In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography

The use of Micro-Computed Tomography (MicroCT) for in vivo studies of small animals as models of human disease has risen tremendously due to the fact that MicroCT provides quantitative high-resolution three-dimensional (3D) anatomical data non-destructively and longitudinally. Most importantly, with the development of a novel preclinical iodinated contrast agent called eXIA160, functional and metabolic assessment of the heart became possible. However, prior to the advent of commercial MicroCT scanners equipped with X-ray flat-panel detector technology and easy-to-use cardio-respiratory gating, preclinical studies of cardiovascular disease (CVD) in small animals required a MicroCT technologist with advanced skills, and thus were impractical for widespread implementation. The goal of this work is to provide a practical guide to the use of the high-speed Quantum FX MicroCT system for comprehensive determination of myocardial global and regional function along with assessment of myocardial perfusion, metabolism and viability in healthy mice and in a cardiac ischemia mouse model induced by permanent occlusion of the left anterior descending coronary artery (LAD)

Quantitative Assessment of Intra- and Inter-Modality Deformable Image Registration of the Heart, Left Ventricle, and Thoracic Aorta on Longitudinal 4D-CT and MR Images

Purpose Magnetic resonance imaging (MRI)-based investigations into radiotherapy (RT)-induced cardiotoxicity require reliable registrations of magnetic resonance (MR) imaging to planning computed tomography (CT) for correlation to regional dose. In this study, the accuracy of intra- and inter-modality deformable image registration (DIR) of longitudinal four-dimensional CT (4D-CT) and MR images were evaluated for heart, left ventricle (LV), and thoracic aorta (TA). Methods and materials Non-cardiac-gated 4D-CT and T1 volumetric interpolated breath-hold examination (T1-VIBE) MRI datasets from five lung cancer patients were obtained at two breathing phases (inspiration/expiration) and two time points (before treatment and 5 weeks after initiating RT). Heart, LV, and TA were manually contoured. Each organ underwent three intramodal DIRs ((A) CT modality over time, (B) MR modality over time, and (C) MR contrast effect at the same time) and two intermodal DIRs ((D) CT/MR multimodality at same time and (E) CT/MR multimodality over time). Hausdorff distance (HD), mean distance to agreement (MDA), and Dice were evaluated and assessed for compliance with American Association of Physicists in Medicine (AAPM) Task Group (TG)-132 recommendations. Results Mean values of HD, MDA, and Dice under all registration scenarios for each region of interest ranged between 8.7 and 16.8 mm, 1.0 and 2.6 mm, and 0.85 and 0.95, respectively, and were within the TG-132 recommended range (MDA \u3c 3 mm, Dice \u3e 0.8). Intramodal DIR showed slightly better results compared to intermodal DIR. Heart and TA demonstrated higher registration accuracy compared to LV for all scenarios except for HD and Dice values in Group A. Significant differences for each metric and tissue of interest were noted between Groups B and D and between Groups B and E. MDA and Dice significantly differed between LV and heart in all registrations except for MDA in Group E. Conclusions DIR of the heart, LV, and TA between non-cardiac-gated longitudinal 4D-CT and MRI across two modalities, breathing phases, and pre/post-contrast is acceptably accurate per AAPM TG-132 guidelines. This study paves the way for future evaluation of RT-induced cardiotoxicity and its related factors using multimodality DIR

Characterisation and correction of respiratory-motion artefacts in cardiac PET-CT

Respiratory motion during cardiac Positron Emission Tomography (PET) Computed Tomography (CT) imaging results in blurring of the PET data and can induce mismatches between the PET and CT datasets, leading to attenuation-correction artefacts. The aim of this project was to develop a method of motion-correction to overcome both of these problems. The approach implemented was to transform a single CT to match the frames of a gated PET study, to facilitate respiratory-matched attenuation-correction, without the need for a gated CT. This is benecial for lowering the radiation dose to the patient and in reducing PETCT mismatches, which can arise even in gated studies. The heart and diaphragm were identied through phantom studies as the structures responsible for generating attenuation-correction artefacts in the heart and their motions therefore needed to be considered in transforming the CT. Estimating heart motion was straight-forward, due to its high contrast in PET, however the poor diaphragm contrast meant that additional information was required to track its position. Therefore a diaphragm shape model was constructed using segmented diaphragm surfaces, enabling complete diaphragm surfaces to be produced from incomplete and noisy initial estimates. These complete surfaces, in combination with the estimated heart motions were used to transform the CT. The PET frames were then attenuation-corrected with the transformed CT, reconstructed, aligned and summed, to produce motion-free images. It was found that motion-blurring was reduced through alignment, although benets were marginal in the presence of small respiratory motions. Quantitative accuracy was improved from use of the transformed CT for attenuation-correction (compared with no CT transformation), which was attributed to both the heart and the diaphragm transformations. In comparison to a gated CT, a substantial dose saving and a reduced dependence on gating techniques were achieved, indicating the potential value of the technique in routine clinical procedures

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60–68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Mouse cardiac MRI

