Accuracy of registration algorithms in subtraction CT of the lungs: A digital phantom study

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Organotopic organization of the porcine mid-cervical vagus nerve

Introduction: Despite detailed characterization of fascicular organization of somatic nerves, the functional anatomy of fascicles evident in human and large mammal cervical vagus nerve is unknown. The vagus nerve is a prime target for intervention in the field of electroceuticals due to its extensive distribution to the heart, larynx, lungs, and abdominal viscera. However, current practice of the approved vagus nerve stimulation (VNS) technique is to stimulate the entire nerve. This produces indiscriminate stimulation of non-targeted effectors and undesired side effects. Selective neuromodulation is now a possibility with a spatially-selective vagal nerve cuff. However, this requires the knowledge of the fascicular organization at the level of cuff placement to inform selectivity of only the desired target organ or function. / Methods and results: We imaged function over milliseconds with fast neural electrical impedance tomography and selective stimulation, and found consistent spatially separated regions within the nerve correlating with the three fascicular groups of interest, suggesting organotopy. This was independently verified with structural imaging by tracing anatomical connections from the end organ with microCT and the development of an anatomical map of the vagus nerve. This confirmed organotopic organization. / Discussion: Here we show, for the first time, localized fascicles in the porcine cervical vagus nerve which map to cardiac, pulmonary and recurrent laryngeal function (N = 4). These findings pave the way for improved outcomes in VNS as unwanted side effects could be reduced by targeted selective stimulation of identified organ-specific fiber-containing fascicles and the extension of this technique clinically beyond the currently approved disorders to treat heart failure, chronic inflammatory disorders, and more

Doctor of Philosophy

dissertationImage-based biomechanics, particularly numerical modeling using subject-specific data obtained via imaging, has proven useful for elucidating several biomechanical processes, such as prediction of deformation due to external loads, applicable to both normal function and pathophysiology of various organs. As the field evolves towards applications that stretch the limits of imaging hardware and acquisition time, the information traditionally expected as input for numerical routines often becomes incomplete or ambiguous, and requires specific acquisition and processing strategies to ensure physical accuracy and compatibility with predictive mathematical modeling. These strategies, often derivatives or specializations of traditional mechanics, effectively extend the nominal capability of medical imaging hardware providing subject-specific information coupled with the option of using the results for predictive numerical simulations. This research deals with the development of tools for extracting mechanical measurements from a finite set of imaging data and finite element analysis in the context of constructing structural atlases of the heart, understanding the biomechanics of the venous vasculature, and right ventricular failure. The tools include: (1) application of Hyperelastic Warping image registration to displacement-encoded MRI for reconstructing absolute displacement fields, (2) combination of imaging and a material parameter identification approach to measure morphology, deformation, and mechanical properties of vascular tissue, and (3) extrapolation of diffusion tensor MRI acquired at a single time point for the prediction the structural changes across the cardiac cycle with mechanical simulations. Selected tools were then applied to evaluate structural changes in a reversible animal model for right ventricular failure due to pressure overload

Imaging fascicular organisation in mammalian vagus nerve for selective VNS

Nerves contain a large number of nerve fibres, or axons, organised into bundles known as fascicles. Despite the somatic nervous system being well understood, the organisation of the fascicles within the nerves of the autonomic nervous system remains almost completely unknown. The new field of bioelectronics medicine, Electroceuticals, involves the electrical stimulation of nerves to treat diseases instead of administering drugs or performing complex surgical procedures. Of particular interest is the vagus nerve, a prime target for intervention due to its afferent and efferent innervation to the heart, lungs and majority of the visceral organs. Vagus nerve stimulation (VNS) is a promising therapy for treatment of various conditions resistant to standard therapeutics. However, due to the unknown anatomy, the whole nerve is stimulated which leads to unwanted off-target effects. Electrical Impedance Tomography (EIT) is a non-invasive medical imaging technique in which the impedance of a part of the body is inferred from electrode measurements and used to form a tomographic image of that part. Micro-computed tomography (microCT) is an ex vivo method that has the potential to allow for imaging and tracing of fascicles within experimental models and facilitate the development of a fascicular map. Additionally, it could validate the in vivo technique of EIT. The aim of this thesis was to develop and optimise the microCT imaging method for imaging the fascicles within the nerve and to determine the fascicular organisation of the vagus nerve, ultimately allowing for selective VNS. Understanding and imaging the fascicular anatomy of nerves will not only allow for selective VNS and the improvement of its therapeutic efficacy but could also be integrated into the study on all peripheral nerves for peripheral nerve repair, microsurgery and improving the implementation of nerve guidance conduits. Chapter 1 provides an introduction to vagus nerve anatomy and the principles of microCT, neuronal tracing and EIT. Chapter 2 describes the optimisation of microCT for imaging the fascicular anatomy of peripheral nerves in the experimental rat sciatic and pig vagus nerve models, including the development of pre-processing methods and scanning parameters. Cross-validation of this optimised microCT method, neuronal tracing and EIT in the rat sciatic nerve was detailed in Chapter 3. Chapter 4 describes the study with microCT with tracing, EIT and selective stimulation in pigs, a model for human nerves. The microCT tracing approach was then extended into the subdiaphragmatic branches of the vagus nerves, detailed in Chapter 5. The ultimate goal of human vagus nerve tracing was preliminarily performed and described in Chapter 6. Chapter 7 concludes the work and describes future work. Lastly, Appendix 1 (Chapter 8) is a mini review on the application of selective vagus nerve stimulation to treat acute respiratory distress syndrome and Appendix 2 is morphological data corresponding to Chapter 4

