TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction

Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-frame motion correction, especially for early frames where intensity-based image registration techniques often fail. To address this issue, we propose a novel method called Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames into those with tracer distribution similar to the last reference frame. The TAI-GAN consists of a feature-wise linear modulation layer that encodes channel-wise parameters generated from temporal information and rough cardiac segmentation masks with local shifts that serve as anatomical information. Our proposed method was evaluated on a clinical 82-Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, the motion estimation accuracy and subsequent myocardial blood flow (MBF) quantification with both conventional and deep learning-based motion correction methods were improved compared to using the original frames.Comment: Under revision at Medical Image Analysi

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

Postprocessing Neuroimaging methods in MRI and PET/MRI with applications to Multiple Sclerosis and other Neurological diseases

Many non-invasive imaging instruments have been developed in the last 40 years, allowing to obtain images of the interior human body while the patient is still alive. In the contest of Neurology studies, imaging system as CT, MRI, SPECT or PET allows to obtain biomarkers useful to quantitatively distinguish between healthy and unhealthy subjects, evaluate the staging of a Neurological illness in a patient, evaluate the efficacy of a treatment, explore the causes of the illness. In this work MRI and PET imaging system introduced from scratch, going from reconstruction from raw data to state-of-the art post-processing techniques and the computation of more popular biomarkers. After these introduction, three original work using the recent PET/MRI imaging system are presented, with a particular focus on the methods. These three studies involve patients with Multiple Sclerosis, Alzheimer's Disease and Brain Tumor

Exploiting MRI information for improved kinetic modelling of dynamic PET data

Kinetic analysis of dynamic PET data requires an accurate estimation of the concen- tration of the available tracer in blood plasma, also known as the arterial input function (AIF). The gold standard method to determine the AIF involves serial blood sampling and is avoided in practice due to its invasiveness. An image derived input function (IDIF) can be a blood-free alternative but its accuracy is limited due to partial volume (PV) effects caused by the restricted spatial resolution of PET scanners. Furthermore, IDIFs are not accurate when metabolite products are present in the blood. Magnetic resonance imaging (MRI) can provide complementary information to PET with high spatial resolution and excellent soft tissue contrast. Furthermore, dynamic MRI techniques can be reliably used to measure the AIF, the concentration of contrast agent in plasma, due to their high temporal resolution. The underlying aim of this research is to improve IDIF estimation in PET, utilising spatial and temporal information from MRI. An IDIF measurement method was developed which involves segmentation of carotid arteries from MR angiography images and uses a practical PVC method to correct for PV effects. It was demonstrated that the IDIFs can be used to compute the cerebral metabolic rate of glucose in the brain with no significant difference compared to arterial sampling. The simultaneous estimation method (SIME) is an alternative technique used to estimate the AIF by fitting time activity curves derived from multiple regions. Due to its computational complexity, SIME is usually complemented with blood samples. In this work, we observed that the early part of an image derived blood curve or an MRI derived AIF could provide prior knowledge regarding the AIF. This was incorporated into SIME to make more accurate kinetic parameter estimations and to perform blood-free analysis of tracers with metabolites

