Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Improving Quantification in Lung PET/CT for the Evaluation of Disease Progression and Treatment Effectiveness

Positron Emission Tomography (PET) allows imaging of functional processes in vivo by measuring the distribution of an administered radiotracer. Whilst one of its main uses is directed towards lung cancer, there is an increased interest in diffuse lung diseases, for which the incidences rise every year, mainly due to environmental reasons and population ageing. However, PET acquisitions in the lung are particularly challenging due to several effects, including the inevitable cardiac and respiratory motion and the loss of spatial resolution due to low density, causing increased positron range. This thesis will focus on Idiopathic Pulmonary Fibrosis (IPF), a disease whose aetiology is poorly understood while patient survival is limited to a few years only. Contrary to lung tumours, this diffuse lung disease modifies the lung architecture more globally. The changes result in small structures with varying densities. Previous work has developed data analysis techniques addressing some of the challenges of imaging patients with IPF. However, robust reconstruction techniques are still necessary to obtain quantitative measures for such data, where it should be beneficial to exploit recent advances in PET scanner hardware such as Time of Flight (TOF) and respiratory motion monitoring. Firstly, positron range in the lung will be discussed, evaluating its effect in density-varying media, such as fibrotic lung. Secondly, the general effect of using incorrect attenuation data in lung PET reconstructions will be assessed. The study will compare TOF and non-TOF reconstructions and quantify the local and global artefacts created by data inconsistencies and respiratory motion. Then, motion compensation will be addressed by proposing a method which takes into account the changes of density and activity in the lungs during the respiration, via the estimation of the volume changes using the deformation fields. The method is evaluated on late time frame PET acquisitions using ¹⁸F-FDG where the radiotracer distribution has stabilised. It is then used as the basis for a method for motion compensation of the early time frames (starting with the administration of the radiotracer), leading to a technique that could be used for motion compensation of kinetic measures. Preliminary results are provided for kinetic parameters extracted from short dynamic data using ¹⁸F-FDG

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages

Small animal PET imaging using GATE Monte Carlo simulations : Implementation of physiological and metabolic information

Tese de doutoramento, (Engenharia Biomédica e Biofísica), Universidade de Lisboa, Faculdade de Ciências, 2010O rato/ratinho de laboratório é o modelo animal de escolha para o estudo dos processos fundamentais associados a determinadas patologias, como o cancro. Esta escolha deve-se a uma gama de factores que incluem uma grande homologia genética com o Homem. Assim sendo o rato/ratinho é amplamente utilizado em laboratórios por todo o Mundo para estudo dos processos celulares básicos associados á doença e à terapia. A comunidade laboratorial tem, nos últimos anos, desenvolvido um grande interesse pela imagiologia não-invasiva destes animais. De entre as diversas tecnologias de imagem aplicadas aos estudosin vivo de pequenos animais, a Tomografia por Emissão de Positrões (PET) permite obter informação sobre a distribuição espacial e temporal de moléculas marcadas com átomo emissor de positrões, de forma não invasiva. Os traçadores utilizados para obter esta “imagem molecular” são administrados em baixas quantidades, de tal forma que os processos biológicos que envolvem concentrações da ordem do nano molar, ou mesmo inferiores, podem ser determinadas sem perturbar o processo em estudo. Muitas combinações de diferentes moléculas com diferentes radionúclidos permitem traçar uma gama de caminhos moleculares específicos (e.g. processos biológicos de receptores e síntese de transmissores em caminhos de comunicação em células, processos metabólicos e expressão genética). A imagem pode ser executada repetidamente antes e depois de intervenções permitindo o uso de cada animal como o seu próprio controlo biológico. A investigação já realizada em curso que aplicam a PET ao estudos de pequenos animais, tem permitido compreender, entre outras coisas, a evolução de determinadas doenças e suas potenciais terapias. Contudo, existem algumas dificuldades de implementação desta técnica já que a informação obtida está condicionada pelos fenómenos físicos associados à interacção da radiação com a matéria, pelos instrumentos envolvidos na obtenção da informação e pela própria fisiologia do animal (por exemplo o seu movimento fisiológico). De facto, a fiabilidade da quantificação das imagens obtidas experimentalmente, em sistemas PET dedicados aos pequenos animais, é afectada ao mesmo tempo pelos limites de desempenho dos detectores (resolução espacial e em energia, sensibilidade, etc.), os efeitos físicos como a atenuação e a dispersão, que perturbam a reconstrução da imagem, e os efeitos fisiológicos (movimentos do animal). Na prática estes efeitos são corrigidos com métodos de correcção específicos com a finalidade de extrair parâmetros quantitativos fiáveis. Por outro lado, as características fisiológicas dos animais a estudar e a necessidade da existência de animais disponíveis, são factores adicionais de complexidade. Recentemente, tem sido dedicada alguma atenção aos efeitos resultantes dos movimentos fisiológicos, nomeadamente do movimento respiratório, na qualidade das imagens obtidas no decurso de um exame PET. Em particular, no caso do estudo dos tumores do pulmão (algo infelizmente muito frequente em humanos), o movimento fisiológico dos pulmões é uma fonte de degradação das imagens PET, podendo comprometer a sua resolução e o contraste entre regiões sãs e doentes deste orgão. A precisão quantitativa na determinação da concentração de actividade e dos volumes funcionais fica assim debilitada, sendo por vezes impedida a localização, detecção e quantificação do radiotraçador captado nas lesões pulmonares. De modo a conseguir diminuir estes efeitos, existe a necessidade de melhor compreender a influência deste movimento nos resultados PET. Neste contexto, as simulações Monte Carlo são um instrumento útil e eficaz de ajuda à optimização dos componentes dos detectores existentes, à concepção de novos detectores, ao desenvolviBaseados em modelos matemáticos dos processos físicos, químicos e, sempre que possível, biológicos, os métodos de simulação Monte Carlo são, desde há muito, uma ferramenta privilegiada para a obtenção de informação fiável da previsão do comportamento de sistemas complexos e por maioria de razão, para uma sua melhor compreensão. No contexto da Imagiologia Molecular, a plataforma de simulação Geant4 Application for Tomographic Emission (GATE), validada para as técnicas de imagem de Medicina Nuclear, permite a simulação por Monte Carlo dos processos de obtenção de imagem. Esta simulação pode mesmo ser feita quando se pretende estudar a distribuição de emissores de positrões cuja localização varia ao longo do tempo. Adicionalmente, estas plataformas permitem a utilização de modelos computacionais para modelar a anatomia e a fisiologia dos organismos em estudo mediante a utilização de uma sua representação digital realista denominada de fantôma. A grande vantagem na utilização destes fantômas relaciona-se com o facto de conhecermos as suas características geométricas (“anatómicas”) e de podermos controlar as suas características funcionais (“fisiológicas”). Podemos assim obter padrões a partir dos quais podemos avaliar e aumentar a qualidade dos equipamentos e técnicas de imagem. O objectivo do presente trabalho consiste na modelação e validação de uma plataforma de simulação do sistema microPET® FOCUS 220, usado em estudos de PET para pequenos animais, utilizando a plataforma de simulação GATE. A metodologia adoptada procurou reproduzir de uma forma realista, o ambiente de radiação e factores instrumentais relacionados com o sistema de imagem, assim como o formato digital dos dados produzidos pelo equipamento. Foram usados modelos computacionais, obtidos por segmentação de imagem de exames reais, para a avaliação da quantificação das imagens obtidas. Os resultados obtidos indicam que a plataforma produz resultados reprodutíveis, adequados para a sua utilização de estudos de pequenos animais em PET. Este objectivo foi concretizado