Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

Medical Diagnosis with Multimodal Image Fusion Techniques

Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and  the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications

Statistical Sinogram Restoration in Dual-Energy CT for PET Attenuation Correction

Dual-energy (DE) X-ray computed tomography (CT) has been found useful in various applications. In medical imaging, one promising application is using low-dose DECT for attenuation correction in positron emission tomography (PET). Existing approaches to sinogram material decomposition ignore noise characteristics and are based on logarithmic transforms, producing noisy component sinogram estimates for low-dose DECT. In this paper, we propose two novel sinogram restoration methods based on statistical models: penalized weighted least square (PWLS) and penalized likelihood (PL), yielding less noisy component sinogram estimates for low-dose DECT than classical methods. The proposed methods consequently provide more precise attenuation correction of the PET emission images than do previous methods for sinogram material decomposition with DECT. We report simulations that compare the proposed techniques and existing approaches.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85900/1/Fessler11.pd

Multi-Isotope Multi-Pinhole SPECT Bildgebung in kleinen Labortieren: Experimentelle Messungen und Monte Carlo Simulationen

Single photon emission computed tomography (SPECT) in small laboratory animals has become an integral part of translational medicine. It enables non-invasive validation of drug targeting, safety and efficacy in living organisms, which is progressively gaining importance in pharmaceutical industry. The increasing demand for efficiency in pharmaceutical research could be addressed by novel multitracer study designs. Multi-isotope multi-pinhole sampling allows validation of multiple tracers in a single experiment and consolidation of consecutive research trials. Due to physical and technical limitations, however, image quality and quantification can be substantially reduced. Advanced corrective procedures are required to establish multi-isotope multi-pinhole SPECT as a reliable and quantitative imaging technique for widespread use. For this purpose, the present work aimed to investigate the technical capabilities and physical limitations of multi-isotope multi-pinhole SPECT imaging in small laboratory animals. Based on experimental measurements and Monte Carlo simulations, specific error sources have been identified and procedures for quantitative image correction have been developed. A Monte Carlo simulation model of a state-of-the art SPECT/CT system has been established to provide a generalized framework for in-silico optimization of imaging hardware, acquisition protocols and reconstruction algorithms. The findings of this work can be used to improve image quality and quantification of SPECT in-vivo data for multi-isotope applications. They guide through the laborious process of multi-isotope protocol optimization and support the 3R welfare initiative that aims to replace, reduce and refine animal experimentation.Die Einzelphotonen-Emissionscomputertomographie (SPECT) in kleinen Labortieren hat sich als wichtiger Bestandteil der translationalen Medizin etabliert. Sie ermöglicht die nicht-invasive Validierung der Zielgenauigkeit, Wirksamkeit und Sicherheit von Wirkstoffen in lebenden Organismen und gewinnt zunehmend an Bedeutung in der pharmazeutischen Industrie. Die Forderung nach mehr Effizienz in der pharmazeutischen Forschung könnte durch neuartige Multitracer-Studien adressiert werden. Die Multi-Isotopen Akquisition mit Multi-Pinhole Kollimatoren ermöglicht die Validierung mehrerer Tracer in einem einzelnen Experiment und die Konsolidierung konsekutiver Bildgebungsstudien. Aufgrund physikalischer und technischer Limitationen ist die Bildqualität und Quantifizierbarkeit bei diesem Verfahren jedoch häufig reduziert. Um die Multi-Isotopen SPECT als zuverlässige und quantitative Bildgebungsmethode für den breiten Einsatz zu etablieren sind komplexe Korrekturverfahren erforderlich. Ziel der vorliegenden Arbeit war daher, die technischen Möglichkeiten und physikalischen Limitationen der Multi-Isotopen SPECT-Bildgebung in kleinen Labortieren systematisch zu untersuchen. Mithilfe von experimentellen Messungen und Monte Carlo Simulationen wurden spezifische Fehlerquellen identifiziert und Verfahren zur quantitativen Bildkorrektur entwickelt. Zudem wurde das Monte-Carlo Modell eines neuartigen SPECT/CT-Systems etabliert, um eine Plattform für die in-silico Optimierung von Bildgebungshardware, Aufnahmeprotokollen und Rekonstruktionsalgorithmen zu schaffen. Die Ergebnisse dieser Arbeit können die Bildqualität und Quantifizierbarkeit von SPECT in-vivo Daten für Multi-Isotopen Anwendungen verbessern. Sie führen beispielhaft durch den Prozess der Multi-Isotopen Protokolloptimierung und unterstützen die 3R-Initiative mit dem Ziel, experimentelle Tierversuche zu vermeiden (Replace), zu vermindern (Reduce) und zu verbessern (Refine)

A role for artificial intelligence in molecular imaging of infection and inflammation

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [F-18]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment