Mesh-to-raster based non-rigid registration of multi-modal images

Region of interest (ROI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from CAT scanners as pixel or voxel data. Previously, we presented a 2D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a 3D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2D using ground truth provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem where the objective consists of a data term, which involves the signed distance function of the ROI from the reference image, and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The ROI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit (ITK) and Elastix

Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Innovations in thoracic imaging:CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation

Image Reconstruction in Photoacoustic Computed Tomography with Acoustically Heterogeneous Media

Photoacoustic computed tomography (PACT), also known as optoacoustic or thermoacoustic tomography, is a rapidly emerging hybrid imaging modality that combines optical image contrast with ultrasound detection. The majority of currently available PACT image reconstruction algorithms are based on idealized imaging models that assume a lossless and acoustically homogeneous medium. However, in many applications of PACT these assumptions are violated and the induced photoacoustic (PA) wavefields are scattered and absorbed as they propagate to the receiving transducers. In those applications of PACT, the reconstructed images can contain significant distortions and artifacts if the inhomogeneous acoustic properties of the object are not accounted for in the reconstruction algorithm. In this dissertation, we develop and investigate a full-wave approach to iterative image reconstruction in PACT with acoustically heterogeneous lossy media. A key contribution of this work is the establishment of a discrete imaging model that is based on the exact PA wave equation and a procedure to implement an associated matched discrete forward and backprojection operator pair, which permits application of a variety of modern iterative image reconstruction algorithms that can mitigate the effects of noise, data incompleteness and model errors. Another key contribution is the development of an optimization approach to joint reconstruction (JR) of absorbed optical energy density and speed of sound in PACT, which is utilized to investigate the numerical properties of the JR problem and its feasibility in practice. We also develop a TR-based methodology to compensate for heterogeneous acoustic attenuation that obeys a frequency power law. In addition, we propose a image reconstruction methodology for transcranial PACT that employs detailed subject-specific descriptions of the acoustic properties of the skull to mitigate skull-induced distortions in the reconstructed image