From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological imaging, digital pathology, molecular imaging, microscopy, and associated computational imaging, image analysis, and image-guided treatment, alongside physiological signal processing, computational biology, and bioinformatics. BISP has 40 members and covers a wide range of EDICS, including CIS-MI: Medical Imaging, BIO-MIA: Medical Image Analysis, BIO-BI: Biological Imaging, BIO: Biomedical Signal Processing, BIO-BCI: Brain/Human-Computer Interfaces, and BIO-INFR: Bioinformatics. BISP plays a central role in the organization of the IEEE International Symposium on Biomedical Imaging (ISBI) and contributes to the technical sessions at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), and the IEEE International Conference on Image Processing (ICIP). In this paper, we provide a brief history of the TC, review the technological and methodological contributions its community delivered, and highlight promising new directions we anticipate

Development and Performance Evaluation of High Resolution TOF-PET Detectors Suitable for Novel PET Scanners

Tesis por compendio[ES] La Tomografía por Emisión de Positrones (PET) es una de las técnicas más importantes en la medicina de diagnóstico actual y la más representativa en el campo de la Imagen Molecular. Esta modalidad de imagen es capaz de producir información funcional única, que permite la visualización en detalle, cuantificación y conocimiento de una variedad de enfermedades y patologías. Áreas como la oncología, neurología o la cardiología, entre otras, se han beneficiado en gran medida de esta técnica. A pesar de que un elevado número de avances han ocurrido durante el desarrollo del PET, existen otros que son de gran interés para futuras investigaciones. Uno de los principales pilares actualmente en PET, tanto en investigación como en desarrollo, es la obtención de la información del tiempo de vuelo (TOF) de los rayos gamma detectados. Cuando esto ocurre, aumenta la sensibilidad efectiva del PET, mejorando la calidad señal-ruido de las imágenes. Sin embargo, la obtención precisa de la marca temporal de los rayos gamma es un reto que requiere, además de técnicas y métodos específicos, compromisos entre coste y rendimiento. Una de las características que siempre se ve afectada es la resolución espacial. Como discutiremos, la resolución espacial está directamente relacionada con el tipo de centellador y, por lo tanto, con el coste del sistema y su complejidad. En esta tesis, motivada por los conocidos beneficios en imagen clínica de una medida precisa del tiempo y de la posición de los rayos gamma, proponemos configuraciones de detectores TOF- PET novedosos capaces de proveer de ambas características. Sugerimos el uso de lo que se conoce como métodos de "light-sharing", tanto basado en cristales monolíticos como pixelados de tamaño diferente al del fotosensor. Estas propuestas hacen que la resolución espacial sea muy alta. Sin embargo, sus capacidades temporales han sido muy poco abordadas hasta ahora. En esta tesis, a través de varios artículos revisados, pretendemos mostrar los retos encontrados en esta dirección, proponer determinadas configuraciones y, además, indagar en los límites temporales de éstas. Hemos puesto un gran énfasis en estudiar y analizar las distribuciones de la luz centellante, así como su impacto en la determinación temporal. Hasta nuestro conocimiento, este es el primer trabajo en el que se estudia la relación de la determinación temporal y la distribución de luz de centelleo, en particular usando SiPM analógicos y ASICs. Esperamos que esta tesis motive y permita otros muchos trabajos orientados en nuevos diseños, útiles para instrumentación PET, así como referencia para otros trabajos. Esta tesis esta organizada como se describe a continuación. Hay una introducción compuesta por tres capítulos donde se resumen los conocimientos sobre imagen PET, y especialmente aquellos relacionados con la técnica TOF-PET. Algunos trabajos recientes, pero aún no publicados se muestran también, con el objetivo de corroborar ciertas ideas. En la segunda parte se incluyen las cuatro contribuciones que el candidato sugiere para el compendio de artículos.[CA] La Tomografia per Emissió de Positrons (PET) és una de les tècniques més importants en la medicina de diagnòstic actual i la més representativa en el camp de la Imatge Molecular. Esta modalitat d'imatge és capaç de produir informació funcional única, que permet la visualització en detall, quantificació i coneixement d'una varietat de malalties i patologies. Àrees com l'oncologia, neurologia o la cardiologia, entre altres, s'han beneficiat en gran manera d'aquesta tècnica. Tot i que un elevat nombre d'avanços han ocorregut durant el desenvolupament del PET, hi ha altres que són de gran interés per a futures investigacions. Un dels principals pilars actuals en PET, tant en investigació com en desenvolupament, és l'obtenció de la informació del temps de vol (TOF en anglès) dels raigs gamma detectats. Quan açò ocorre, augmenta la sensibilitat efectiva del PET, millorant la qualitat senyal-soroll de les imatges. No obstant això, l'obtenció precisa de la marca temporal dels raigs gamma és un repte que requerix, a més de tècniques i mètodes específics, compromisos entre cost i rendiment. Una de les característiques que sempre es veu afectada és la resolució espacial. Com discutirem, la resolució espacial està directament relacionada amb el tipus de centellador, i per tant, amb el cost del sistema i la seua complexitat. En aquesta tesi, motivada pels coneguts beneficis en imatge clínica d'una mesura precisa del temps i de la posició dels raigs gamma, proposem nouves configuracions de detectors TOF-PET capaços de proveir d'ambduess característiques. Suggerim l'ús del que es coneix com a mètodes de "light-sharing", tant basat en cristalls monolítics com pixelats de diferent tamany del fotosensor. Aquestes propostes fan que la resolució espacial siga molt alta. No obstant això, les seues capacitats temporals han sigut molt