Augmented Image-Guidance for Transcatheter Aortic Valve Implantation

The introduction of transcatheter aortic valve implantation (TAVI), an innovative stent-based technique for delivery of a bioprosthetic valve, has resulted in a paradigm shift in treatment options for elderly patients with aortic stenosis. While there have been major advancements in valve design and access routes, TAVI still relies largely on single-plane fluoroscopy for intraoperative navigation and guidance, which provides only gross imaging of anatomical structures. Inadequate imaging leading to suboptimal valve positioning contributes to many of the early complications experienced by TAVI patients, including valve embolism, coronary ostia obstruction, paravalvular leak, heart block, and secondary nephrotoxicity from contrast use. A potential method of providing improved image-guidance for TAVI is to combine the information derived from intra-operative fluoroscopy and TEE with pre-operative CT data. This would allow the 3D anatomy of the aortic root to be visualized along with real-time information about valve and prosthesis motion. The combined information can be visualized as a `merged\u27 image where the different imaging modalities are overlaid upon each other, or as an `augmented\u27 image, where the location of key target features identified on one image are displayed on a different imaging modality. This research develops image registration techniques to bring fluoroscopy, TEE, and CT models into a common coordinate frame with an image processing workflow that is compatible with the TAVI procedure. The techniques are designed to be fast enough to allow for real-time image fusion and visualization during the procedure, with an intra-procedural set-up requiring only a few minutes. TEE to fluoroscopy registration was achieved using a single-perspective TEE probe pose estimation technique. The alignment of CT and TEE images was achieved using custom-designed algorithms to extract aortic root contours from XPlane TEE images, and matching the shape of these contours to a CT-derived surface model. Registration accuracy was assessed on porcine and human images by identifying targets (such as guidewires or coronary ostia) on the different imaging modalities and measuring the correspondence of these targets after registration. The merged images demonstrated good visual alignment of aortic root structures, and quantitative assessment measured an accuracy of less than 1.5mm error for TEE-fluoroscopy registration and less than 6mm error for CT-TEE registration. These results suggest that the image processing techniques presented have potential for development into a clinical tool to guide TAVI. Such a tool could potentially reduce TAVI complications, reducing morbidity and mortality and allowing for a safer procedure

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

Traitement et exploration d'images TDM pour l'évaluation des bioprothèses valvulaires aortiques

Le but de cette étude est d évaluer la faisabilité de l analyse tomodensitométrique 3D des bioprothèses aortiques pour faciliter leur évaluation morphologique durant le suivi et d aider la sélection de cas et améliorer la planification d une procédure valvein-valve. Le challenge était représenté par le rehaussement des feuillets valvulaires, en raison d images très bruitées. Un angio-scanner synchronisé était réalisé chez des patients porteurs d une bioprotèses aortique dégénérée avant réintervention (images in-vivo). Différentes méthodes pour la réduction du bruit étaient proposées. La reconstruction tridimensionnelle des bioprothèses était réalisée en utilisant des méthodes de segmentation de régions par "sticks". Après réopération ces méthodes étaient appliquées aux images scanner des bioprothèses explantées (images ex-vivo) et utilisées comme référence. La réduction du bruit obtenue par le filtre stick modifié montrait meilleurs résultats en rapport signal/bruit en comparaison aux filtres de diffusion anisotropique. Toutes les méthodes de segmentation ont permis une reconstruction 3D des feuillets. L analyse qualitative a montré une bonne concordance entre les images obtenues in-vivo et les altérations des bioprothèses. Les résultats des différentes méthodes étaient comparés par critères volumétriques et discutés. Les bases d'une première approche de visualisation spatio-temporelle d'images TDM 3D+T de la prothèse valvulaire ont été proposés. Elle implique des techniques de rendu volumique et de compensation de mouvement. Son application à la valve native a aussi été envisagée. Les images scanner des bioprothèses aortiques nécessitent un traitement de débruitage et de réduction des artéfacts de façon à permettre le rehaussement des feuillets prothétiques. Les méthodes basées sticks semblent constituer une approche pertinente pour caractériser morphologiquement la dégénérescence des bioprothèses.The aim of the study was to assess the feasibility of CT based 3D analysis of degenerated aortic bioprostheses to make easier their morphological assessment. This could be helpful during regular follow-up and for case selection, improved planning and mapping of valve-in-valve procedure. The challenge was represented by leaflets enhancement because of highly noised CT images. Contrast-enhanced ECG-gated CT scan was performed in patients with degenerated aortic bioprostheses before reoperation (in-vivo images). Different methods for noise reduction were tested and proposed. 3D reconstruction of bioprostheses components was achieved using stick based region segmentation methods. After reoperation, segmentation methods were applied to CT images of the explanted prostheses (exvivo images). Noise reduction obtained by improved stick filter showed best results in terms of signal to noise ratio comparing to anisotropic diffusion filters. All segmentation methods applied to the best phase of in-vivo images allowed 3D bioprosthetic leaflets reconstruction. Explanted bioprostheses CT images were also processed and used as reference. Qualitative analysis revealed a good concordance between the in-vivo images and the bioprostheses alterations. Results from different methods were compared by means of volumetric criteria and discussed. A first approach for spatiotemporal visualization of 3D+T images of valve bioprosthesis has been proposed. Volume rendering and motion compensation techniques were applied to visualize different phases of CT data. Native valve was also considered. ECG-gated CT images of aortic bioprostheses need a preprocessing to reduce noise and artifacts in order to enhance prosthetic leaflets. Stick based methods seems to provide an interesting approach for the morphological characterization of degenerated bioprostheses.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Traitement et exploration d'images TDM pour l'évaluation des bioprothèses valvulaires aortiques

Le but de cette étude est d évaluer la faisabilité de l analyse tomodensitométrique 3D des bioprothèses aortiques pour faciliter leur évaluation morphologique durant le suivi et d aider la sélection de cas et améliorer la planification d une procédure valvein-valve. Le challenge était représenté par le rehaussement des feuillets valvulaires, en raison d images très bruitées. Un angio-scanner synchronisé était réalisé chez des patients porteurs d une bioprotèses aortique dégénérée avant réintervention (images in-vivo). Différentes méthodes pour la réduction du bruit étaient proposées. La reconstruction tridimensionnelle des bioprothèses était réalisée en utilisant des méthodes de segmentation de régions par "sticks". Après réopération ces méthodes étaient appliquées aux images scanner des bioprothèses explantées (images ex-vivo) et utilisées comme référence. La réduction du bruit obtenue par le filtre stick modifié montrait meilleurs résultats en rapport signal/bruit en comparaison aux filtres de diffusion anisotropique. Toutes les méthodes de segmentation ont permis une reconstruction 3D des feuillets. L analyse qualitative a montré une bonne concordance entre les images obtenues in-vivo et les altérations des bioprothèses. Les résultats des différentes méthodes étaient comparés par critères volumétriques et discutés. Les bases d'une première approche de visualisation spatio-temporelle d'images TDM 3D+T de la prothèse valvulaire ont été proposés. Elle implique des techniques de rendu volumique et de compensation de mouvement. Son application à la valve native a aussi été envisagée. Les images scanner des bioprothèses aortiques nécessitent un traitement de débruitage et de réduction des artéfacts de façon à permettre le rehaussement des feuillets prothétiques. Les méthodes basées sticks semblent constituer une approche pertinente pour caractériser morphologiquement la dégénérescence des bioprothèses.The aim of the study was to assess the feasibility of CT based 3D analysis of degenerated aortic bioprostheses to make easier their morphological assessment. This could be helpful during regular follow-up and for case selection, improved planning and mapping of valve-in-valve procedure. The challenge was represented by leaflets enhancement because of highly noised CT images. Contrast-enhanced ECG-gated CT scan was performed in patients with degenerated aortic bioprostheses before reoperation (in-vivo images). Different methods for noise reduction were tested and proposed. 3D reconstruction of bioprostheses components was achieved using stick based region segmentation methods. After reoperation, segmentation methods were applied to CT images of the explanted prostheses (exvivo images). Noise reduction obtained by improved stick filter showed best results in terms of signal to noise ratio comparing to anisotropic diffusion filters. All segmentation methods applied to the best phase of in-vivo images allowed 3D bioprosthetic leaflets reconstruction. Explanted bioprostheses CT images were also processed and used as reference. Qualitative analysis revealed a good concordance between the in-vivo images and the bioprostheses alterations. Results from different methods were compared by means of volumetric criteria and discussed. A first approach for spatiotemporal visualization of 3D+T images of valve bioprosthesis has been proposed. Volume rendering and motion compensation techniques were applied to visualize different phases of CT data. Native valve was also considered. ECG-gated CT images of aortic bioprostheses need a preprocessing to reduce noise and artifacts in order to enhance prosthetic leaflets. Stick based methods seems to provide an interesting approach for the morphological characterization of degenerated bioprostheses.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Identification of Intracranial Lesions with Dual-Energy Computed Tomography and Magnetic Resonance Phase Imaging

On conventional Single-energy Computed Tomography (SECT), lesions with an attenuation greater than 100 Hounsfield Units (HU) can be definitively diagnosed as calcification. However, low-density calcifications and hemorrhage may have overlapping attenuation ranges between 40 and 100 HU and, therefore, cannot be differentiated with SECT alone. On T2*-weighted Gradient Recalled Echo (GRE) MRI, these lesions appear as “foci of susceptibility” in which their signal is hypointense due to the magnetic susceptibility of the lesions differing from that of the background tissue. Dual-energy Computed Tomography (DECT) and Phase-Sensitive Magnetic Resonance Imaging (PS-MRI) represent two new imaging paradigms which both have the potential to more accurately identify intracranial calcification and hemorrhage. In DECT, x-ray tomography is acquired at two tube voltages; because x-ray attenuation is energy- and material-dependent, the data can be used to differentiate between materials that may have the same signal level on SECT. PS-MRI utilizes the phase data from T2*-weighted MRI acquisitions to determine how the local magnetic field varies across the image. By applying post-processing algorithms such as Quantitative Susceptibility Mapping (QSM), the phase can be used to calculate the magnetic susceptibility of a lesion. Since calcifications are diamagnetic and hemorrhage paramagnetic, we can make inferences about a lesion’s composition from these algorithms. The objective of this dissertation work was to characterize brain lesions, discovered with traditional imaging methods, as either hemorrhagic or calcific by using Dual-Energy Computed Tomography (DECT) and Phase-Sensitive Magnetic Resonance Imaging (PS-MRI). To this end, MRI-compatible phantoms featuring models of both calcific and hemorrhagic lesions were developed and validated. This resulted in two phantoms with biologically similar lesion models that were then used to test the feasibility of differentiating calcific and hemorrhagic lesions with PS-MRI post-processing methods, in which QSM was able to accurately differentiate calcific and hemorrhagic lesion models. Finally, we undertook a patient trial testing the feasibility of identifying calcification and chronic hemorrhage in humans using both DECT and QSM in which the two modalities had accuracies of 99.7% (327/328) and 99.4% (326/328), respectively. The two modalities were concordant for 99.3% (148/149) lesions with SECT attenuation under 100 HU

Quantification of Coronary Artery Atherosclerotic Burden and Muscle Mass: Exploratory Comparison of Two Freely Available Software Programs

Abstract: Coronary artery calcification and sarcopenia may have a relevant prognostic impact in oncological and non‐oncological patients. The use of freeware software is promising for quantitative evaluation of these parameters after whole‐body positron emission tomography (PET)/computed tomography (CT) and might be useful for one‐stop shop risk stratification without additional radiation ionizing burden and further charges to health care costs. In this study, we compared two semiautomatic freeware software tools (Horos Medical Image software and LIFEx) for the assessment of coronary artery calcium (CAC) score and muscle mass in 40 patients undergoing whole‐body PET/CT. The muscle areas obtained by the two software programs were comparable, showing high correlation with Lin’s concordance coefficient (0.9997; 95% confidence intervals: 0.9995–0.9999) and very good agreement with Bland–Altman analysis (mean difference = 0.41 cm2, lower limit = −1.06 cm2, upper limit = 1.89) was also found. For CAC score, Lin’s concordance correlation coefficient was 0.9976 (95% confidence intervals: 0.9965–0.9984) and in a Bland–Altman analysis an increasing mean difference from 8 to 78 by the mean values (intercept = −0.050; slope = 0.054; p < 0.001) was observed, with a slight overestimation of Horos CAC score as compared to LIFEx, likely due to a different calculation method of the CAC score, with the ROI being equal for the two software programs. Our results demonstrated that off‐line analysis performed with freeware software may allow a comprehensive evaluation of the oncological patient, making available the evaluation of parameters, such as muscle mass and calcium score, that may be relevant for the staging and prognostic stratification of these patients, beside standard data obtained by PET/CT imaging. For this purpose, the Horos and LIFEx software seem to be interchangeable

