress with standard and high dose adenosine within the same scan. All studies were carried out on a 3 T CMR scanner. Quantitative global myocardial perfusion and haemodynamic response were compared between doses.

RESULTS

In healthy controls, no significant difference was seen in stress MBF between the 3 protocols. In patients with known or suspected CAD, and those with heart failure and mild systolic impairment (LV ejection fraction (LVEF) ≥ 40%) no significant difference was seen in stress MBF between standard and high dose adenosine. In those with LVEF < 40%, there was a significantly higher stress MBF following high dose adenosine compared to standard dose (1.33 ± 0.46 vs 1.10 ± 0.47 ml/g/min, p = 0.004). Non-responders to standard dose adenosine (defined by an increase in heart rate (HR) < 10 bpm) had a significantly higher stress HR following high dose (75 ± 12 vs 70 ± 14 bpm, p = 0.034), but showed no significant difference in stress MBF.

CONCLUSIONS

Increasing adenosine dose from 140 to 210 µg/kg/min leads to increased stress MBF in patients with significantly impaired LV systolic function. Adenosine dose in clinical perfusion assessment may need to be increased in these patients.

Quantitative perfusion-CMR is significantly influenced by the placement of the arterial input function

The aim of this study is to provide a systematic assessment of the influence of the position on the arterial input function (AIF) for perfusion quantification. In 39 patients with a wide range of left ventricular function the AIF was determined using a diluted contrast bolus of a cardiac magnetic resonance imaging in three left ventricular levels (basal, mid, apex) as well as aortic sinus (AoS). Time to peak signal intensities, baseline corrected peak signal intensity and upslopes were determined and compared to those obtained in the AoS. The error induced by sampling the AIF in a position different to the AoS was determined by Fermi deconvolution. The time to peak signal intensity was strongly correlated (r2 > 0.9) for all positions with a systematic earlier arrival in the basal (- 2153 ± 818 ms), the mid (- 1429 ± 928 ms) and the apical slice (- 450 ± 739 ms) relative to the AoS (all p < 0.001). Peak signal intensity as well as upslopes were strongly correlated (r2 > 0.9 for both) for all positions with a systematic overestimation in all positions relative to the AoS (all p < 0.001 and all p < 0.05). Differences between the positions were more pronounced for patients with reduced ejection fraction. The error of averaged MBF quantification was 8%, 13% and 27% for the base, mid and apex. The location of the AIF significantly influences core parameters for perfusion quantification with a systematic and ejection fraction dependent error. Full quantification should be based on obtaining the AIF as close as possible to the myocardium to minimize these errors.

Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning

Purpose

To develop a deep neural network–based computational workflow for inline myocardial perfusion analysis that automatically delineates the myocardium, which improves the clinical workflow and offers a “one-click” solution.

Materials and Methods

In this retrospective study, consecutive adenosine stress and rest perfusion scans were acquired from three hospitals between October 1, 2018 and February 27, 2019. The training and validation set included 1825 perfusion series from 1034 patients (mean age, 60.6 years ± 14.2 [standard deviation]). The independent test set included 200 scans from 105 patients (mean age, 59.1 years ± 12.5). A convolutional neural network (CNN) model was trained to segment the left ventricular cavity, myocardium, and right ventricle by processing an incoming time series of perfusion images. Model outputs were compared with manual ground truth for accuracy of segmentation and flow measures derived on a global and per-sector basis with t test performed for statistical significance. The trained models were integrated onto MR scanners for effective inference.

Results

The mean Dice ratio of automatic and manual segmentation was 0.93 ± 0.04. The CNN performed similarly to manual segmentation and flow measures for mean stress myocardial blood flow (MBF; 2.25 mL/min/g ± 0.59 vs 2.24 mL/min/g ± 0.59, P = .94) and mean rest MBF (1.08 mL/min/g ± 0.23 vs 1.07 mL/min/g ± 0.23, P = .83). The per-sector MBF values showed no difference between the CNN and manual assessment (P = .92). A central processing unit–based model inference on the MR scanner took less than 1 second for a typical perfusion scan of three slices.

Conclusion

The described CNN was capable of cardiac perfusion mapping and integrated an automated inline implementation on the MR scanner, enabling one-click analysis and reporting in a manner comparable to manual assessment.

Published under a CC BY 4.0 license.

Supplemental material is available for this article.

Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging

Dynamic contrast-enhanced quantitative first-pass perfusion using magnetic resonance imaging enables non-invasive objective assessment of myocardial ischemia without ionizing radiation. However, quantification of perfusion is challenging due to the non-linearity between the magnetic resonance signal intensity and contrast agent concentration. Furthermore, respiratory motion during data acquisition precludes quantification of perfusion. While motion correction techniques have been proposed, they have been hampered by the challenge of accounting for dramatic contrast changes during the bolus and long execution times. In this work we investigate the use of a novel free-breathing multi-echo Dixon technique for quantitative myocardial perfusion. The Dixon fat images, unaffected by the dynamic contrast-enhancement, are used to efficiently estimate rigid-body respiratory motion and the computed transformations are applied to the corresponding diagnostic water images. This is followed by a second non-linear correction step using the Dixon water images to remove residual motion. The proposed Dixon motion correction technique was compared to the state-of-the-art technique (spatiotemporal based registration). We demonstrate that the proposed method performs comparably to the state-of-the-art but is significantly faster to execute. Furthermore, the proposed technique can be used to correct for the decay of signal due to T2* effects to improve quantification and additionally, yields fat-free diagnostic images.

Pathophysiology, classification, and MRI parallels in microvascular disease of the heart and brain

Diagnostic imaging technology in vascular disease has long focused on large vessels and the pathologic processes that impact them. With improved diagnostic techniques, investigators are now able to uncover many underlying mechanisms and prognostic factors for microvascular disease. In the heart and brain, these pathologic entities include coronary microvascular disease and cerebral small vessel disease, both of which have significant impact on patients, causing angina, myocardial infarction, heart failure, stroke, and dementia. In the current paper, we will discuss parallels in pathophysiology, classification, and diagnostic modalities, with a focus on the role of magnetic resonance imaging in microvascular disease of the heart and brain. Novel approaches for streamlined imaging of the cardiac and central nervous systems including the use of intravascular contrast agents such as ferumoxytol are presented, and unmet research gaps in diagnostics are summarized.

Females have higher myocardial perfusion, blood volume and extracellular volume compared to males - an adenosine stress cardiovascular magnetic resonance study

Knowledge on sex differences in myocardial perfusion, blood volume (MBV), and extracellular volume (ECV) in healthy individuals is scarce and conflicting. Therefore, this was investigated quantitatively by cardiovascular magnetic resonance (CMR). Healthy volunteers (n = 41, 51% female) underwent CMR at 1.5 T. Quantitative MBV [%] and perfusion [ml/min/g] maps were acquired during adenosine stress and at rest following an intravenous contrast bolus (0.05 mmol/kg, gadobutrol). Native T1 maps were acquired before and during adenosine stress, and after contrast (0.2 mmol/kg) at rest and during adenosine stress, rendering rest and stress ECV maps. Compared to males, females had higher perfusion, ECV, and MBV at stress, and perfusion and ECV at rest (p < 0.01 for all). Multivariate linear regression revealed that sex and MBV were associated with perfusion (sex beta −0.31, p = 0.03; MBV beta −0.37, p = 0.01, model R² = 0.29, p < 0.01) while sex and hematocrit were associated with ECV (sex beta −0.33, p = 0.03; hematocrit beta −0.48, p < 0.01, model R² = 0.54, p < 0.001). Myocardial perfusion, MBV, and ECV are higher in female healthy volunteers compared to males. Sex is an independent contributor to perfusion and ECV, beyond other physiological factors that differ between the sexes. These findings provide mechanistic insight into sex differences in myocardial physiology.