Magnetic Resonance Imaging of Ventilation and Perfusion in the Lung

Methods, devices, and systems are disclosed for implementing a fully quantitative non-injectable contrast proton MRI technique to measure spatial ventilation-perfusion (VA/Q) matching and spatial distribution of ventilation and perfusion. In one aspect, a method using MRI to characterize ventilation and perfusion in a lung includes acquiring an MR image of the lung having MR data in a voxel and obtaining a breathing frequency parameter, determining a water density value, a specific ventilation value, and a perfusion value in at least one voxel of the MR image based on the MR data and using the water density value to determine an air content value, and determining a ventilation-perfusion ratio value that is the product of the specific ventilation value, the air content value, the inverse of the perfusion value, and the breathing frequency

Bent Laue Crystals in Biomedical X-ray Imaging Applications

This dissertation presents several synchrotron-based biomedical X-ray imaging projects involving bent Laue silicon (Si) crystals. Log-spiral bent Laue analyzers (BLA) were made from 20 micron thick Si crystals to map manganese (Mn) fluorescence in the X-ray fluorescence (XRF) imaging of the human brain with Parkinson's disease (PD). The BLA improved Mn specificity in XRF imaging and achieved a Mn detection limit of 0.5 mM concentration and an energy resolution of 34.5 eV. A novel method of three dimensional (3D) confocal XRF imaging of iodine was designed based on the one dimensional (1D) focusing ability of a log-spiral BLA made from 175 micron thick Si crystals. Combined with a pencil beam or a two dimensional (2D) focused beam, a 3D voxel of 100 × 100 × 124 cubic micron could be used to probe the 3D elemental mapping of a sample. A cylindrical bent Laue monochromator (BLM) was made from 600 micron thick Si crystals to simultaneously prepare three beams for the three-energy K-Edge Subtraction (KES) imaging and KES Computed Tomography (CT). A novel three-beam chopper was made as the first beam chopper for fast switching among the three beams. The three-energy KES imaging was successfully used to track uptake of injected iodine with time in a live mouse. The first simultaneous dual-energy KES imaging and the first KES CT imaging were successfully performed in Canada at the Biomedical Imaging and Therapy (BMIT) beamline. A novel rat head restraint and its corresponding field flatteners were constructed using a rapid-prototyping machine for the KES project based on CT scan data of a rat. This type of animal restraint worked well to immobilize the animal and holds great promise in improving the image quality and repeatability while reducing stress on experimental animals. The field flattener improved the signal-to-noise ratio (SNR) of the image at a cost of raised maximum exposure to some regions of the subject and reduced anatomical information in the images. In animal imaging applications, this method holds great promise to visualize low concentrations of contrast agents. Another cylindrical BLM was made to perform the novel Near Edge Spectral Imaging (NESI) and NESI CT. It showed high sensitivity for iodine with a measured detection limit of 2 microgram/centimetre square and a slightly better SNR performance than conventional KES imaging. The overwhelming impact of NESI is that it will bring together contrast imaging and elemental speciation imaging through X-ray Absorption Near Edge Structure (XANES) and Extended X-ray Absorption Fine Structure (EXAFS) analysis which have been totally different realms

Context-sensitive imaging for single, dual and multi energy computed tomography

In clinical routine, a case-adapted CT examination is usually conducted for each medical indication in order to allow for a comprehensive high-quality diagnosis of a patient. Therefore, image reading requires the transition between various image stacks, since each medical question implicitly requires organ-dependent reconstructions, display settings, multi planar reformations and image analysis tools. In particular, if dual or multi energy CT data are available, various spectral evaluation methods yield material-specific or functional information. However, the interpretation of this large amount of data is a time-consuming and tedious task. Hence, the purpose of this thesis is to evaluate the potential benefit of the incorporation of patient-specific anatomical priors, which are gained from an automatic multi-organ segmentation, in order to discover novel opportunities to improve the clinical workflow. In this thesis, a new paradigm is proposed which combines competing image properties resulting from different reconstruction algorithms and display settings into a context-sensitive CT imaging by means of anatomical prior information. With the incorporation of anatomical prior knowledge, which is obtained using an automatic multi-organ segmentation approach, various desired image characteristics are combined into a single context-sensitive CT image formation and presentation. The comparison with conventional CT images reveals an improved spatial resolution in highly attenuating materials as well as in air-filled body regions. Simultaneously, the compound image maintains a low noise level in soft tissue resulting in a superior soft tissue contrast compared to conventional images. Furthermore, the novel CT imaging framework allows for the combination of mutually exclusive display settings for the presentation of context-sensitive images to the radiologists. By exploiting anatomical prior information, numerous DECT applications can be integrated into one single DE analysis tool. Moreover, the tools can be chosen and applied to different organs simultaneously without any user interaction. The prior-based DE scheme performs all organ-specific feasible methods instantaneously without the need of a manual selection. Exploiting the anatomical priors, DECT analysis and evaluations are automated and standardized. The iodine quantification accuracy is significantly improved using patient-specific calibrations. The evaluation method and the presentation of the data to the radiologist can be realized via color overlays, pop up menus, volume rendering etc. Furthermore, the method can readily be generalized to the cases of multi energy CT data as it is not limited to the processing of DECT data. The principle of incorporation anatomical prior knowledge is then extended to provide a novel pseudo material decomposition that decomposes dual energy data into more than three basis materials. The method consists of multiple three-material decompositions, where the basis materials are automatically adjusted to the organ of interest based on the automatic segmentation. Moreover, a patient-specific calibration is introduced to improve the volume fraction and material quantification accuracy. An organ-adapted basis material triplet is automatically assigned to each anatomical region resulting in overlapping triangles in the dual energy space. The basis materials are calibrated by evaluating ROIs to improve the volume fraction accuracy. Besides presenting evermore increasing material images to the radiologists, the volume fractions are rescaled to organ-dependent material scores and visualized via pie charts to be later correlated with different diagnoses. The prior-based pseudo multi material decomposition is evaluated using phantom and patient data. The materials are quantified according to the anatomical structure they belong to. Overall, the proposed method provides physically plausible volume fractions that bear the potential to improve the material quantification for diagnosis and e.g. tumor treatment monitoring. In addition, the iodine quantification accuracy and the volume fraction accuracy are evaluated depending on different material calibration methods in conventional DECT applications as well as in the novel pseudo multi material decomposition. The accuracy using default parameters or simulation-based calibrations is compared against the accuracy obtained using patient-specific ROIs. All patient-specific calibrations can be performed directly from the patient data itself, such that almost no user interaction is required. It turns out that a patient-specific calibration is superior compared to a default or simulation based calibration. The new paradigm offers the possibility to display evermore complex information in CT imaging in order to significantly improve the workflow of radiologists. In the clinical routine, e.g. during case presentations and discussions, the fast switching between different image stacks is time-consuming and can be avoided in the future since the CS images merge advantageous image properties resulting from various reconstructions and display settings. The results of the DE evaluation can be dynamically superimposed by color overlays. This superposition provides a comprehensive quantitative analysis of the patient data that can be interpreted as an additional image dimension. By means of the combined DECT evaluation scheme, the radiologists might be assisted in finding a precise diagnosis. In summary, diagnostic accuracy could be increased with the CS imaging by improving the sensitivity for incidental findings: e.g. small nodules can be diagnosed in the lung parenchyma, even if the radiologist is mainly focused on assessing soft tissue. The possibility to robustly decompose DECT data into more than three basis materials opens up for novel clinical evaluation to quantify e.g. fat content and iodine content in the liver simultaneously and to assess long term material scores using pie chart visualizations