De laatste decennia is de interesse in het afbeelden van het hart in de levende muis enorm toegenomen. Deze interesse wordt voor een groot gedeelte gedragen door nieuwe ontwikkelingen in de gentechnologie en moleculaire biologie. Doel van het promotieonderzoek was een toolbox te ontwikkelen van verschillende MRI- (Magnetische Resonantie Imaging) en analysetechnieken, als ondersteuning van het huidige en toekomstige onderzoek op het gebied van muizenharten. Het is met een MRI scanner moeilijk het muizenhart goed af te beelden vanwege de geringe afmetingen van het muizenhart en de hoge frequentie waarmee het muizenhart klopt. Om een goede afbeelding van het muizenhart te krijgen is het noodzakelijk gebruik te maken van een opstelling welke zorgt dat de verschillende fysiologische parameters van de muizen gemeten en gecontroleerd kunnen worden gedurende de acquisitie. Verder worden grote bewegingsfouten voorkomen door de acquisitie van het tomografische beeld te synchroniseren met de cyclische beweging van het hart. Deze synchronisatie wordt normaal gerealiseerd door het begin van de hartcyclus te bepalen aan de hand van een elektrocardiogram en te bepalen wanneer de muis geen ademhalingsbeweging uitvoert. In dit proefschrift wordt een ’draadloze’ techniek beschreven waarbij het meten van de fase van de hartcyclus en de ademhalingscyclus bepaald wordt vanuit het magnetische resonantiesignaal zelf. Het asynchroon uitvoeren van de acquisitie, en deze na afloop van de meting te synchroniseren met de hartslag, heeft als bijkomend voordeel dat de sterkte van het magnetische resonantiesignaal hetzelfde blijft gedurende de volledige acquisitie. Dit zorgt ervoor dat contrastverschillen in het tomografische beeld gelijk blijven en niet afhankelijk zijn van de conditie van de muis. De constante signaalsterkte gaat ten koste van een lagere signaal-ruis-verhouding. De verlaging van de signaal-ruis-verhouding heeft er toe geleid dat de volumes van het werkende muizenhart enigszins verschillend werden beoordeeld. MRI is niet alleen in staat om tomografische afbeeldingen te maken, maar kan ook fysiologische parameters te kwantificeren. Het grote nadeel van een tomografische afbeeldingtechniek is dat het alleen kwalitatieve informatie geeft, zoals de morfologie. Kwantitatieve informatie ,bijvoorbeeld een volumebepaling, kan alleen verkregen worden door het segmenteren van het muizenhart uit de verkregen tomografische afbeelding. Het seg menteren kost enorm veel tijd wanneer dit met de hand moet worden uitgevoerd. Bovendien kan handmatige segmentatie onnauwkeurig worden als het niet door gekwalificeerde personen wordt uitgevoerd. We laten in dit proefschrift zien dat een automatische segmentatie methode een even grote fout maakt als de fout die gemaakt wordt wanneer twee verschillende gekwalificeerde personen dezelfde segmentaties met de hand uitvoeren binnen een muizengroep. De synchronisatietechniek zoals hierboven beschreven, gebaseerd op het magnetische resonantiesignaal, werd ook toegepast om de volledige bewegingscyclus van de aorta af te beelden in twee verschillende muisgenotypes (Smtn-B+/+ and Smtn-B-/-). Het genotype Smtn-B-/- heeft een afwijkende contractiekracht in de arteri¨en en een hogere gemiddelde arteri¨ele bloeddruk. De aortadiametertoename in het genotype Smtn-B-/- was tweemaal groter gedurende de hartcyclus in vergelijking met muizen van het genotype Smtn-B+/+. Verder hadden de muizenharten van de Smtn-B-/- genotype een grotere linkerventrikelmassa en een hogere ejectiefractie. Deze studie, waarin twee verschillende muizengroepen met elkaar werden vergeleken, laat zien dat de MRI-techniek zeer kleine verschillen in fysiologische parameters van het muizenhart kan detecteren. MRI wordt naast phenotypering ook gebruikt om muizenharten met een infarct te karakteriseren. Verschillende publicaties vergelijken fysiologische parameters van muizenharten met infarcten gemeten tussen MRI enerzijds en anderzijds: computertomografie-, echocardiografie- of druk-volumemeting met een katheter. Nieuwe ontwikkelingen op het gebied van nucleaire scantechnieken ,zoals bijvoorbeeld positron emissie tomografie (PET), maken het mogelijk dat deze nucleaire technieken ook muizenharten kunnen karakteriseren. Het grote voordeel van deze nucleaire scantechnieken is hun hoge gevoeligheid voor radioactieve contrastmiddelen, die ervoor zorgt dat men bijna geen toxicologische reacties hoeft te verwachten. In een gecombineerd experiment werden fysiologische parameters vergeleken tussen MRI- en PET-metingen. Ook werd een vergelijking gemaakt tussen de infarctgroottes zoals bepaald met behulp van een MRI-contrastmiddel en een PETcontrastmiddel. Er werd een goede correlatie gevonden tussen beide imagingtechnieken m.b.t. de fysiologische parameters: einddiastole volume, eindsystole volume en ejectiefractie. Aanzienlijke verschillen werden gemeten in de infarctgrootte bepaald uit de MRI- en PET-beelden. Verder werden er hoge correlaties gevonden in de MRI-data tussen drie verschillende infarctgroottebepalingen en de ejectiefracties. Het afbeelden van ziekteprocessen op het cellulaire en moleculaire niveau met MRI is mogelijk door gebruik te maken van krachtige en specifieke contrastmiddelen. MRI heeft een relatief lage detectiegevoeligheid voor contrastmiddelen. Om de gevoeligheid te vergroten, werd er een studie uitgevoerd naar een snelle MRI-sequentie, bekend onder de naam ’Rephased-FFE’. Deze sequentie liet een 6-maal hogere detectiegevoeligheid zien voor het contrastmiddel Gd-DTPA in een fantoomexperiment met een conventionele humane 1.5 Tesla MRI-scanner

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD