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Spatially registered positron emission tomography (PET), computed tomography (CT), and magnetic resonance (MR) images of the same small animal offer potential advantages over PET alone: CT images should allow accurate, nearly noise-free correction of the PET image data for attenuation; the CT or MR images should permit more certain identification of structures evident in the PET images; and CT images provide a priori anatomical information that may be of use with resolution-improving image-reconstruction algorithms that model the PET imaging process. However, image registration algorithms effective in human studies have not been characterized in the small-animal setting. Accordingly, we evaluated the ability of the automated image registration (AIR) and mutual information (MI) algorithms to register PET images of the rat skull and brain to CT or MR images of the same animal. External fiducial marks visible in all three modalities were used to estimate residual errors after registration. The AIR algorithm registered PET bone-to-CT bone images with a maximum error of less than 1.0 mm. The registration errors for PET brain-to-CT brain images, however, were greater, and considerable user intervention was required prior to registration. The AIR algorithm either failed or required excessive user intervention to register PET and MR brain images. In contrast, the MI algorithm yielded smaller registration errors in all scenarios with little user intervention. The MI algorithm thus appears to be a more robust method for registering PET, CT, and MR images of the rat head. Index Terms--Image reconstruction, positron emission tomography (PET), small animal imaging.

Emphysema quantification techniques rely on the use of CT scans, but they are rarely used in the diagnosis and management of patients with COPD; X-ray films are the preferred method to do this. However, this diagnosis method is very controversial, as there are not established guidelines to define the disease, sensitivity is low, and quantification cannot be done. We developed a quantification method based on a CNN, capable of predicting the emphysema percentage of a patient based on an X-ray image. We used real CT scans to simulate X-ray films and to calculate emphysema percentage using the LAA%. The model developed was able to calculate emphysema percentage with an LAA% mean error of 3.96, and it obtained an AUC accuracy of 90.73% for an emphysema definition of ≥10%, with a mean sensitivity of 85.68%, significantly improving X-ray-based emphysema diagnosis.

Advanced liver surgery requires a precise pre-operative planning, where liver segmentation and remnant liver volume are key elements to avoid post-operative liver failure. In that context, level-set algorithms have achieved better results than others, especially with altered liver parenchyma or in cases with previous surgery. In order to improve functional liver parenchyma volume measurements, in this work we propose two strategies to enhance previous level-set algorithms: an optimal multi-resolution strategy with fine details correction and adaptive curvature, as well as an additional semiautomatic step imposing local curvature constraints. Results show more accurate segmentations, especially in elongated structures, detecting internal lesions and avoiding leakages to close structures.

In this work we propose an active surface method to segment complete liver volumes from preoperative CT abdominal images. The method finds the surface that minimizes an energy function combining intensity inside and outside the surface, gradient information and curvature restrictions. The implementation is based on a level set technique following a multi-resolution strategy to reduce computing time. It requires only a single seed point inside the liver to initialize the active surface. The algorithm has been validated on a set of previously diagnosed livers. Resulting segmentations have been supervised by clinicians and radiologists, and numerically evaluated in terms of volume measurements with respect to those obtained from radiologists' manual segmentations. Additionally, radiologists analyzed the necessity of additional corrections on segmenting volumes.