Imperial College London

Dr Andrew D. Scott

Faculty of MedicineNational Heart & Lung Institute

Honorary Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 7352 8121 ext 2937a.scott07

 
 
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Location

 

Cardiovascular MR UnitRoyal Brompton Campus

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Summary

 

Publications

Publication Type
Year
to

74 results found

Huang J, Ferreira P, Wang L, Wu Y, Aviles-Rivero A, Schonlieb C-B, Scott A, Khalique Z, Dwornik D, Rajakulasingam R, De Silva R, Pennell D, Nielles-Vallespin S, Yang Get al., 2024, Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study, Scientific Reports, ISSN: 2045-2322

Journal article

Garza-Villarreal E, Moy L, Mao H, Hussain T, Lupo J, Fleischer C, Scott Aet al., 2024, Ethical considerations of preclinical models in imaging research, Magnetic Resonance in Medicine, Vol: 91, Pages: 858-859, ISSN: 0740-3194

Journal article

Huo Z, Wen K, Luo Y, Neji R, Kunze KP, Ferreira PF, Pennell DJ, Scott AD, Nielles-Vallespin Set al., 2024, Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance, Magnetic Resonance in Medicine, ISSN: 0740-3194

PURPOSE: The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. METHODS: Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. RESULTS: Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600   smm - 2 $$ {\mathrm{smm}}^{-2} $$ . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes. CONCLUSION: Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.

Journal article

Roehl M, Conway M, Ghonim S, Ferreira P, Nielles-Vallespin S, Babu-Narayan S, Pennell D, Gatehouse P, Scott Aet al., 2024, STEAM-SASHA: A novel approach for blood and fat suppressed native T1 measurement in the right ventricular myocardium, Magnetic Resonance Materials in Physics, Biology and Medicine, ISSN: 0968-5243

Objective:The excellent blood and fat suppression of stimulated echo acquisition mode (STEAM) can be combined with saturation recovery single-shot acquisition (SASHA) in a novel STEAM-SASHA sequence for right ventricular (RV) native T1 mapping.Materials and methods:STEAM-SASHA splits magnetization preparation over two cardiac cycles, nulling blood signal and allowing fat signal to decay. Breath-hold T1 mapping was performed in a T1 phantom and twice in 10 volunteers using STEAM-SASHA and a modified Look-Locker sequence at peak systole at 3T. T1 was measured in 3 RV regions, the septum and left ventricle (LV).Results:In phantoms, MOLLI under-estimated while STEAM-SASHA over-estimated T1, on average by 3.0% and 7.0% respectively, although at typical 3T myocardial T1 (T1 > 1200 ms) STEAM-SASHA was more accurate. In volunteers, T1 was higher using STEAM-SASHA than MOLLI in the LV and septum (p = 0.03, p = 0.006, respectively), but lower in RV regions (p > 0.05). Inter-study, inter-observer and intra-observer coefficients of variation in all regions were < 15%. Blood suppression was excellent with STEAM-SASHA and noise floor effects were minimal.Discussion:STEAM-SASHA provides accurate and reproducible T1 in the RV with excellent blood and fat suppression. STEAM-SASHA has potential to provide new insights into pathological changes in the RV in future studies.

Journal article

Tänzer M, Ferreira P, Scott A, Khalique Z, Dwornik M, Rajakulasingam R, de Silva R, Pennell D, Yang G, Rueckert D, Nielles-Vallespin Set al., 2024, Correction to: Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction, Medical Image Understanding and Analysis, Publisher: Springer International Publishing, Pages: C1-C1, ISBN: 9783031120527

Book chapter

Mao H, Garza-Villarreal EA, Moy L, Hussain T, Scott AD, Lupo JM, Zhou XJ, Fleischer CCet al., 2023, Ethical considerations for MRI research in human subjects in the era of precision medicine, Journal of Magnetic Resonance Imaging, ISSN: 1053-1807

Journal article

Zheng Y, Chan WX, Nielles-Vallespin S, Scott AD, Ferreira PF, Leo HL, Yap CHet al., 2023, Effects of myocardial sheetlet sliding on left ventricular function, Biomechanics and Modeling in Mechanobiology, Vol: 22, Pages: 1313-1332, ISSN: 1617-7940