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

OBJECTIVE: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. MATERIALS AND METHODS: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. RESULTS: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. CONCLUSION: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis

Shedding light on living cells and mineralised tissues using Raman spectroscopy

Raman micro-spectroscopy presents a highly sensitive, non-invasive, and rapid way to collect biochemical information from cells and tissues. The resulting Raman spectrum is a chemical ‘fingerprint’ containing a wealth of molecular level information which has been used to characterize, monitor, compare and confirm biological processes from the cellular to tissue levels. The work presented in this thesis utilizes Raman spectroscopy to test live in vitro cellular models, classify human tissues of interest, and determine the biomolecular differences in tissue samples which are diseased or undergoing therapeutic treatment. Additionally new ways of visualizing and interpreting multivariate analytical results are proposed and demonstrated to ease the determination of the biomolecular features which are most important when comparing sample groups. A persistent challenge in the interpretation of information rich biological Raman spectra includes the multitude of signals from lipids, proteins, carbohydrates, nucleic acids, and minerals found in a limited spectral range and in some instances overlapping significantly. Partial Least Squares – Discriminant Analysis (PLS-DA) Variable Importance Projection (VIP) scores were presented as heat maps overlaying difference spectra to ease the visualization of significant biochemical bond changes between sample groups and their trends. The advantages of applying PLS-DA VIP scores in this way are demonstrated in well studied and known system including a cultured cellular model incorporating fixation methods and a human tissue comparison between healthy and osteoporotic bone. PLS-DA VIP score plots were additionally utilized to characterize and compare the biolomecular environments surrounding the recently described microscopic mineral inclusions in human aortic valves and aortae. The PLS-DA VIP score plots exposed the chemical differences in these systems through highlighting the corresponding spectral bands in an easy to read and interpret way. Raman micro-spectroscopy was also applied to investigate an in vitro ‘calcified’ porcine aortic valvular interstitial cell model. This model system was probed for the first time using the combination of Raman micro-spectroscopy and complimentary gold standard biological techniques to determine the protein and potential mineral content within these nodular, cellular systems. The ‘calcified’ porcine aortic valvular cell nodules showed no evidence of mineral inclusion. These nodules did exhibit a heavy extracellular matrix production including the production of collagen I. The porcine aortic valvular cell nodules acting as a model system for diseased aortic valve tissue requires not only the characterization of the cell nodule in vitro but also the characterization of the human disease spectrum which the model is suggested to replicate. The discovery and characterisation of microscopic mineral spherical inclusions (50nm-200µm) located in both valvular and vascular tissues leads to an interesting question on the introduction and role of microscopic mineral deposits in cardiovascular disease. Here Raman micro-spectroscopy was utilized to investigate the organic matrix surrounding these microscopic mineral deposits to determine if any colocalised protein changes exist. Protein and specifically collagen changes are demonstrated between tissues with and without the spherical mineral deposits despite being macroscopically indistinguishable. Raman spectroscopy was also utilized to provide direct insights into tissue constituent and structural changes on the molecular level in heat-induced tissue fusion via radio-frequency (RF) energy. This type of tissue fusion has gained wide acceptance clinically and is presented here as the first optical-Raman-spectroscopy study on tissue fusion samples in vitro. This study exposed spectroscopic evidence for the loss of distinct collagen fibres rich tissue layers as well as the denaturing and restructuring of collagen crosslinks post RF fusion. Raman spectroscopy is a demonstrated, powerful, biomolecular imaging technique which benefits from advancements in mathematical analytical techniques as well as its own application in biological investigations. This thesis explores the application of Raman spectroscopy in combination with powerful analytical techniques to further characterize and compare biological systems of interest.Open Acces