Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems

Positron emission tomography (PET) imaging is based on detecting two time-coincident high-energy photons from the emission of a positronemitting radioisotope. The physics of the emission, and the detection of the coincident photons, give PET imaging unique capabilities for both very high sensitivity and accurate estimation of the in vivo concentration of the radiotracer. PET imaging has been widely adopted as an important clinical modality for oncological, cardiovascular, and neurological applications. PET imaging has also become an important tool in preclinical studies, particularly for investigating murine models of disease and other small-animal models. However, there are several challenges to using PET imaging systems. These include the fundamental trade-offs between resolution and noise, the quantitative accuracy of the measurements, and integration with X-ray computed tomography and magnetic resonance imaging. In this article, we review how researchers and industry are addressing these challenges.This work was supported in part by National Institutes of Health grants R01-CA042593, U01-CA148131, R01CA160253, R01CA169072, and R01CA164371; by Human Frontier Science Program grant RGP0004/2013; and by the Innovative Medicines Initiative under grant agreement 115337, which comprises financial contributions from the European Union’s Seventh Framework Program (FP7/2007–2013

Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Content-Based Image Retreival for Detecting Brain Tumors and Amyloid Fluid Presence

Medical images play a vital role in identifying diseases and detecting if organs are functioning correctly. Image processing related to medical images is an active research area in which various techniques are used in order to make diagnosis easier. The brain is a vital organ in our body, and brain tumors are a very critical life altering condition. Identifying tumors is a challenging task and various image processing techniques can be used. Doctors can identify tumors from looking at the scan, and this project attempts to automatically derive these results. In this project, image processing is done for automatically detecting the presence of brain tumors in a given brain scan. Content-based image retrieval extracts features from a query or template image, computes a measure of similarity, and gives results by detecting tumors. Template matching is used to identify a template at any position within the image to identify tumor location. Secondly, early detection of Alzheimer’s, which in turn prevents dementia, can be determined from the presence of amyloid fluid along with the other factors. The amyloid fluid presence helps in detecting dementia at an early stage. The presence of this fluid can be found in a PET scan of the brain. Here, the idea is to show the color distribution from a scan image, i.e., the domination of given colors. Content-based image retrieval’s low level feature based approaches such as color histograms are used. In this project, the conventional K- means algorithm is used for clustering the histograms, and identifying dominant colors

Dynamic PET-Tau Quantification for Progressive Supranuclear Palsy Diagnosis

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2023-2024. Tutor: Raúl Tudela ; Director: Aida Niñerola, Raúl TudelaTauopathies are neurodegenerative diseases caused by the abnormal accumulation of tau proteins in the brain. One uncommon tauopathy is progressive supranuclear palsy (PSP), whose symptoms often overlap with other brain disorders, and its detection is only possible postmortem since there is not an available ideal biomarker. PET-tau imaging has the potential to revolutionize the early detection of this disease. PET is a nuclear imaging test which allows seeing the functionality of organs and tissues in vivo using a radiotracer that emits radiation from inside the body. A new PET tracer called 18F-PI-2620 has shown promising results concerning the detection of PSP, with high affinity to tau aggregates and low off-target binding. This project consists of designing and testing a software for the quantification of PET images of the brain with a dynamic acquisition, which show the radiotracer distribution through time. The software performs a coregistration of the images to the standard space, where the different regions of the brain can be segmented using an atlas, and provides two physiologically meaningful parameters which are the Distribution Volume Ratio (DVR) and Standardized Uptake Value Ratio (SUVR). It gives out the DVR and SUVR values for any region of interest, as well as parametric images which help visualizing the radiotracer distribution in the brain. A set of brain PET images from 13 subjects acquired using 18F-PI-2620 has been used for the development and testing of the software, divided into healthy controls, subjects with Down syndrome, some of whom have developed Alzheimer’s disease (AD), which also implies a higher amount of abnormal deposited tau proteins. The results have shown higher DVR and SUVR values for several brain regions in those subjects who have developed AD, confirming that they have a higher radiotracer uptake and a greater amount of deposited tau proteins. This proves the correct functionality of the software and its potential as a future tool for detecting tauopathies such as PSP in combination with the radiotracer

MR-based attenuation correction and scatter correction in neurological PET/MR imaging with 18F-FDG

The aim was to investigate the effects of MR-based attenuation correction (MRAC) and scatter correction to positron emission tomography (PET) image quantification in neurological PET/MR with 18F-FDG. A multi-center phantom study was conducted to investigate the effect of MRAC between PET/MR and PET/CT systems (I). An MRAC method to derive bone from T1-weighted MR images was developed (II, III). Finally, scatter correction accuracy with MRAC was investigated (IV). The results show that the quantitative accuracy in PET is well-comparable be-tween PET/MR and PET/CT systems when an attenuation correction method resembling CT-based attenuation correction (CTAC) is implemented. This al-lows achieving of a PET bias within standard uptake value (SUV) quantification repeatability (< 10 % error) and is within the repeatability of PET in most sys-tems and brain regions (< 5 % error). In addition, MRAC considering soft tissue, air and bone can be derived using T1-weighted images alone. The improved version of the MRAC method allows achieving a quantitative accuracy feasible for advanced applications (< 5 % error). MRAC has a minor effect on the scatter correction accuracy (< 3 % error), even when using MRAC without bone. In conclusion, MRAC can be considered the largest contributing factor to PET quantification bias in 18F-FDG neurological PET/MR. This finding is not explicitly limited only to 18F-FDG imaging. Once an MRAC method that performs close to CTAC is implemented, there is no reason why a PET/MR system would perform differently from a PET/CT system. Such an MRAC method has been developed and is freely available (http://bit.ly/2fx6Jjz). Scatter correction can be considered a non-issue in neurological PET/MR imaging when using 18F-FD