This paper presents a review of automated image registration methodologies that have been used in the medical field. The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application. The registration methodologies under review are classified into intensity or feature based. The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described.

Image registration has been used to support pixel-level data analysis on pedobarographic image data sets. Some registration methods have focused on robustness and sacrificed speed, but a recent approach based on external contours offered both high computational processing speed and high accuracy. However, since contours can be influenced by local perturbations, we sought more global methods. Thus, we propose two new registration methods based on the Fourier transform, cross-correlation and phase correlation which offer high computational speed. We found out that both proposed methods revealed high accuracy for the similarity measures considered, using control geometric transformations. Additionally, both methods revealed high computational processing speed which, combined with their accuracy and robustness, allows their implementation in near-real-time applications. Furthermore, we found that the current methods were robust to moderate levels of noise, and consequently, do not require noise removal procedure like the contours method does.

The length of the striatal region uptake is clinically useful and highly valuable to confirm dopaminergic degeneration "in vivo" as an aid to the diagnosis of Parkinson's disease. It compares fairly well to the standard uptake ratio-based features, reaching, at least, similar accuracies and is easier to obtain automatically. Thus, we propose its day to day clinical use, jointly with the uptake ratio-based features, in the computer-aided diagnosis of dopaminergic degeneration in Parkinson's disease.

The understanding of the natural history of Alzheimer’s disease (AD) and temporal trajectories of in vivo molecular mechanisms requires longitudinal approaches. A behavioral and multimodal imaging study was performed at 4/8/12 and 16 months of age in a triple transgenic mouse model of AD (3xTg-AD). Behavioral assessment included the open field and novel object recognition tests. Molecular characterization evaluated hippocampal levels of amyloid (A ) and hyperphosphorylated tau. Magnetic resonance imaging (MRI) included assessment of hippocampal structural integrity, blood–brain barrier (BBB) permeability and neurospectroscopy to determine levels of the endogenous neuroprotector taurine. Longitudinal brain amyloid accumulation was assessed using 11 C Pittsburgh compound B positron emission tomography (PET), and neuroinflammation/microglia activation was investigated using 11 C-PK1195. We found altered locomotor activity at months 4/8 and 16 months and recognition memory impairment at all time points. Substantial early reduction of hippocampal volume started at month 4 and progressed over 8/12 and 16 months. Hippocampal taurine levels were significantly decreased in the hippocampus at months 4/8 and 16. No differences were found for amyloid and neuroinflammation with PET, and BBB was disrupted only at month 16. In summary, 3xTg-AD mice showed exploratory and recognition memory impairments, early hippocampal structural loss, increased A and hyperphosphorylated tau and decreased levels of taurine. In sum, the 3xTg-AD animal model mimics pathological and neurobehavioral features of AD, with early-onset recognition memory loss and MRI-documented hippocampal damage. The early-onset profile suggests temporal windows and opportunities for therapeutic intervention, targeting endogenous neuroprotectors such as taurine.

Currently, Nuclear Medicine has a clearly defined role in clinical practice due to its usefulness in many medical disciplines. It provides relevant diagnostic and therapeutic options leading to patients' healthcare and quality of life improvement. During the first two decades of the 21stt century, the number of Nuclear Medicine procedures increased considerably. Clinical and research advances in Nuclear Medicine and Molecular Imaging have been based on developments in radiopharmaceuticals and equipment, namely, the introduction of multimodality imaging. In addition, new therapeutic applications of radiopharmaceuticals, mainly in oncology, are underway. This review will focus on radiopharmaceuticals for positron emission tomography (PET), in particular, those labeled with Fluorine-18 and Gallium-68. Multimodality as a key player in clinical practice led to the development of new detector technology and combined efforts to improve resolution. The concept of dual probe (a single molecule labeled with a radionuclide for single photon emission computed tomography)/positron emission tomography and a light emitter for optical imaging) is gaining increasing acceptance, especially in minimally invasive radioguided surgery. The expansion of theranostics, using the same molecule for diagnosis (γ or positron emitter) and therapy (β minus or α emitter) is reshaping personalized medicine. Upcoming research and development efforts will lead to an even wider array of indications for Nuclear Medicine both in diagnosis and treatment.

The achieved classification accuracy was very high and, in fact, higher than accuracies found in previous studies reported in the literature. In addition, results were obtained on a large dataset of early Parkinson's disease subjects. In summation, the information provided by the developed computational solution potentially supports clinical decision-making in nuclear medicine, using important additional information beyond the commonly used uptake ratios and respective statistical comparisons. (ClinicalTrials.gov Identifier: NCT01141023).

This article presents a framework to register (or align) plantar pressure images based on a hybrid registration approach, which first establishes an initial registration that is subsequently improved by the optimization of a selected image (dis)similarity measure. The initial registration has two different solutions: one based on image contour matching and the other on image cross-correlation. In the final registration, a multidimensional optimization algorithm is applied to one of the following (dis)similarity measures: the mean squared error (MSE), the mutual information, and the exclusive or (XOR). The framework has been applied to intra- and inter-subject registration. In the former, the framework has proven to be extremely accurate and fast (<70 ms on a normal PC notebook), and obtained superior XOR and identical MSE values compared to the best values reported in previous studies. Regarding the inter-subject registration, by using rigid, similarity, affine, projective, and polynomial (up to the fourth degree) transformations, the framework significantly optimized the image (dis)similarity measures. Thus, it is considered to be very accurate, fast, and robust in terms of noise, as well as being extremely versatile, all of which are regarded as essential features for near-real-time applications.

Image registration, the process of optimally aligning homologous structures in multiple images, has recently been demonstrated to support automated pixel-level analysis of pedobarographic images and, subsequently, to extract unique and biomechanically relevant information from plantar pressure data. Recent registration methods have focused on robustness, with slow but globally powerful algorithms. In this paper, we present an alternative registration approach that affords both speed and accuracy, with the goal of making pedobarographic image registration more practical for near-real-time laboratory and clinical applications. The current algorithm first extracts centroid-based curvature trajectories from pressure image contours, and then optimally matches these curvature profiles using optimization based on dynamic programming. Special cases of disconnected images (that occur in high-arched subjects, for example) are dealt with by introducing an artificial spatially linear bridge between adjacent image clusters. Two registration algorithms were developed: a 'geometric' algorithm, which exclusively matched geometry, and a 'hybrid' algorithm, which performed subsequent pseudo-optimization. After testing the two algorithms on 30 control image pairs considered in a previous study, we found that, when compared with previously published results, the hybrid algorithm improved overlap ratio ( 010 . 0 = p ), but both current algorithms had slightly higher mean-squared error, assumedly because they did not consider pixel intensity. Nonetheless, both algorithms greatly improved the computational efficiency ( 8 25 ± ms and 9 53 ± ms per image pair for geometric and hybrid registrations, respectively). These results imply that registration-based pixellevel pressure image analyses can, eventually, be implemented for practical clinical purposes.