estudando os efeitos combinados do tamanho das lesões, do rácio de concentração de actividade lesão-para-fundo e do movimento respiratório na recuperação de sinal de lesões esféricas localizadas no pulmão em imagens PET de pequenos animais. Para este efeito, foi implementada no código GATE uma representação digital em 4D de um ratinho de corpo inteiro (o fantôma MOBY). O MOBY permitiu reproduzir uma condição fisiológica que representa a respiração em condição de "stress", durante um exame típico de PET pequeno animal, e a inclusão de uma lesão esférica no pulmão tendo em conta o movimento da mesma. Foram realizadas um conjunto de simulações estáticas e dinâmicas usando 2-Deoxy-[18F]fluoro-D-glucose (FDG) tendo em consideração diferentes tamanhos das lesões e diferentes captações deste radiofármaco. O ruído da imagem e a resolução temporal foram determinadas usando imagens 3D e 4D. O rácio sínal-para-ruído (SNR), o rácio contraste-para-ruído (CNR), a relação lesão-fundo (target-to-background activity concentration ratio- TBR), a recuperação de contraste (CR) e a recuperação de volume (VR) foram também avaliados em função do tamanho da lesão e da actividade captada. Globalmente, os resultados obtidos demonstram que a perda de sinal depende tanto do tamanho da lesão como da captação de actividade na lesão. Nas simulações estáticas, onde não foi simulado movimento, os coeficientes de recuperação foram influenciados pelo efeito de volume parcial para os tamanhos mais reduzidos de lesão. Além disso, o aumento do contraste na lesão produz um aumento significativo no desvio padrão da média de sinal recuperado resultando numa diminuição no CNR e no SNR. Também concluímos que o movimento respiratório diminui significativamente a recuperação do sinal e que esta perda depende principalmente do tamanho da lesão. A melhor resolução temporal e resolução espacial foram obtidas nas simulações estáticas, onde não existia movimento envolvido. Os resultados simulados mostram que o efeito de volume parcial é dominante nas lesões mais pequenas devido à resolução espacial do sistema FOCUS, tanto nas imagens estáticas como nas dinâmicas. Além disso, para concentrações baixas de radiofármaco existe uma dificuldade inerente em quantificar a recuperação de sinal nas lesões comprometendo a análise quantitativa dos dados obtidos.Organ motion has become of great concern in medical imaging only recently. Respiratory motion is one source of degradation of PET images. Respiratory motion may lead to image blurring, which may result in reduced contrast and quantitative accuracy in terms of recovered activity concentration and functional volumes. Consequently, the motion of lungs hinders the localization, detection, and the quantification of tracer uptake in lung lesions. There is, therefore, a need to better understand the effects of this motion on PET data outcome. Medical imaging methods and devices are commonly evaluated through computer simulation. Computer generated phantoms are used to model patient anatomy and physiology, as well as the imaging process itself. A major advantage of using computer generated phantoms in simulation studies is that the anatomy and physiological functions of the phantom are known, thus providing a gold standard from which to evaluate and improve medical imaging devices and techniques. In this thesis, are presented the results of a research studied the combined effects of lesion size, lesion-to-background activity concentration ratio and respiratory motion on signal recovery of spherical lesions in small animal PET images using Monte Carlo simulation. Moreover, background activity is unavoidable and it causes significant noise and contrast loss in PET images. For these purposes, has been used the Geant4 Application for Tomographic Emission (GATE) Monte Carlo platform to model the microPET®FOCUS 220 system. Additionaly, was implemented the digital 4D Mouse Whole-Body (MOBY) phantom into GATE. A physiological “stress breathing” condition was created for MOBY in order to reproduce the respiratory mouse motion during a typical PET examination. A spherical lung lesion was implemented within this phantom and its motion also modelled. Over a complete respiratory cycle of 0.37 s was retrieved a set of 10 temporal frames (including the lesion movement) generated in addition to a non-gated data set. Sets of static (non-gated data) and dynamic (gated data) 2-Deoxy-[18F]fluoro-D-glucose (FDG) simulations were performed considering different lesion sizes and different activity uptakes. Image noise and temporal resolution were determined on 3D and 4D images. Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR), Target-to-Background activity concentration Ratio (TBR), Contrast Recovery (CR) and Volume Recovery (VR) were also evaluated as a function of lesion size and activity uptake. Globally, the results obtained show that signal loss depends both on lesion size and lesion activity uptake. In the non-gated data, where was no motion included (perfect motion correction), the recovery coefficients were influenced by the partial volume effect for the smallest lesion size. Moreover, the increased lesion contrast produces a significant increase on the standard deviation of the mean signal recover. This led to a decrease in CNR and SNR. In addition, respiratory motion significantly deteriorates signal recovery and this loss depends mainly of the lesion size. Best temporal resolution (volume recovery) and spatial resolution was given by the non-gated data, where no motion is involved. The simulated results show that the partial volume effect is dominant for small objects due to limited FOCUS system resolution in both 3D and 4D PET images. In addition, lower activity concentrations significantly deteriorates the lesion signal recovery compromising quantitative analysis.Fundação para a Ciência e a Tecnologia (FCT) under grant nº SFRH/BD/22723/200