poc abordades fins ara. En aquesta tesi, a través de diversos articles revisats, pretenem mostrar els reptes trobats en aquesta direcció, proposar determinades configuracions i, a més, indagar en els límits temporals d'aquestes. Hem posat un gran èmfasi a estudiar i analitzar les distribucions de la llum centellejant, així com el seu impacte en la determinació temporal. Fins al nostre coneixement, aquest és el primer treball en què s'estudia la relació de la determinació temporal i la distribució de llum de centelleig, en particular utilitzant SiPM analògics i ASICs. Esperem que aquesta tesi motive i permeta molts altres treballs orientats en nous dissenys, útils per a instrumentació PET, així com referència per a altres treballs. Aquesta tesi esta organitzada com es descriu a continuació. Hi ha una introducció composta per tres capítols on es resumeixen els coneixements sobre imatge PET i, especialmente, aquells relacionats amb la tècnica TOF-PET. Alguns treballs recents, però encara no publicats es mostren també, amb l'objectiu de corroborar certes idees. La segona part de la tesi conté els quatre articles revisats que el candidat suggereix.[EN] Positron Emission Tomography (PET) is one of the greatest tools of modern diagnostic medicine and the most representative in the field of molecular imaging. This imaging modality, is capable of providing a unique type of functional information which permits a deep visualization, quantification and understanding of a variety of diseases and pathologies. Areas like oncology, neurology, or cardiology, among others, have been well benefited by this technique. Although numerous important advances have already been achieved in PET, some other individual aspects still seem to have a great potential for further investigation. One of the main trends in modern PET research and development, is based in the extrapolation of the Time- Of-Flight (TOF) information from the gamma-ray detectors. In such case, an increase in the effective sensitivity of PET is accomplished, resulting in an improved image signal-to-noise ratio. However, the direction towards a precise decoding of the photons time arrival is a challenging task that requires, besides specific approaches and techniques, tradeoffs between cost and performance. A performance characteristic very habitually compromised in TOF-PET detector configurations is the spatial resolution. As it will be discussed, this feature is directly related to the scintillation materials and types, and consequently, with system cost and complexity. In this thesis, motivated by the well-known benefits in clinical imaging of a precise time and spatial resolution, we propose novel TOF-PET detector configurations capable of inferring both characteristics. Our suggestions are based in light sharing approaches, either using monolithic detectors or crystal arrays with different pixel-to-photosensor sizes. These approaches, make it possible to reach a precise impact position determination. However, their TOF capabilities have not yet been explored in depth. In the present thesis, through a series of peer-reviewed publications we attempt to demonstrate the challenges encountered in these kinds of configurations, propose specific approaches improving their performance and eventually reveal their limits in terms of timing. High emphasis is given in analyzing and studying the scintillation light distributions and their impact to the timing determination. To the best of our knowledge, this is one of the first works in which such detailed study of the relation between light distribution and timing capabilities is carried out, especially when using analog SiPMs and ASICs. Hopefully, this thesis will motivate and enable many other novel design concepts, useful in PET instrumentation as well as it will serve as a helpful reference for similar attempts. The present PhD thesis is organized as follows. There is an introduction part composed by three detailed sections. We attempt to summarize here some of the knowledge related to PET imaging and especially with the technique of TOF-PET. Some very recent but still unpublished results are also presented and included in this part, aiming to support statements and theories. The second part of this thesis lists the four peer-reviewed papers that the candidate is including.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 695536). It has also been supported by the Spanish Ministerio de Economía, Industria y Competitividad under Grants No. FIS2014-62341-EXP and TEC2016-79884-C2-1-R. Efthymios Lamprou has also been supported by Generalitat Valenciana under grant agreement GRISOLIAP-2018-026.Lamprou, E. (2021). Development and Performance Evaluation of High Resolution TOF-PET Detectors Suitable for Novel PET Scanners [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/162991TESISCompendi

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine

3D Deep Learning on Medical Images: A Review

The rapid advancements in machine learning, graphics processing technologies and availability of medical imaging data has led to a rapid increase in use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, give a brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection, and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models, in general) and possible future trends in the field.Comment: 13 pages, 4 figures, 2 table

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Evaluation of the region-specific risks of accidental radioactive releases from the European Spallation Source

The European Spallation Source (ESS) is a neutron research facility under construction in southern Sweden. The facility will produce a wide range ofradionuclides that could be released into the environment. Some radionuclides are of particular concern such as the rare earth gadolinium-148. In this article, the local environment was investigated in terms of food production and rare earth element concentration in soil. The collected data will later be used to model thetransfer of radioactive contaminations from the ESS