Left ventricle myocardium has a complex micro-architecture, which was revealed to consist of myocyte bundles arranged in a series of laminar sheetlets. Recent imaging studies demonstrated that these sheetlets re-orientated and likely slided over each other during the deformations between systole and diastole, and that sheetlet dynamics were altered during cardiomyopathy. However, the biomechanical effect of sheetlet sliding is not well-understood, which is the focus here. We conducted finite element simulations of the left ventricle (LV) coupled with a windkessel lumped parameter model to study sheetlet sliding, based on cardiac MRI of a healthy human subject, and modifications to account for hypertrophic and dilated geometric changes during cardiomyopathy remodeling. We modeled sheetlet sliding as a reduced shear stiffness in the sheet-normal direction and observed that (1) the diastolic sheetlet orientations must depart from alignment with the LV wall plane in order for sheetlet sliding to have an effect on cardiac function, that (2) sheetlet sliding modestly aided cardiac function of the healthy and dilated hearts, in terms of ejection fraction, stroke volume, and systolic pressure generation, but its effects were amplified during hypertrophic cardiomyopathy and diminished during dilated cardiomyopathy due to both sheetlet angle configuration and geometry, and that (3) where sheetlet sliding aided cardiac function, it increased tissue stresses, particularly in the myofibre direction. We speculate that sheetlet sliding is a tissue architectural adaptation to allow easier deformations of the LV walls so that LV wall stiffness will not hinder function, and to provide a balance between function and tissue stresses. A limitation here is that sheetlet sliding is modeled as a simple reduction in shear stiffness, without consideration of micro-scale sheetlet mechanics and dynamics.

Journal article

Alemany I, Rose JN, Ferreira PF, Pennell DJ, Nielles-Vallespin S, Scott AD, Doorly DJet al., 2023, Realistic numerical simulations of diffusion tensor cardiovascular magnetic resonance: the effects of perfusion and membrane permeability, Magnetic Resonance in Medicine, Vol: 90, Pages: 1641-1656, ISSN: 0740-3194

PurposeTo study the sensitivity of diffusion tensor cardiovascular magnetic resonance (DT-CMR) to microvascular perfusion and changes in cell permeability.MethodsMonte Carlo (MC) random walk simulations in the myocardium have been performed to simulate self-diffusion of water molecules in histology-based media with varying extracellular volume fraction (ECV) and permeable membranes. The effect of microvascular perfusion on simulations of the DT-CMR signal has been incorporated by adding the contribution of particles traveling through an anisotropic capillary network to the diffusion signal. The simulations have been performed considering three pulse sequences with clinical gradient strengths: monopolar stimulated echo acquisition mode (STEAM), monopolar pulsed-gradient spin echo (PGSE), and second-order motion-compensated spin echo (MCSE).ResultsReducing ECV intensifies the diffusion restriction and incorporating membrane permeability reduces the anisotropy of the diffusion tensor. Widening the intercapillary velocity distribution results in increased measured diffusion along the cardiomyocytes long axis when the capillary networks are anisotropic. Perfusion amplifies the mean diffusivity for STEAM while the opposite is observed for short diffusion encoding time sequences (PGSE and MCSE).ConclusionThe effect of perfusion on the measured diffusion tensor is reduced using an increased reference b-value. Our results pave the way for characterization of the response of DT-CMR to microstructural changes underlying cardiac pathology and highlight the higher sensitivity of STEAM to permeability and microvascular circulation due to its longer diffusion encoding time.

Journal article

Wang Y, Sun C, Ghadimi S, Auger DC, Croisille P, Viallon M, Mangion K, Berry C, Haggerty CM, Jing L, Fornwalt BK, Cao JJ, Cheng J, Scott AD, Ferreira PF, Oshinski JN, Ennis DB, Bilchick KC, Epstein FHet al., 2023, StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE, RADIOLOGY-CARDIOTHORACIC IMAGING, Vol: 5, ISSN: 2638-6135

Journal article

Moulin K, Stoeck CT, Axel L, Broncano J, Croisille P, Dall'Armellina E, Ennis DB, Ferreira PF, Gotschy A, Miro S, Schneider JE, Scott AD, Sosnovik DE, Teh I, Tous C, Tunnicliffe EM, Viallon M, Nguyen Cet al., 2023, In Vivo Cardiac Diffusion Imaging Without Motion-Compensation Leads to Unreasonably High Diffusivity, JOURNAL OF MAGNETIC RESONANCE IMAGING, ISSN: 1053-1807

