Quantitative MRI correlates of hippocampal and neocortical pathology in intractable temporal lobe epilepsy

Intractable or drug-resistant epilepsy occurs in over 30% of epilepsy patients, with many of these patients undergoing surgical excision of the affected brain region to achieve seizure control. Advances in MRI have the potential to improve surgical treatment of epilepsy through improved identification and delineation of lesions. However, validation is currently needed to investigate histopathological correlates of these new imaging techniques. The purpose of this work is to investigate histopathological correlates of quantitative relaxometry and DTI from hippocampal and neocortical specimens of intractable TLE patients. To achieve this goal I developed and evaluated a pipeline for histology to in-vivo MRI image registration, which finds dense spatial correspondence between both modalities. This protocol was divided in two steps whereby sparsely sectioned histology from temporal lobe specimens was first registered to the intermediate ex-vivo MRI which is then registered to the in-vivo MRI, completing a pipeline for histology to in-vivo MRI registration. When correlating relaxometry and DTI with neuronal density and morphology in the temporal lobe neocortex, I found T1 to be a predictor of neuronal density in the neocortical GM and demonstrated that employing multi-parametric MRI (combining T1 and FA together) provided a significantly better fit than each parameter alone in predicting density of neurons. This work was the first to relate in-vivo T1 and FA values to the proportion of neurons in GM. When investigating these quantitative multimodal parameters with histological features within the hippocampal subfields, I demonstrated that MD correlates with neuronal density and size, and can act as a marker for neuron integrity within the hippocampus. More importantly, this work was the first to highlight the potential of subfield relaxometry and diffusion parameters (mainly T2 and MD) as well as volumetry in predicting the extent of cell loss per subfield pre-operatively, with a precision so far unachievable. These results suggest that high-resolution quantitative MRI sequences could impact clinical practice for pre-operative evaluation and prediction of surgical outcomes of intractable epilepsy

Registration of pre-operative lung cancer PET/CT scans with post-operative histopathology images

Non-invasive imaging modalities used in the diagnosis of lung cancer, such as Positron Emission Tomography (PET) or Computed Tomography (CT), currently provide insuffcient information about the cellular make-up of the lesion microenvironment, unless they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and its surroundings.;Initial experiments were performed on tissue-mimicking phantoms, to test different shape reconstruction methods. The choice of interpolator and slice thickness were found to affect the algorithm's output, in terms of overall volume and local feature recovery. In the second phase of the study, nine lung cancer patients referred for radical lobectomy were recruited. Resected specimens were inflated with agar, sliced at 5 mm intervals, and each cross-section was photographed. The tumour area was delineated on the block-face pathology images and on the preoperative PET/CT scans.;Airway segments were also added to the reconstructed models, to act as anatomical fiducials. Binary shapes were pre-registered by aligning their minimal bounding box axes, and subsequently transformed using rigid registration. In addition, histopathology slides were matched to the block-face photographs using moving least squares algorithm.;A two-step validation process was used to evaluate the performance of the proposed method against manual registration carried out by experienced consultants. In two out of three cases, experts rated the results generated by the algorithm as the best output, suggesting that the developed framework outperforms the current standard practice.Non-invasive imaging modalities used in the diagnosis of lung cancer, such as Positron Emission Tomography (PET) or Computed Tomography (CT), currently provide insuffcient information about the cellular make-up of the lesion microenvironment, unless they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and its surroundings.;Initial experiments were performed on tissue-mimicking phantoms, to test different shape reconstruction methods. The choice of interpolator and slice thickness were found to affect the algorithm's output, in terms of overall volume and local feature recovery. In the second phase of the study, nine lung cancer patients referred for radical lobectomy were recruited. Resected specimens were inflated with agar, sliced at 5 mm intervals, and each cross-section was photographed. The tumour area was delineated on the block-face pathology images and on the preoperative PET/CT scans.;Airway segments were also added to the reconstructed models, to act as anatomical fiducials. Binary shapes were pre-registered by aligning their minimal bounding box axes, and subsequently transformed using rigid registration. In addition, histopathology slides were matched to the block-face photographs using moving least squares algorithm.;A two-step validation process was used to evaluate the performance of the proposed method against manual registration carried out by experienced consultants. In two out of three cases, experts rated the results generated by the algorithm as the best output, suggesting that the developed framework outperforms the current standard practice

Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities

The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies. Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes. Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics. Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities. Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system

Micro- and nanoanatomy of human brain tissues

“Better to see something once than to hear about it a thousand times.” Proverb The human brain is one of the most complex organs in the body, containing billions of neurons of hundreds of types. To understand its properties and functionality at the most fundamental level, one must reveal and describe its structure down to the(sub-)cellular level. In general, three-dimensional (3D) characterisation of physically soft tissues is a challenge. Thus, the possibility of performing non-destructive label-free 3D imaging with the reasonable sensitivity, resolution and increased manageable specimen sizes, especially within the laboratory environment, is of great interest. The focus of the thesis relies on the non-destructive 3D investigation of the micro and nanoanatomy of human brain tissues. The ambitious challenge faced was to bridge the performance gap between the tomography data from laboratory systems, histological approaches employed by anatomists and pathologists, and synchrotron radiation-based tomography, by taking advantage of recent developments in X-ray tomographic imaging. The main reached milestones of the project include (i) visualisation of individual Purkinje cells in a label-free manner by laboratory-based absorption-contrast micro computed tomography (LBμCT), (ii) incorporation of the double-grating interferometer into the nanotom® m (GE Sensing & Inspection Technologies GmbH, Wunstorf, Germany) for phase-contrast imaging and (iii) visualisation and quantification of sub-cellular structures using nano-holotomography (nano-imaging beamline ID16A-NI, European ynchrotron Radiation Facility (ESRF), Grenoble, France). Hard X-ray micro computed tomography (μCT) in the absorption-contrast mode is well-established for hard tissue visualisation. However, performance in relation to lower density materials, such as post mortem brain tissues, is questionable, as attenuation differences between anatomical features are weak. It was demonstrated, through the example of a formalin-fixed paraffin-embedded (FFPE) human cerebellum, that absorption-contrast laboratory-based micro computed tomography can provide premium contrast images, complementary to hematoxylin and eosin (H&E) stained histological sections. Based on our knowledge, the detection of individual Purkinje cells without a dedicated contrast agent is unique in the field of absorption-contrast laboratory-based micro computed tomography. As the intensity of H&E staining of histological sections and the attenuation contrast of LBμCT data demonstrated a correlation, pseudo colouring of tomography data according to the H&E stain can be performed, virtually extending two-dimensional (2D) histology into the third dimension. The LBμCT of FFPE samples can be understood as a time-efficient and reliable tissue visualisation methodology, and so it could become a method of choice for imaging of relatively large specimens within the laboratory environment. Comparing the data acquired at the LBμCT system nanotom® m and synchrotron radiation facilities (Diamond-Manchester Imaging Branchline I13-2, Diamond Light Source, Didcot, UK and Microtomography beamline ID19, ESRF), it was demonstrated that all selected modalities, namely LBμCT, synchrotron radiation-based in line phase-contrast tomography using single-distance phase reconstruction and synchrotron radiation-based grating interferometry, can reach cellular resolution. As phase contrast yields better data quality for soft tissues, and in order to overcome the restrictions of limited beamtime access for phase-contrast measurements,a commercially available advanced μCT system nanotom® m was equipped with an X-ray double-grating interferometer (XDGI). The successful performance of the interferometer in the tomography mode was demonstrated on a human knee joint sample. XDGI provided enough contrast (1.094 ± 0.152) and spatial resolution (73 ± 6) μm to identify the cartilage layer, which is not recognised in the absorption mode without staining. These results suggest that the extension of a commercially available absorption-contrast μCT system via grating interferometry offers the potential to fill the performance gap between LBμCT and phase-contrast μCT using synchrotron radiation in the visualising soft tissues. Although optical microscopy of stained tissue sections enables the quantification of neuron morphology within brain tissues in health and disease, the lateral spatial resolution of histological sections is limited to the wavelength of visible light, while the orthogonal resolution is usually restricted to the section´s thickness. Based on the data acquired from the ID16A-NI, the study demonstrated the application of hard X-ray nano-holotomography with isotropic voxels down to 25 nm for the three dimensional visualising the human cerebellum and neocortex. The images exhibit a reasonable contrast to noise ratio and a spatial resolution of at least 84 nm. Therefore, the three dimensional data resembles the surface images obtained by electron microscopy (EM), but in this case electron dense staining is avoided. The (sub-)cellular structures within the Purkinje, granule, stellate and pyramidal cells of the FFPE tissue blocks were resolved and segmented. Micrometre spatial resolution is routinely achieved at synchrotron radiation facilities worldwide, while reaching the isotropic 100-nm barrier for soft tissues without applying any dedicated contrast agent, labelling or tissue-transformation is a challenge that could set a new standard in non-destructive 3D imaging

Methods and algorithms for quantitative analysis of metallomic images to assess traumatic brain injury

The primary aim of this thesis is to develop image processing algorithms to quantitatively determine the link between traumatic brain injury (TBI) severity and chronic traumatic encephalopathy (CTE) neuropathology, specifically looking into the role of blood-brain barrier disruption following TBI. In order to causally investigate the relationship between the tau protein neurodegenerative disease CTE and TBI, mouse models of blast neurotrauma (BNT) and impact neurotrauma (INT) are investigated. First, a high-speed video tracking algorithm is developed based on K-means clustering, active contours and Kalman filtering to comparatively study the head kinematics in blast and impact experiments. Then, to compare BNT and INT neuropathology, methods for quantitative analysis of macroscopic optical images and fluorescent images are described. The secondary aim of this thesis focuses on developing methods for a novel application of metallomic imaging mass spectrometry (MIMS) to biological tissue. Unlike traditional modalities used to assess neuropathology, that suffer from limited sensitivity and analytical capacity, MIMS uses a mass spectrometer -- an analytical instrument for measuring elements and isotopes with high dynamic range, sensitivity and specificity -- as the imaging sensor to generate spatial maps with spectral (vector-valued) data per pixel. Given the vector nature of MIMS data, a unique end-to-end processing pipeline is designed to support data acquisition, visualization and interpretation. A novel multi-modal and multi-channel image registration (MMMCIR) method using multi-variate mutual information as a similarity metric is developed in order to establish correspondence between two images of arbitrary modality. The MMMCIR method is then used to automatically segment MIMS images of the mouse brain and systematically evaluate the levels of relevant elements and isotopes after experimental closed-head impact injury on the impact side (ipsilateral) and opposing side (contralateral) of the brain. This method quantifiably confirms observed differences in gadolinium levels for a cohort of images. Finally, MIMS images of human lacrimal sac biopsy samples are used for preliminary clinicopathological assessments, supporting the utility of the unique insights MIMS provides by correlating areas of inflammation to areas of elevated toxic metals. The image processing methods developed in this work demonstrate the significant capabilities of MIMS and its role in enhancing our understanding of the underlying pathological mechanisms of TBI and other medical conditions.2019-07-09T00:00:00

Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems

Fúze obrazu je dnes jednou z nejběžnějších avšak stále velmi diskutovanou oblastí v lékařském zobrazování a hraje důležitou roli ve všech oblastech lékařské péče jako je diagnóza, léčba a chirurgie. V této dizertační práci jsou představeny tři projekty, které jsou velmi úzce spojeny s oblastí fúze medicínských dat. První projekt pojednává o 3D CT subtrakční angiografii dolních končetin. V práci je využito kombinace kontrastních a nekontrastních dat pro získání kompletního cévního stromu. Druhý projekt se zabývá fúzí DTI a T1 váhovaných MRI dat mozku. Cílem tohoto projektu je zkombinovat stukturální a funkční informace, které umožňují zlepšit znalosti konektivity v mozkové tkáni. Třetí projekt se zabývá metastázemi v CT časových datech páteře. Tento projekt je zaměřen na studium vývoje metastáz uvnitř obratlů ve fúzované časové řadě snímků. Tato dizertační práce představuje novou metodologii pro klasifikaci těchto metastáz. Všechny projekty zmíněné v této dizertační práci byly řešeny v rámci pracovní skupiny zabývající se analýzou lékařských dat, kterou vedl pan Prof. Jiří Jan. Tato dizertační práce obsahuje registrační část prvního a klasifikační část třetího projektu. Druhý projekt je představen kompletně. Další část prvního a třetího projektu, obsahující specifické předzpracování dat, jsou obsaženy v disertační práci mého kolegy Ing. Romana Petera.Image fusion is one of today´s most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. Jiří Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population