Dual gated PET/CT imaging of heart

Coronary artery disease (CAD) resulting from atherosclerotic arterial changes, plaques, is a progressive process, which can be asymptomatic for many years. Asymptomatic CAD can cause a heart attack that leads to sudden death if the vulnerable coronary plaque ruptures and causes artery occlusion. The plaque inflammation plays an important role in the rupture susceptibility. Reliable anticipation of rupture is still clinically impossible for a single patient. Detection of the vulnerable coronary plaques before clinical signs remains a significant scientific challenge where positron emission tomography (PET) can play an important role. The aim of this dissertation was to find out whether a small, coronary plaque size, heart structures could be detected by a clinically available positron emission tomography and computed tomography (PET/CT) hybrid camera in realistically moving cardiac phantoms, a minipig model, and patients with CAD. Due to cardiac motions accurate detection of small heart structures are known to be problematic in PET imaging. Due to absence of commercial application at the beginning of the study, new dual gating method for cardiac PET imaging was developed and programmed that takes into account both contraction and respiratory induced cardiac motions. Cardiac phantom PET studies showed that small, active and moving plaques can be distinguished from myocardium activity and the gating methods improved the detection sensitivity and resolution of the plaques. In minipig and CAD patient cardiac PET studies small structures of myocardium and coronary arteries was detected more sensitive and accurately when using dual gating method than manufacturer gating methods. In cardiac patient PET study respiratory induced cardiac motions were shown to be linearly dependent with spirometry-measured respiratory volumes. Standard 3-lead electrocardiogram (ECG) measurement can be filtered by anesthesia monitor to detect lung impedance signal. In cardiac patient PET study this lung impedance signal were applied for respiratory gating. In this study was observed that the 3-lead ECG derived impedance signal gating method detects respiratory induced cardiac motion in PET as well as other externally used respiratory gating methods. In summary, the dual gated cardiac PET method is more sensitive and accurate to detect small cardiac structures, as coronary vessel wall pathology, than the commercial methods used in the study.Sydämen kaksoisliiketahdistettu PET/CT kuvantaminen Ateroskleroottisten valtimomuutosten, plakkien, seurauksena asteittain kehittyvä sepelvaltimotauti voi olla vuosia oireeton. Oireeton sepelvaltimotauti voi aiheuttaa äkkikuolemaan johtavan sydäninfarktin, mikäli sepelvaltimon seinämäplakin repeytymisestä aiheutuu verisuonen tukkiva hyytymä. Tutkimuksissa on osoitettu, että plakin tulehduksella on merkittävä rooli repeytymisalttiudelle. Repeytymisen luotettava ennakointi on yksittäisen potilaan kohdalla edelleen kliinisesti mahdotonta. Tulehtuneiden ja repeytymisalttiiden sepelvaltimoplakkien toteaminen ennen kliinisiä oireita on edelleen merkittävä tieteellinen haaste, missä positroniemissiotomografia (PET) kuvantamisella voi olla merkittävä rooli. Väitöskirjan tavoitteena oli selvittää, voidaanko kliinisessä käytössä olevalla positroniemissiotomografia ja tietokonetomografia (PET/TT) yhdistelmäkameralla havaita pieniä, sepelvaltimoplakkien kokoisia, sydämen rakenteita koneellisesti toimivissa todenmukaisissa sydänmalleissa, eläinmallissa ja sepelvaltimotautia sairastavilla potilailla. Sydämen pienten rakenteiden tarkka havaitseminen PET/TTkameroilla on haasteellista sydämen liikkumisen vuoksi. Tutkimuksessa kehitettiin ja ohjelmoitiin uusi sydämen PET-kuvantamisen liiketahdistusmenetelmä, joka ottaa huomioon sekä sydämen supistusliikkeen että hengitysliikkeen vaikutuksen sydämen PET kuvantamissa. Koneellisilla sydänmalleilla osoitettiin, että PET on riittävän herkkä havaitsemaan pieniä ja liikkuvia radioaktiivisia ”sepelvaltimoplakkeja”, ja että liiketahdistusmenetelmät parantavat plakkien havaitsemisherkkyyttä ja tarkkuutta. Eläinmallissa ja sepelvaltimotautipotilailla kaksoisliiketahdistusmenetelmän herkkyys ja tarkkuus havaita pieniä sydänlihaksen ja sepelvaltimoiden rakenteita todettiin kaupallisia tahdistusmenetelmiä paremmaksi. Potilastutkimuksissa todettiin hengityksen aiheuttama sydämen liike PET-kuvissa lineaarisesti riippuvaiseksi spirometrialla mitattujen hengitystilavuuksien kanssa. Tavallisesta 3-johtoisesta sydänsähkökäyrästä voidaan anestesiamonitorin avulla suodattaa keuhkojen impedanssisignaalia. Hengitysliikkeen aiheuttama potilaiden sydämen liike PETkuvissa havaittiin yhtä hyvin käyttämällä tätä keuhkojen impedanssisignaalia kuin muita yleisesti käytettäviä ulkoisia hengitystahdistussignaaleja. Todetaan, että kaksoisliiketahdistettu sydämen PET-kuvantamismenetelmä on tutkimuksessa käytettyjä kaupallisia menetelmiä herkempi ja tarkempi havaitsemaan sydämen pieniä rakenteita sekä sepelvaltimon seinämän tulehdusplakkeja

Improving Quantification in Lung PET/CT for the Evaluation of Disease Progression and Treatment Effectiveness

Positron Emission Tomography (PET) allows imaging of functional processes in vivo by measuring the distribution of an administered radiotracer. Whilst one of its main uses is directed towards lung cancer, there is an increased interest in diffuse lung diseases, for which the incidences rise every year, mainly due to environmental reasons and population ageing. However, PET acquisitions in the lung are particularly challenging due to several effects, including the inevitable cardiac and respiratory motion and the loss of spatial resolution due to low density, causing increased positron range. This thesis will focus on Idiopathic Pulmonary Fibrosis (IPF), a disease whose aetiology is poorly understood while patient survival is limited to a few years only. Contrary to lung tumours, this diffuse lung disease modifies the lung architecture more globally. The changes result in small structures with varying densities. Previous work has developed data analysis techniques addressing some of the challenges of imaging patients with IPF. However, robust reconstruction techniques are still necessary to obtain quantitative measures for such data, where it should be beneficial to exploit recent advances in PET scanner hardware such as Time of Flight (TOF) and respiratory motion monitoring. Firstly, positron range in the lung will be discussed, evaluating its effect in density-varying media, such as fibrotic lung. Secondly, the general effect of using incorrect attenuation data in lung PET reconstructions will be assessed. The study will compare TOF and non-TOF reconstructions and quantify the local and global artefacts created by data inconsistencies and respiratory motion. Then, motion compensation will be addressed by proposing a method which takes into account the changes of density and activity in the lungs during the respiration, via the estimation of the volume changes using the deformation fields. The method is evaluated on late time frame PET acquisitions using ¹⁸F-FDG where the radiotracer distribution has stabilised. It is then used as the basis for a method for motion compensation of the early time frames (starting with the administration of the radiotracer), leading to a technique that could be used for motion compensation of kinetic measures. Preliminary results are provided for kinetic parameters extracted from short dynamic data using ¹⁸F-FDG

Parametric imaging of FET PET using nonlinear based fitting

Tese de mestrado integrado em Engenharia Biomédica e Biofísica , apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2016A importância do uso de aminoácidos marcados com isótopos radioativos em estudos de Tomografia de Emissão de Positrões (PET) tem sido amplamente demonstrada. Dentro deste grupo de traçadores, a metionina marcada com 11C tem sido o mais estudado. No entanto, a curta semi-vida do radioisótopo 11C tem levado ao desenvolvimento de marcadores análogos. Os marcadores com o radioisótopo 18F revelam-se os mais promissores para deteção de tumores no cérebro. Mais especificamente, o marcador O-(2-18F-Fluoroetil)-L-tirosina (FET) provou ser de grande importância na determinação da dimensão de tumores cerebrais e dos locais onde realizar a biopsia, no planeamento do tratamento a aplicar, e na deteção de recorrências. Foi também demonstrado que a forma como o FET é metabolizado ao longo do tempo depende do grau do tumor em estudo. Em gliomas de alto grau (HGG), a taxa de captação do FET é caracterizada por um pico inicial, seguido de uma diminuição da captação de FET, enquanto que em gliomas de baixo-grau (LGG) a taxa de captação do marcador tem um aumento contínuo ao longo do tempo. O presente estudo contou com 11 pacientes (3 mulheres, 8 homens, idade: 45 ± 15 anos) com tumores cerebrais primários não tratados confirmados por histologia. Seis pacientes foram diagnosticados com HGG, enquanto os restantes 5 foram diagnosticados com LGG. Os dados de PET foram adquiridos com o PET Insert do sistema híbrido Siemens 3T MR-BrainPET. As imagens foram segmentadas de forma a extrair apenas o volume correspondente ao tumor. Após a segmentação, calculou-se a média das curvas de tempo-atividade (TAC) dos volumes tumorais segmentados (STVs), e foram usados métodos de regressão linear e não linear para fazer o ajuste à TAC de cada volume. Para calcular os ajustes com o modelo linear, foram descartados os primeiros 5 minutos de aquisição. Os ajustes baseados na regressão não-linear foram aplicados à TAC correspondente à média entre os 2 e os 60 minutos de aquisição após a injeção. As imagens dos parâmetros foram calculadas a partir dos ajustes baseados na regressão não linear e aplicados a cada voxel. Foram testados três modelos não lineares diferentes: um modelo linear amortecido exponencialmente, um modelo linear amortecido exponencialmente e com um offset, e um modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada. Dos ajustes não lineares, foram extraídos dois parâmetros: a amplitude, A, e o parâmetro κ. De seguida, geraram-se as imagens dos parâmetros calculados sobre uma área tridimensional selecionada manualmente e contendo o tumor. Para tal, além dos três modelos não lineares, utilizou-se também o modelo linear, de modo a permitir uma comparação entre os diferentes métodos. No caso dos ajustes lineares, os parâmetros extraídos foram a ordenada na origem e o declive. Calcularam-se também as imagens dos parâmetros da regressão não linear usando o modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada para a cabeça inteira. Os modelos não-lineares foram mais precisos na reprodução das curvas de FET. Os modelos mais robustos foram os modelos lineares exponencialmente amortecidos sem offset. Nos ajustes aplicados à TAC média dos STVs, o modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada provou ser o que reproduz mais precisamente os dados, com valores de 2 entre 0,94 e 1,00. O parâmetro A do modelo linear amortecido exponencialmente com o tempo dependente da raiz quadrada foi o único que revelou uma diferença significativa entre HGG e LGG (p-value= 0.04, α=0.05). Ao gerar imagens paramétricas com base nos ajustes aplicados a cada voxel, os modelos de regressão não-linear com 2 parâmetros tiveram o melhor desempenho, com valores de 2 perto de 1. Combinando as imagens do parâmetro amplitude e as imagens da atividade total ao longo do tempo, foi possível distinguir entre graus tumorais. Os LGGs assumem valores de amplitude próximos dos valores do tecido saudável à sua volta, e por isso “desaparecem” da imagem paramétrica da amplitude. No caso dos HGGs, a imagem da amplitude reproduz a atividade no tumor. Os ajustes realizados com base na regressão linear devolveram valores de 2 próximos de zero, quer no caso dos STVs, quer no cálculo das imagens paramétricas. A distinção entre HGG e LGG é possível com base nas imagens paramétricas do declive, com os LGGs a assumirem valores de declive superiores aos do tecido saudável adjacente. Com os HGGs, a situação é a oposta: os valores do declive no tumor são inferiores aos do tecido saudável que o rodeia. Em geral, os modelos não lineares reproduzem melhor os dados provenientes de FET PET, mas a distinção entre HGG e LGG baseada num parâmetro apenas é melhor conseguida através de regressão linear. No entanto, a distinção entre HGG e LGG também é possível analisando simultaneamente as imagens dos parâmetros A e κ.The importance of radiolabeled amino acids in Positron Emission Tomography (PET) imaging of the brain has been demonstrated by several studies. The most well studied amino acid tracer is 11C-metionine, but because of the short half-life of 11C, 18F-labeled amino acid analogues have been developed for tumour imaging. A number of studies have proven the importance of O-(2-18F-Fluoroethyl)-L-tyrosine (FET) in determining the extent of cerebral gliomas, biopsy guidance, treatment planning, and detecting recurrence of brain tumours. It was also demonstrated that dynamic changes of FET accumulation in gliomas are variable. High-grade gliomas (HGG) are characterized by an early peak, followed by decrease of FET uptake, whereas the uptake in low-grade gliomas (LGG) steadily increases. Eleven patients (3 female, 8 male, age: 45±15 years) with untreated primary brain tumours and histopathologic confirmation were studied. Six patients had HGG, while the remaining 5 were diagnosed with LGG. PET acquisition was done with the PET Insert of a hybrid Siemens 3T MR-BrainPET system. For tumour volume fitting, a segmentation procedure was applied. After segmentation, the mean time-activity curve (TAC) of the segmented tumour volumes (STVs) was calculated. Linear and nonlinear regression were used to fit to the TAC of each volume. When performing the fits with the linear model, the first 5 minutes of acquisition were discarded. For the nonlinear regression, the fits were applied to the mean TAC from 2 to 60 minutes after injection. Parametric images were calculated based on nonlinear regression fitting of FET data in each voxel. Three different nonlinear models were tested: an exponentially damped linear model, an exponentially damped linear model with an offset, and an exponentially damped linear model with square-root time dependence. The considered nonlinear model parameters were amplitude, A, and κ. The parametric images of manually selected tridimensional volumes containing the tumour were generated. Linear regression based parametric images were also computed for comparison, and the assessed parameters were intercept and slope. Whole-head parametric images were calculated based on nonlinear regression fitting using the exponentially damped linear model with square-root time dependence. Nonlinear regression models were more accurate at reproducing FET TAC characteristics. The most robust models are the exponentially damped linear models without offset. For mean TAC fitting, a model with square-root time dependence reproduced FET activity curves more accurately, with coefficient of determination (2) values between 0.94 and 1.00. The A parameter from the exponentially damped linear model with square-root time dependence was the only one significantly different between HGG and LGG (p-value= 0.04, α=0.05). When generating parametric images based on voxel-wise fit, the nonlinear regression models with 2 parameters performed the best, with 2 close to 1. Visual distinction between tumour grades was possible by comparing the amplitude images with the images of the summed activity across time. In the amplitude, LGGs take values similar to the ones of the surrounding background, thus disappearing from the image. On the other hand, HGGs amplitude images reproduce tumour uptake. Linear regression model fits returned 2 values that were close to zero in both mean TAC fitting, and parametric image calculation. Grade distinction was possible based on the slope parameter alone, with LGGs showing higher slope values than the neighbouring tissue, and HGGs showing lower slope values than their surroundings. In general, though nonlinear models reproduce FET time activity curves more accurately, the distinction between low-grade and high-grade tumours based on one parameter only is better achieved by using linear regression model fitting. However, a reliable differentiation seems to be possible with joint analysis of A and κ parametric images