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

The role of deep learning in structural and functional lung imaging

Background: Structural and functional lung imaging are critical components of pulmonary patient care. Image analysis methods, such as image segmentation, applied to structural and functional lung images, have significant benefits for patients with lung pathologies, including the computation of clinical biomarkers. Traditionally, machine learning (ML) approaches, such as clustering, and computational modelling techniques, such as CT-ventilation imaging, have been used for segmentation and synthesis, respectively. Deep learning (DL) has shown promise in medical image analysis tasks, often outperforming alternative methods. Purpose: To address the hypothesis that DL can outperform conventional ML and classical image analysis methods for the segmentation and synthesis of structural and functional lung imaging via: i. development and comparison of 3D convolutional neural networks (CNNs) for the segmentation of ventilated lung using hyperpolarised (HP) gas MRI. ii. development of a generalisable, multi-centre CNN for segmentation of the lung cavity using 1H-MRI. iii. the proposal of a framework for estimating the lung cavity in the spatial domain of HP gas MRI. iv. development of a workflow to synthesise HP gas MRI from multi-inflation, non-contrast CT. v. the proposal of a framework for the synthesis of fully-volumetric HP gas MRI ventilation from a large, diverse dataset of non-contrast, multi-inflation 1H-MRI scans. Methods: i. A 3D CNN-based method for the segmentation of ventilated lung using HP gas MRI was developed and CNN parameters, such as architecture, loss function and pre-processing were optimised. ii. A 3D CNN trained on a multi-acquisition dataset and validated on data from external centres was compared with a 2D alternative for the segmentation of the lung cavity using 1H-MRI. iii. A dual-channel, multi-modal segmentation framework was compared to single-channel approaches for estimation of the lung cavity in the domain of HP gas MRI. iv. A hybrid data-driven and model-based approach for the synthesis of HP gas MRI ventilation from CT was compared to approaches utilising DL or computational modelling alone. v. A physics-constrained, multi-channel framework for the synthesis of fully-volumetric ventilation surrogates from 1H-MRI was validated using five-fold cross-validation and an external test data set. Results: i. The 3D CNN, developed via parameterisation experiments, accurately segmented ventilation scans and outperformed conventional ML methods. ii. The 3D CNN produced more accurate segmentations than its 2D analogues for the segmentation of the lung cavity, exhibiting minimal variation in performance between centres, vendors and acquisitions. iii. Dual-channel, multi-modal approaches generate significant improvements compared to methods which use a single imaging modality for the estimation of the lung cavity. iv. The hybrid approach produced synthetic ventilation scans which correlate with HP gas MRI. v. The physics-constrained, 3D multi-channel synthesis framework outperformed approaches which did not integrate computational modelling, demonstrating generalisability to external data. Conclusion: DL approaches demonstrate the ability to segment and synthesise lung MRI across a range of modalities and pulmonary pathologies. These methods outperform computational modelling and classical ML approaches, reducing the time required to adequately edit segmentations and improving the modelling of synthetic ventilation, which may facilitate the clinical translation of DL in structural and functional lung imaging