Journal article

Barbaroux H, Kunze KP, Neji R, Nazir MS, Pennell DJ, Nielles-Vallespin S, Scott AD, Young AAet al., 2023, Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional neural networks, Journal of Cardiovascular Magnetic Resonance, Vol: 25, Pages: 1-17, ISSN: 1097-6647

BACKGROUND: Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images. METHODS: 2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods. RESULTS: Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myo

Journal article

Alemany I, Ferreira PF, Nielles-Vallespin S, Scott AD, Doorly DJet al., 2023, The Effect of Temporal Variations in Myocardial Perfusion on Diffusion Tensor Measurements, Pages: 54-63, ISBN: 9783031353017

The aim of this study is to investigate the impact of velocity fluctuations on the perfusion signal and tensor parameters in diffusion tensor cardiovascular magnetic resonance (DT-CMR) using numerical simulations. A sinusoidal velocity function with increasing amplitude and frequency and a physiological velocity function have been considered. Both velocity functions have been analyzed using two mean inter-capillary velocity distributions with varying levels of dispersion. The results of the perfusion simulations, along with previous diffusion results, have been utilized to analyse the impact of perfusion on the diffusion tensor. The findings indicated that MCSE effectively compensated the rapid velocity changes considered in the study, while PGSE was sensitive to temporal changes in velocity. STEAM was found to be more sensitive to variations in the mean-intercapillary dispersion rather than to temporal velocity fluctuations. These simulation results provide insights regarding the potential of dispersed perfusion velocity fluctuations to affect the DT-CMR signal.

Book chapter

Barbaroux H, Loecher M, Kunze KP, Neji R, Ennis DB, Nielles-Vallespin S, Scott AD, Young AAet al., 2023, Generating Short-Axis DENSE Images from 4D XCAT Phantoms: A Proof-of-Concept Study, Pages: 412-421, ISSN: 0302-9743

Displacement ENcoding with Stimulated Echoes (DENSE) is a CMR modality that can encode myocardial tissue displacement at a pixel level, enabling the characterization of cardiac disease at early stages. However, we do not currently have a way of evaluating the accuracy of derived results, since the ground truth is unknown. In this study, we developed a proof-of-concept pipeline to generate realistic DENSE images with a known ground truth. We leverage the XCAT tool to create body anatomies, along with associated myocardial tissue displacements, and generate DENSE images with a Bloch simulation based on the time-resolved positions. We generated 6 samples: an apical, a mid, and a basal short-axis slice for both male and female anatomy. We then extracted radial and circumferential strain components using DENSEanalysis, and compared them to the ground-truth strain obtained from the XCAT displacements. While the reproducibility of the strain calculations was similar to the inter-observer variability from previous studies, and the bias in circumferential strain was small (0.03 ± 0.02), the current methods for strain extraction resulted in a bias in radial strain of 0.19 ± 0.19. There is a need to develop better regularization strategies for DENSE analysis, for instance using Deep Learning, and this study provides initial groundwork for obtaining ground-truth strain to evaluate these methods.

Conference paper

Wang L, Huang J, Xing X, Wu Y, Rajakulasingam R, Scott AD, Ferreira PF, Silva RD, Nielles-Vallespin S, Yang Get al., 2023, Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation

This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diffusion tensor imaging (DTI) images by translating them into the late gadolinium enhancement (LGE) domain, which offers a larger amount of data with high-resolution and distinct highlighting of myocardium infarction (MI) areas. Subsequently, the segmentation task is performed on the LGE style image. An end-to-end super-resolution segmentation model is introduced to generate high-resolution mask from low-resolution LGE style DTI image. Further, to enhance the performance of the model, a multi-task self-supervised learning strategy is employed to pre-train the super-resolution segmentation model, allowing it to acquire more representative knowledge and improve its segmentation performance after fine-tuning. https://github.com/wlc2424762917/Med_Img

Conference paper

Conway M, Vallespin SN, Ferreira P, Scott A, Roehl M, McCarthy K, Smith GC, Ho SY, Li W, Pennell DJ, Babu-Narayan Set al., 2023, IN-VIVO DIFFUSION TENSOR CARDIOVASCULAR MAGNETIC RESONANCE DETECTS THE ARRANGEMENT AND DYNAMIC NATURE OF RIGHT VENTRICULAR MICROSTRUCTURE IN HEALTH AND DISEASE, 17th Annual Congress of the British-Society-of-Cardiovascular-Magnetic-Resonance (BSCMR), Publisher: BMJ PUBLISHING GROUP, Pages: A2-A3, ISSN: 1355-6037

Conference paper

Ferreira PF, Banerjee A, Scott AD, Khalique Z, Yang G, Rajakulasingam R, Dwornik M, De Silva R, Pennell DJ, Firmin DN, Nielles-Vallespin Set al., 2022, Accelerating Cardiac Diffusion Tensor Imaging With a U-Net Based Model: Toward Single Breath-Hold, JOURNAL OF MAGNETIC RESONANCE IMAGING, Vol: 56, Pages: 1691-1704, ISSN: 1053-1807

Journal article

Alemany Juvanteny I, Rose J, Garnier-Brun J, Scott A, Doorly Det al., 2022, Random walk diffusion simulations in semi-permeable layered media with varying diffusivity, Scientific Reports, Vol: 12, ISSN: 2045-2322

In this paper we present random walk based solutions to diffusion in semi-permeable layered media with varying diffusivity.We propose a novel transit model for solving the interaction of random walkers with a membrane. This hybrid model isbased on treating the membrane permeability and the step change in diffusion coefficient as two interactions separated byan infinitesimally small layer. By conducting an extensive analytical flux analysis, the performance of our hybrid model iscompared with a commonly used membrane transit model (reference model). Numerical simulations demonstrate the limitationsof the reference model in dealing with step changes in diffusivity and show the capability of the hybrid model to overcomethis limitation and to offer substantial gains in computational efficiency. The suitability of both random walk transit modelsfor the application to simulations of diffusion tensor cardiovascular magnetic resonance (DT-CMR) imaging is assessed in ahistology-based domain relevant to DT-CMR. In order to demonstrate the usefulness of the new hybrid model for other possibleapplications, we also consider a larger range of permeabilities beyond those commonly found in biological tissues.

Journal article

Teh I, Romero W, Boyle J, Coll-Font J, Dall'Armellina E, Ennis DB, Ferreira PF, Kalra P, Kolipaka A, Kozerke S, Lohr D, Mongeon F-P, Moulin K, Nguyen C, Nielles-Vallespin S, Raterman B, Schreiber LM, Scott AD, Sosnovik DE, Stoeck CT, Tous C, Tunnicliffe EM, Weng AM, Croisille P, Viallon M, Schneider JEet al., 2022, Validation of cardiac diffusion tensor imaging sequences: A multi-centre test-retest phantom study, NMR in Biomedicine, Vol: 35, Pages: 1-18, ISSN: 0952-3480

INTRODUCTION: Cardiac diffusion tensor imaging (DTI) is an emerging technique for the in vivo characterisation of myocardial microstructure, and there is a growing need for its validation and standardisation. We sought to establish accuracy, precision, repeatability and reproducibility of state-of-the-art pulse sequences for cardiac DTI between ten centres internationally. METHODS: Phantoms comprising 0-20% polyvinylpyrrolidone (PVP) were scanned with DTI using a product pulsed gradient spin echo (PGSE; N=10 sites) sequence, and a custom motion-compensated spin echo (SE; N=5) or stimulated echo (STEAM; N=5) sequence suitable for cardiac DTI in vivo. A second identical scan was performed 1-9 days post, and the data analysed centrally. RESULTS: The average mean diffusivities (MD) in 0% PVP were (1.124, 1.130, 1.113) × 10-3 mm2 /s for PGSE, SE and STEAM respectively, and accurate to within 1.5% of reference data from literature. The coefficients of variation in MD across sites were 2.6%, 3.1%, 2.1% for PGSE, SE and STEAM, and were similar to previous studies using only PGSE. Reproducibility in MD was excellent, with mean differences in PGSE, SE and STEAM of (0.3 ± 2.3, 0.24 ± 0.95, 0.52 ± 0.58) × 10-5 mm2 /s (mean ± 1.96SD). CONCLUSION: We show that custom sequences for cardiac DTI provide accurate, precise, repeatable and reproducible measurements. Further work in anisotropic and/or deforming phantoms is warranted.

Journal article

Auger DA, Ghadimi S, Cai X, Reagan CE, Sun C, Abdi M, Cao JJ, Cheng JY, Ngai N, Scott AD, Ferreira PF, Oshinski JN, Emamifar N, Ennis DB, Loecher M, Liu Z-Q, Croisille P, Viallon M, Bilchick KC, Epstein FHet al., 2022, Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease, Journal of Cardiovascular Magnetic Resonance, Vol: 24, Pages: 23-23, ISSN: 1097-6647

BACKGROUND: While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (Ecc), longitudinal (Ell) and radial (Err) strain, torsion, and segmental Ecc at multiple centers. METHODS: Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global Ecc, Ell and Err, torsion, and segmental Ecc were quantified using Bland-Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10% < CV ≤ 20%, fair for 20% < CV ≤ 40%, and poor for CV > 40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6 < ICC ≤ 0.74, fair for ICC 0.4 < ICC ≤ 0.59

Journal article

Dwornik M, Khalique Z, Rajakulasingam R, Scott A, Azzu A, Ferreira PF, Nielles-Vallespin S, Pennell DJet al., 2022, Cardiovascular Magnetic Resonance in Cardiomyopathy, International Journal of Cardiodiabetes

Journal article

Scott A, Jackson T, Khalique Z, Gorodezky M, Pardoe B, Begum L, Bruno VD, Chowdhury R, Ferreira P, Nielles-Vallespin S, Roehl M, McCarthy K, Sarathchandra P, Rose J, Doorly D, Pennell D, Ascione R, De Silva PER, Firmin Det al., 2022, Development of a CMR compatible large animal isolated heart model for direct comparison of beating and arrested hearts, NMR in Biomedicine, Vol: 35, ISSN: 0952-3480

BackgroundCardiac motion results in image artefacts and quantification errors in many cardiovascular magnetic resonance (CMR) techniques, including microstructural assessment using diffusion tensor cardiovascular magnetic resonance (DT-CMR). Here we develop a CMR compatible isolated perfused porcine heart model that allows comparison of data obtained in beating and arrested states.Methods10 porcine hearts (8/10 for protocol optimisation) were harvested using a donor heart retrieval protocol and transported to the remote CMR facility. Langendorff perfusion in a 3D printed chamber and perfusion circuit re-established contraction. Hearts were imaged using cine, parametric mapping and STEAM DT-CMR at cardiac phases with the minimum and maximum wall thickness. High potassium and lithium perfusates were then used to arrest the heart in a slack and contracted state respectively. Imaging was repeated in both arrested states. After imaging, tissue was removed for subsequent histology in a location matched to the DT-CMR data using fiducial markers.ResultsRegular sustained contraction was successfully established in 6/10 hearts, including the final 5 hearts. Imaging was performed in 4 hearts and one underwent the full protocol including co-localised histology. Image quality was good and there was good agreement between DT-CMR data in equivalent beating and arrested states. Despite the use of autologous blood and dextran within the perfusate, T2, DT-CMR measures and an increase in mass was consistent with development of myocardial edema resulting in failure to achieve a true diastolic-like state. A contiguous stack of 313 5μm histological sections at and a 100μm thick section showing cell morphology on 3D fluorescent confocal microscopy co-localised to DT-CMR data were obtained.ConclusionsA CMR compatible isolated perfused beating heart setup for large animal hearts allows direct comparisons of beating and arrested heart data with subsequent co-localised histology without

Journal article

Tanzer M, Ferreira P, Scott A, Khalique Z, Dwornik M, Pennell D, Yang G, Rueckert D, Nielles-Vallespin Set al., 2022, Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction, MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, MIUA 2022, Vol: 13413, Pages: 101-115, ISSN: 0302-9743

Journal article

Ghadimi S, Auger DA, Feng X, Sun C, Meyer CH, Bilchick KC, Cao JJ, Scott AD, Oshinski JN, Ennis DB, Epstein FHet al., 2021, Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping, Journal of Cardiovascular Magnetic Resonance, Vol: 23, ISSN: 1097-6647

BackgroundCardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis.MethodsConvolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain.ResultsLV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p <&

Journal article

Ferreira PF, Martin RR, Scott AD, Khalique Z, Yang G, Nielles-Vallespin S, Pennell DJ, Firmin DNet al., 2020, Automating in vivo cardiac diffusion tensor postprocessing with deep learning-based segmentation, Magnetic Resonance in Medicine, Vol: 84, Pages: 2801-2814, ISSN: 0740-3194

