Publications by authors named "Jose Gardiazabal"

Purpose: Hand- and robot-guided mini gamma cameras have been introduced for the acquisition of single-photon emission computed tomography (SPECT) images. Less cumbersome than whole-body scanners, they allow for a fast acquisition of the radioactivity distribution, for example, to differentiate cancerous from hormonally hyperactive lesions inside the thyroid. This work compares acquisition protocols and reconstruction algorithms in an attempt to identify the most suitable approach for fast acquisition and efficient image reconstruction, suitable for localization of extended sources, such as lesions inside the thyroid.

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The development of modern robotics and compact imaging detectors allows the transfer of diagnostic imaging modalities to the operating room, supporting surgeons to perform faster and safer procedures. An intervention that currently suffers from a lack of interventional imaging is radioembolization, a treatment for hepatic carcinoma. Currently, this procedure requires moving the patient from an angiography suite for preliminary catheterization and injection to a whole-body SPECT/CT for leakage detection, necessitating a second catheterization back in the angiography suite for the actual radioembolization.

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Nuclear imaging modalities like PET or SPECT are in extensive use in medical diagnostics. In a move towards personalized therapy, we present a flexible nuclear tomographic imaging system to enable intra-operative SPECT-like 3D imaging. The system consists of a miniaturized gamma camera mounted on a robot arm for flexible positioning, while spatio-temporal localization is provided by an optical tracking system.

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Different types of nuclear imaging systems have been used in the past, starting with pre-operative gantry-based SPECT systems and gamma cameras for 2D imaging of radioactive distributions. The main applications are concentrated on diagnostic imaging, since traditional SPECT systems and gamma cameras are bulky and heavy. With the development of compact gamma cameras with good resolution and high sensitivity, it is now possible to use them without a fixed imaging gantry.

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Electromagnetic (EM) tracking is highly relevant for many computer assisted interventions. This is in particular due to the fact that the scientific community has not yet developed a general solution for tracking of flexible instruments within the human body. Electromagnetic tracking solutions are highly attractive for minimally invasive procedures, since they do not require line of sight.

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Diagnostic nuclear imaging modalities like SPECT typically employ gantries to ensure a densely sampled geometry of detectors in order to keep the inverse problem of tomographic reconstruction as well-posed as possible. In an intra-operative setting with mobile freehand detectors the situation changes significantly, and having an optimal detector trajectory during acquisition becomes critical. In this paper we propose an incremental optimization method based on the numerical condition of the system matrix of the underlying iterative reconstruction method to calculate optimal detector positions during acquisition in real-time.

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Acquisition geometries for tomographic reconstruction are usually densely sampled in order to keep the underlying linear system used in iterative reconstruction as well-posed as possible. While this objective is easily enforced in imaging systems with gantries, this issue is more critical for intra-operative setups using freehand-guided data sensing. This paper investigates an incremental method to monitor the numerical condition of the system based on the singular value decomposition of the system matrix, and presents an approach to find optimal detector positions via a randomized optimization scheme.

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The reconstruction of histology sections into a 3-D volume receives increased attention due to its various applications in modern medical image analysis. To guarantee a geometrically coherent reconstruction, we propose a new way to register histological sections simuItaneously to previously acquired reference images and to neighboring slices in the stack. To this end, we formulate two potential functions and associate them to the same Markov random field through which we can efficiently find an optimal solution.

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