PET/MR imaging of hypoxic atherosclerotic plaque using 64Cu-ATSM

ABSTRACT OF THE DISSERTATION PET/MR Imaging of Hypoxic Atherosclerotic Plaque Using 64Cu-ATSM by Xingyu Nie Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2017 Professor Pamela K. Woodard, Chair Professor Suzanne Lapi, Co-Chair It is important to accurately identify the factors involved in the progression of atherosclerosis because advanced atherosclerotic lesions are prone to rupture, leading to disability or death. Hypoxic areas have been known to be present in human atherosclerotic lesions, and lesion progression is associated with the formation of lipid-loaded macrophages and increased local inflammation which are potential major factors in the formation of vulnerable plaque. This dissertation work represents a comprehensive investigation of non-invasive identification of hypoxic atherosclerotic plaque in animal models and human subjects using the PET hypoxia imaging agent 64Cu-ATSM. We first demonstrated the feasibility of 64Cu-ATSM for the identification of hypoxic atherosclerotic plaque and evaluated the relative effects of diet and genetics on hypoxia progression in atherosclerotic plaque in a genetically-altered mouse model. We then fully validated the feasibility of using 64Cu-ATSM to image the extent of hypoxia in a rabbit model with atherosclerotic-like plaque using a simultaneous PET-MR system. We also proceeded with a pilot clinical trial to determine whether 64Cu-ATSM MR/PET scanning is capable of detecting hypoxic carotid atherosclerosis in human subjects. In order to improve the 64Cu-ATSM PET image quality, we investigated the Siemens HD (high-definition) PET software and 4 partial volume correction methods to correct for partial volume effects. In addition, we incorporated the attenuation effect of the carotid surface coil into the MR attenuation correction _-map to correct for photon attention. In the long term, this imaging strategy has the potential to help identify patients at risk for cardiovascular events, guide therapy, and add to the understanding of plaque biology in human patients