PurposeIn this work we develop and validate a fully automated postprocessing framework for in vivo diffusion tensor cardiac magnetic resonance (DT‐CMR) data powered by deep learning.MethodsA U‐Net based convolutional neural network was developed and trained to segment the heart in short‐axis DT‐CMR images. This was used as the basis to automate and enhance several stages of the DT‐CMR tensor calculation workflow, including image registration and removal of data corrupted with artifacts, and to segment the left ventricle. Previously collected and analyzed scans (348 healthy scans and 144 cardiomyopathy patient scans) were used to train and validate the U‐Net. All data were acquired at 3 T with a STEAM‐EPI sequence. The DT‐CMR postprocessing and U‐Net training/testing were performed with MATLAB and Python TensorFlow, respectively.ResultsThe U‐Net achieved a median Dice coefficient of 0.93 [0.92, 0.94] for the segmentation of the left‐ventricular myocardial region. The image registration of diffusion images improved with the U‐Net segmentation (P < .0001), and the identification of corrupted images achieved an F1 score of 0.70 when compared with an experienced user. Finally, the resulting tensor measures showed good agreement between an experienced user and the fully automated method.ConclusionThe trained U‐Net successfully automated the DT‐CMR postprocessing, supporting real‐time results and reducing human workload. The automatic segmentation of the heart improved image registration, resulting in improvements of the calculated DT parameters.

Journal article

Nielles-Vallespin S, Ferreira PF, Scott A, Rajakulasingam R, Sehmi J, Gorodezky M, Kellman P, Xue H, Pennell DJ, Firmin DN, Arai AE, De Silva Ret al., 2020, Diffusion tensor cardiovascular magnetic resonance predicts adverse remodelling after myocardial infarction, European-Society-of-Cardiology (ESC) Congress, Publisher: OXFORD UNIV PRESS, Pages: 216-216, ISSN: 0195-668X

Conference paper

Nielles-Vallespin S, Scott A, Ferreira P, Khalique Z, Pennell D, Firmin Det al., 2020, Cardiac Diffusion: Technique and Practical Applications, JOURNAL OF MAGNETIC RESONANCE IMAGING, Vol: 52, Pages: 348-368, ISSN: 1053-1807

Journal article

Tayal U, Wage R, Newsome S, Manivarmane R, Izgi C, Muthumala A, Dungu JN, Assomull R, Hatipoglu S, Halliday BP, Lota AS, Ware JS, Gregson J, Frenneaux M, Cook SA, Pennell DJ, Scott AD, Cleland JGF, Prasad SKet al., 2020, Predictors of left ventricular remodelling in patients with dilated cardiomyopathy - a cardiovascular magnetic resonance study, European Journal of Heart Failure, Vol: 22, Pages: 1160-1170, ISSN: 1388-9842

AimsThere is an important need for better biomarkers to predict left ventricular (LV) remodelling in dilated cardiomyopathy (DCM). We undertook a comprehensive assessment of cardiac structure and myocardial composition to determine predictors of remodelling.Methods and resultsProspective study of patients with recent‐onset DCM with cardiovascular magnetic resonance (CMR) assessment of ventricular structure and function, extracellular volume (T1 mapping), myocardial strain, myocardial scar (late gadolinium enhancement) and contractile reserve (dobutamine stress). Regression analyses were used to evaluate predictors of change in LV ejection fraction (LVEF) over 12 months. We evaluated 56 participants (34 DCM patients, median LVEF 43%; 22 controls). Absolute LV contractile reserve predicted change in LVEF (1% increase associated with 0.4% increase in LVEF at 12 months, P = 0.02). Baseline myocardial strain (P = 0.39 global longitudinal strain), interstitial myocardial fibrosis (P = 0.41), replacement myocardial fibrosis (P = 0.25), and right ventricular contractile reserve (P = 0.17) were not associated with LV reverse remodelling. There was a poor correlation between contractile reserve and either LV extracellular volume fraction (r = −0.22, P = 0.23) or baseline LVEF (r = 0.07, P = 0.62). Men were more likely to experience adverse LV remodelling (P = 0.01) but age (P = 0.88) and disease‐modifying heart failure medication (beta‐blocker, P = 0.28; angiotensin‐converting enzyme inhibitor, P = 0.92) did not predict follow‐up LVEF.ConclusionsSubstantial recovery of LV function occurs within 12 months in most patients with recent‐onset DCM. Women had the greatest improvement in LVEF. A low LV contractile reserve measured by dobutamine stress CMR appears to identify patients whose LVEF is less likely to recover.

Journal article

Khalique Z, Ferreira PF, Scott AD, Nielles-Vallespin S, Martinez-Naharro A, Fontana M, Hawkins P, Firmin DN, Pennell DJet al., 2020, Diffusion tensor cardiovascular magnetic resonance in cardiac amyloidosis, Circulation: Cardiovascular Imaging, Vol: 13, ISSN: 1941-9651

Background Cardiac amyloidosis (CA) is a disease of interstitial myocardial infiltration, usually by light chains or transthyretin. We used diffusion tensor cardiovascular magnetic resonance (DT-CMR) to noninvasively assess the effects of amyloid infiltration on the cardiac microstructure. Methods DT-CMR was performed at diastole and systole in 20 CA, 11 hypertrophic cardiomyopathy, and 10 control subjects with calculation of mean diffusivity, fractional anisotropy, and sheetlet orientation (secondary eigenvector angle). Results Mean diffusivity was elevated and fractional anisotropy reduced in CA compared with both controls and hypertrophic cardiomyopathy (P<0.001). In CA, mean diffusivity was correlated with extracellular volume (r=0.68, P=0.004), and fractional anisotropy was inversely correlated with circumferential strain (r=-0.65, P=0.02). In CA, diastolic secondary eigenvector angle was elevated, and secondary eigenvector angle mobility was reduced compared with controls (both P<0.001). Diastolic secondary eigenvector angle was correlated with amyloid burden measured by extracellular volume in transthyretin, but not light chain amyloidosis. Conclusions DT-CMR can characterize the microstructural effects of amyloid infiltration and is a contrast-free method to identify the location and extent of the expanded disorganized myocardium. The diffusion biomarkers mean diffusivity and fractional anisotropy effectively discriminate CA from hypertrophic cardiomyopathy. DT-CMR demonstrated that failure of sheetlet relaxation in diastole correlated with extracellular volume in transthyretin, but not light chain amyloidosis. This indicates that different mechanisms may be responsible for impaired contractility in CA, with an amyloid burden effect in transthyretin, but an idiosyncratic effect in light chain amyloidosis. Consequently, DT-CMR offers a contrast-free tool to identify novel pathophysiology, improve diagnostics, and monitor disease through noninvasive micr

Journal article

Khalique Z, Ferreira P, Scott A, Nielles-Vallespin S, Firmin D, Pennell Det al., 2020, Diffusion tensor cardiovascular magnetic resonance: a clinical perspective, JACC: Cardiovascular Imaging, Vol: 13, Pages: 1235-1255, ISSN: 1936-878X

Imaging the heart is central to cardiac phenotyping but in clinical practice this has been restricted to macroscopic interrogation. Diffusion tensor cardiovascular magnetic resonance (DT-CMR) is a novel, non-invasive technique which is beginning to unlock details of this microstructure in humans in-vivo. DT-CMR demonstrates the helical cardiomyocyte arrangement that drives rotation and torsion. Sheetlets (functional units of cardiomyocytes, separated by shear layers) have been shown to reorientate between diastole and systole, revealing how microstructural function facilitates cardiac thickening. Measures of tissue diffusion can also be made; fractional anisotropy (a measure of myocyte organisation) and mean diffusivity (a measure of myocyte packing). Abnormal myocyte orientation and sheetlet function has been demonstrated in congenital heart disease, cardiomyopathy and after myocardial infarction. It is too early to predict the clinical importance of DT-CMR, but such unique in-vivo information will likely prove valuable in early diagnosis and risk prediction of cardiac dysfunction and arrhythmias.

Journal article

Stoeck CT, Scott AD, Ferreira PF, Tunnicliffe EM, Teh I, Nielles-Vallespin S, Moulin K, Sosnovik DE, Viallon M, Croisille P, Kozerke S, Firmin DN, Ennis DB, Schneider JEet al., 2020, Motion-induced signal loss in In vivo cardiac diffusion-weighted imaging, Journal of Magnetic Resonance Imaging, Vol: 51, Pages: 319-320, ISSN: 1053-1807

Journal article

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