Hyperpolarized Xenon-129 Magnetic Resonance Imaging of Functional Lung Microstructure

Hyperpolarized 129Xe (HXe) is a non-invasive contrast agent for lung magnetic resonance imaging (MRI), which upon inhalation follows the functional pathway of oxygen in the lung by dissolving into lung tissue structures and entering the blood stream. HXe MRI therefore provides unique opportunities for functional lung imaging of gas exchange which occurs from alveolar air spaces across the air-blood boundary into parenchymal tissue. However challenges in acquisition speed and signal-to-noise ratio have limited the development of a HXe imaging biomarker to diagnose lung disease. This thesis addresses these challenges by introducing parallel imaging to HXe MRI. Parallel imaging requires dedicated hardware. This work describes design, implementation, and characterization of a 32-channel phased-array chest receive coil with an integrated asymmetric birdcage transmit coil tuned to the HXe resonance on a 3 Tesla MRI system. Using the newly developed human chest coil, a functional HXe imaging method, multiple exchange time xenon magnetization transfer contrast (MXTC) is implemented. MXTC dynamically encodes HXe gas exchange into the image contrast. This permits two parameters to be derived regionally which are related to gas-exchange functionality by characterizing tissue-to-alveolar-volume ratio and alveolar wall thickness in the lung parenchyma. Initial results in healthy subjects demonstrate the sensitivity of MXTC by quantifying the subtle changes in lung microstructure in response to orientation and lung inflation. Our results in subjects with lung disease show that the MXTC-derived functional tissue density parameter exhibits excellent agreement with established imaging techniques. The newly developed dynamic parameter, which characterizes the alveolar wall, was elevated in subjects with lung disease, most likely indicating parenchymal inflammation. In light of these observations we believe that MXTC has potential as a biomarker for the regional quantification of 1) emphysematous tissue destruction in chronic obstructive pulmonary disease (using the tissue density parameter) and 2) parenchymal inflammation or thickening (using the wall thickness parameter). By simultaneously quantifying two lung function parameters, MXTC provides a more comprehensive picture of lung microstructure than existing lung imaging techniques and could become an important non-invasive and quantitative tool to characterize pulmonary disease

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Deep learning assisted MRI guided attenuation correction in PET

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological and functional details of the tissue at the molecular level. However, the acquired PET images have some limitations such as the attenuation. PET attenuation correction is an essential step to obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners, magnetic resonance (MR) images are used to address the problem of PET attenuation correction. The MR images segmentation is one simple and robust approach to create pseudo computed tomography (CT) images, which are used to generate attenuation coefficient maps to correct the PET attenuation. Recently, deep learning has been proposed and used as a promising technique to efficiently perform MR and various medical images segmentation. In this research work, deep learning guided segmentation approaches have been proposed to enhance the bone class segmentation of MR brain images in order to generate accurate pseudo-CT images. The first approach has introduced the combination of handcrafted features with deep learning features to enrich the set of features. Multiresolution analysis techniques, which generate multiscale and multidirectional coefficients of an image such as contourlet and shearlet transforms, are applied and combined with deep convolutional neural network (CNN) features. Different experiments have been conducted to investigate the number of selected coefficients and the insertion location of the handcrafted features. The second approach aims at reducing the segmentation algorithm’s complexity while maintaining the segmentation performance. An attention based convolutional encode-decoder network has been proposed to adaptively recalibrate the deep network features. This attention based network consists of two different squeeze and excitation blocks that excite the features spatially and channel wise. The two blocks are combined sequentially to decrease the number of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w images. Multiple fine tuning approaches and experiments have been conducted to study the best fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been used to carry an objective evaluation to measure the segmentation performance of the results obtained by the three proposed methods. The first and second approaches have been validated with other studies in the literature that applied deep network based segmentation technique to perform MR based attenuation correction for PET images. The proposed methods have shown an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC) from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%. The proposed excitation-based CNN has decreased the model complexity by decreasing the number of trainable parameters by more than 46% where less computing resources are required to train the model. The proposed hybrid transfer learning method has shown its superiority to build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied transfer learning methods especially with the bone class where the DSC is increased from 0.3841 to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than transfer learning using open and conservative fine tuning

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Estimation of thorax shape for forward modelling in lungs EIT

The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models

Rapid generation of subject-specific thorax forward models

For real-time monitoring of lung function using accurate patient geometry, shape information needs to be acquired and a forward model generated rapidly. This paper shows that warping a cylindrical model to an acquired shape results in meshes of acceptable mesh quality, in terms of stretch and aspect ratio

Torso shape detection to improve lung monitoring

Two methodologies are proposed to detect the patient-specific boundary of the chest, aiming to produce a more accurate forward model for EIT analysis. Thus, a passive resistive and an inertial prototypes were prepared to characterize and reconstruct the shape of multiple phantoms. Preliminary results show how the passive device generates a minimum scatter between the reconstructed image and the actual shap

Nanoparticle electrical impedance tomography

We have developed a new approach to imaging with electrical impedance tomography (EIT) using gold nanoparticles (AuNPs) to enhance impedance changes at targeted tissue sites. This is achieved using radio frequency (RF) to heat nanoparticles while applying EIT imaging. The initial results using 5-nm citrate coated AuNPs show that heating can enhance the impedance in a solution containing AuNPs due to the application of an RF field at 2.60 GHz

Biomedical Engineering

Biomedical engineering is currently relatively wide scientific area which has been constantly bringing innovations with an objective to support and improve all areas of medicine such as therapy, diagnostics and rehabilitation. It holds a strong position also in natural and biological sciences. In the terms of application, biomedical engineering is present at almost all technical universities where some of them are targeted for the research and development in this area. The presented book brings chosen outputs and results of research and development tasks, often supported by important world or European framework programs or grant agencies. The knowledge and findings from the area of biomaterials, bioelectronics, bioinformatics, biomedical devices and tools or computer support in the processes of diagnostics and therapy are defined in a way that they bring both basic information to a reader and also specific outputs with a possible further use in research and development