Regularized Shallow Image Prior for Electrical Impedance Tomography

Untrained Neural Network Prior (UNNP) based algorithms have gained increasing popularity in tomographic imaging, as they offer superior performance compared to hand-crafted priors and do not require training. UNNP-based methods usually rely on deep architectures which are known for their excellent feature extraction ability compared to shallow ones. Contrary to common UNNP-based approaches, we propose a regularized shallow image prior method that combines UNNP with hand-crafted prior for Electrical Impedance Tomography (EIT). Our approach employs a 3-layer Multi-Layer Perceptron (MLP) as the UNNP in regularizing 2D and 3D EIT inversion. We demonstrate the influence of two typical hand-crafted regularizations when representing the conductivity distribution with shallow MLPs. We show considerably improved EIT image quality compared to conventional regularization algorithms, especially in structure preservation. The results suggest that combining the shallow image prior and the hand-crafted regularization can achieve similar performance to the Deep Image Prior (DIP) but with less architectural dependency and complexity of the neural network

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'

Imaging biomarkers in the idiopathic inflammatory myopathies

Idiopathic inflammatory myopathies (IIMs) are a group of acquired muscle diseases with muscle inflammation, weakness, and other extra-muscular manifestations. IIMs can significantly impact the quality of life, and management of IIMs often requires a multi-disciplinary approach. Imaging biomarkers have become an integral part of the management of IIMs. Magnetic resonance imaging (MRI), muscle ultrasound, electrical impedance myography (EIM), and positron emission tomography (PET) are the most widely used imaging technologies in IIMs. They can help make the diagnosis and assess the burden of muscle damage and treatment response. MRI is the most widely used imaging biomarker of IIMs and can assess a large volume of muscle tissue but is limited by availability and cost. Muscle ultrasound and EIM are easy to administer and can even be performed in the clinical setting, but they need further validation. These technologies may complement muscle strength testing and laboratory studies and provide an objective assessment of muscle health in IIMs. Furthermore, this is a rapidly progressing field, and new advances are going to equip care providers with a better objective assessment of IIMS and eventually improve patient management. This review discusses the current state and future direction of imaging biomarkers in IIMs

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

Synergistic Tomographic Image Reconstruction: Part 1

This special issue focuses on synergistic tomographic image reconstruction in a range of contributions in multiple disciplines and various application areas. The topic of image reconstruction covers substantial inverse problems (Mathematics) which are tackled with various methods including statistical approaches (e.g. Bayesian methods, Monte Carlo) and computational approaches (e.g. machine learning, computational modelling, simulations). The issue is separated in two volumes. This volume focuses mainly on algorithms and methods. Some of the articles will demonstrate their utility on real-world challenges, either medical applications (e.g. cardiovascular diseases, proton therapy planning) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issue is to bring together different scientific communities which do not usually interact as they do not share the same platforms (such as journals and conferences). This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area

Autocalibration Region Extending Through Time: A Novel GRAPPA Reconstruction Algorithm to Accelerate 1H Magnetic Resonance Spectroscopic Imaging

Magnetic resonance spectroscopic imaging (MRSI) has the ability to noninvasively interrogate metabolism in vivo. However, excessively long scan times have thus far prevented its adoption into routine clinical practice. Generalized autocalibrating partially parallel acquisitions (GRAPPA) is a parallel imaging technique that allows one to reduce acquisition duration and use spatial sensitivity correlations to reconstruct the unsampled data points. The coil sensitivity weights are determined implicitly via a fully-sampled autocalibration region in k-space. In this dissertation, a novel GRAPPA-based algorithm is presented for the acceleration of 1H MRSI. Autocalibration Region extending Through Time (ARTT) GRAPPA instead extracts the coil weights from a region in k-t space, allowing for undersampling along each spatial dimension. This technique, by exploiting spatial-spectral correlations present in MRSI data, allows for a more accurate determination of the coil weights and subsequent parallel imaging reconstruction. This improved reconstruction accuracy can then be traded for more aggressive undersampling and a further reduction of acquisition duration. It is shown that the ARTT GRAPPA technique allows for approximately two-fold more aggressive undersampling than the conventional technique while achieving the same reconstruction accuracy. This accelerated protocol is then applied to acquire high-resolution brain metabolite maps in less than twenty minutes in three healthy volunteers at B0 = 7 T

Review of Journal of Cardiovascular Magnetic Resonance 2014

There were 102 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2014, which is a 6 % decrease on the 109 articles published in 2013. The quality of the submissions continues to increase. The 2013 JCMR Impact Factor (which is published in June 2014) fell to 4.72 from 5.11 for 2012 (as published in June 2013). The 2013 impact factor means that the JCMR papers that were published in 2011 and 2012 were cited on average 4.72 times in 2013. The impact factor undergoes natural variation according to citation rates of papers in the 2 years following publication, and is significantly influenced by highly cited papers such as official reports. However, the progress of the journal’s impact over the last 5 years has been impressive. Our acceptance rate is <25 % and has been falling because the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. For this reason, the Editors have felt that it is useful once per calendar year to summarize the papers for the readership into broad areas of interest or theme, so that areas of interest can be reviewed in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality papers to JCMR for publication