55 research outputs found

    Quantitative I-131 SPECT Reconstruction using CT Side Information from Hybrid Imaging

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    A penalized-likelihood (PL) SPECT reconstruction method using a modified regularizer that accounts for anatomical boundary side information was implemented to achieve accurate estimates of both the total target activity and the activity distribution within targets. In both simulations and experimental I-131 phantom studies, reconstructions from 1) penalized likelihood employing CT-side information based regularization (PL-CT); 2) penalized likelihood with edge preserving regularization (no CT); 3) penalized likelihood with conventional spatially invariant quadratic regularization (no CT) were compared with 4) Ordered Subset Expectation Maximization (OSEM), which is the iterative algorithm conventionally used in clinics for quantitative SPECT. Evaluations included phantom studies with perfect and imperfect (misregistered) side information and studies with uniform and non-uniform activity distributions in the target. For targets with uniform activity, the PL-CT images and profiles were closest to the `truth', avoided the edge offshoots evident with OSEM and minimized the blurring across boundaries evident with regularization without CT information. Apart from visual comparison, reconstruction accuracy was evaluated using the bias and standard deviation (STD) of the total target activity estimate and the root mean square error (RMSE) of the activity distribution within the target. PL-CT reconstruction reduced both bias and RMSE compared with regularization without side information. When compared with unregularized OSEM, PL-CT reduced RMSE and STD while bias was comparable. For targets with non-uniform activity, these improvements with PL-CT were observed only when the change in activity was matched by a change in the anatomical image and the corresponding inner boundary was also used to control the regularization. In summary, the present work demonstrates the potential of using CT side information to obtain improved estimates of the activity distribution in targets wi- - thout sacrificing the accuracy of total target activity estimation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85862/1/Fessler243.pd

    Penalized PET/CT Reconstruction Algorithms With Automatic Realignment for Anatomical Priors

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    Two algorithms for solving misalignment issues in penalized PET/CT reconstruction using anatomical priors are proposed. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to align them with the measured data. Our current implementation alternates between alignment estimation and image reconstruction. We have chosen parallel level sets (PLSs) as a representative anatomical penalty, incorporating a spatially variant penalty strength. The performance was evaluated using simulated nontime-of-flight data generated with an XCAT phantom in the thorax region. We used the attenuation map in the anatomical prior. The results demonstrated that both methods can estimate the misalignment and deform the anatomical image accordingly. However, the performance of the first approach depends highly on the workflow of the alternating process. The second approach shows a faster convergence rate to the correct alignment and is less sensitive to the workflow. The presence of anatomical information improves the convergence rate of misalignment estimation for the second approach but slow it down for the first approach. Both approaches show improved performance in misalignment estimation as the data noise level decreases

    Regularized reconstruction in quantitative SPECT using CT side information from hybrid imaging

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    A penalized-likelihood (PL) SPECT reconstruction method using a modified regularizer that accounts for anatomical boundary side information was implemented to achieve accurate estimates of both the total target activity and the activity distribution within targets. In both simulations and experimental I-131 phantom studies, reconstructions from (1) penalized likelihood employing CT-side information-based regularization (PL-CT), (2) penalized likelihood with edge preserving regularization (no CT) and (3) penalized likelihood with conventional spatially invariant quadratic regularization (no CT) were compared with (4) ordered subset expectation maximization (OSEM), which is the iterative algorithm conventionally used in clinics for quantitative SPECT. Evaluations included phantom studies with perfect and imperfect side information and studies with uniform and non-uniform activity distributions in the target. For targets with uniform activity, the PL-CT images and profiles were closest to the 'truth', avoided the edge offshoots evident with OSEM and minimized the blurring across boundaries evident with regularization without CT information. Apart from visual comparison, reconstruction accuracy was evaluated using the bias and standard deviation (STD) of the total target activity estimate and the root mean square error (RMSE) of the activity distribution within the target. PL-CT reconstruction reduced both bias and RMSE compared with regularization without side information. When compared with unregularized OSEM, PL-CT reduced RMSE and STD while bias was comparable. For targets with non-uniform activity, these improvements with PL-CT were observed only when the change in activity was matched by a change in the anatomical image and the corresponding inner boundary was also used to control the regularization. In summary, the present work demonstrates the potential of using CT side information to obtain improved estimates of the activity distribution in targets without sacrificing the accuracy of total target activity estimation. The method is best suited for data acquired on hybrid systems where SPECT-CT misregistration is minimized. To demonstrate clinical application, the PL reconstruction with CT-based regularization was applied to data from a patient who underwent SPECT/CT imaging for tumor dosimetry following I-131 radioimmunotherapy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85409/1/pmb10_9_007.pd

    해부학적 유도 PET 재구성: 매끄럽지 않은 사전 함수부터 딥러닝 접근까지

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    학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2021. 2. 이재성.Advances in simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) technology have led to an active investigation of the anatomy-guided regularized PET image reconstruction algorithm based on MR images. Among the various priors proposed for anatomy-guided regularized PET image reconstruction, Bowsher’s method based on second-order smoothing priors sometimes suffers from over-smoothing of detailed structures. Therefore, in this study, we propose a Bowsher prior based on the l1 norm and an iteratively reweighting scheme to overcome the limitation of the original Bowsher method. In addition, we have derived a closed solution for iterative image reconstruction based on this non-smooth prior. A comparison study between the original l2 and proposed l1 Bowsher priors were conducted using computer simulation and real human data. In the simulation and real data application, small lesions with abnormal PET uptake were better detected by the proposed l1 Bowsher prior methods than the original Bowsher prior. The original l2 Bowsher leads to a decreased PET intensity in small lesions when there is no clear separation between the lesions and surrounding tissue in the anatomical prior. However, the proposed l1 Bowsher prior methods showed better contrast between the tumors and surrounding tissues owing to the intrinsic edge-preserving property of the prior which is attributed to the sparseness induced by l1 norm, especially in the iterative reweighting scheme. Besides, the proposed methods demonstrated lower bias and less hyper-parameter dependency on PET intensity estimation in the regions with matched anatomical boundaries in PET and MRI. Moreover, based on the formulation of l1 Bowsher prior, the unrolled network containing the conventional maximum-likelihood expectation-maximization (ML-EM) module was also proposed. The convolutional layers successfully learned the distribution of anatomically-guided PET images and the EM module corrected the intermediate outputs by comparing them with sinograms. The proposed unrolled network showed better performance than ordinary U-Net, where the regional uptake is less biased and deviated. Therefore, these methods will help improve the PET image quality based on the anatomical side information.양전자방출단층촬영 / 자기공명영상 (PET/MRI) 동시 획득 기술의 발전으로 MR 영상을 기반으로 한 해부학적 사전 함수로 정규화 된 PET 영상 재구성 알고리즘에 대한 심도있는 평가가 이루어졌다. 해부학 기반으로 정규화 된 PET 이미지 재구성을 위해 제안 된 다양한 사전 중 2차 평활화 사전함수에 기반한 Bowsher의 방법은 때때로 세부 구조의 과도한 평활화로 어려움을 겪는다. 따라서 본 연구에서는 원래 Bowsher 방법의 한계를 극복하기 위해 l1 norm에 기반한 Bowsher 사전 함수와 반복적인 재가중치 기법을 제안한다. 또한, 우리는 이 매끄럽지 않은 사전 함수를 이용한 반복적 이미지 재구성에 대해 닫힌 해를 도출했다. 원래 l2와 제안 된 l1 Bowsher 사전 함수 간의 비교 연구는 컴퓨터 시뮬레이션과 실제 데이터를 사용하여 수행되었다. 시뮬레이션 및 실제 데이터에서 비정상적인 PET 흡수를 가진 작은 병변은 원래 Bowsher 이전보다 제안 된 l1 Bowsher 사전 방법으로 더 잘 감지되었다. 원래의 l2 Bowsher는 해부학적 영상에서 병변과 주변 조직 사이에 명확한 분리가 없을 때 작은 병변에서의 PET 강도를 감소시킨다. 그러나 제안 된 l1 Bowsher 사전 방법은 특히 반복적 재가중치 기법에서 l1 노름에 의해 유도된 희소성에 기인한 특성으로 인해 종양과 주변 조직 사이에 더 나은 대비를 보여주었다. 또한 제안된 방법은 PET과 MRI의 해부학적 경계가 일치하는 영역에서 PET 강도 추정에 대한 편향이 더 낮고 하이퍼 파라미터 종속성이 적음을 보여주었다. 또한, l1Bowsher 사전 함수의 닫힌 해를 기반으로 기존의 ML-EM (maximum-likelihood expectation-maximization) 모듈을 포함하는 펼쳐진 네트워크도 제안되었다. 컨볼루션 레이어는 해부학적으로 유도 재구성된 PET 이미지의 분포를 성공적으로 학습했으며, EM 모듈은 중간 출력들을 사이노그램과 비교하여 결과 이미지가 잘 들어맞게 수정했다. 제안된 펼쳐진 네트워크는 지역의 흡수선량이 덜 편향되고 편차가 적어, 일반 U-Net보다 더 나은 성능을 보여주었다. 따라서 이러한 방법들은 해부학적 정보를 기반으로 PET 이미지 품질을 향상시키는 데 유용할 것이다.Chapter 1. Introduction 1 1.1. Backgrounds 1 1.1.1. Positron Emission Tomography 1 1.1.2. Maximum a Posterior Reconstruction 1 1.1.3. Anatomical Prior 2 1.1.4. Proposed l_1 Bowsher Prior 3 1.1.5. Deep Learning for MR-less Application 4 1.2. Purpose of the Research 4 Chapter 2. Anatomically-guided PET Reconstruction Using Bowsher Prior 6 2.1. Backgrounds 6 2.1.1. PET Data Model 6 2.1.2. Original Bowsher Prior 7 2.2. Methods and Materials 8 2.2.1. Proposed l_1 Bowsher Prior 8 2.2.2. Iterative Reweighting 13 2.2.3. Computer Simulations 15 2.2.4. Human Data 16 2.2.5. Image Analysis 17 2.3. Results 19 2.3.1. Simulation with Brain Phantom 19 2.3.2.Human Data 20 2.4. Discussions 25 Chapter 3. Deep Learning Approach for Anatomically-guided PET Reconstruction 31 3.1. Backgrounds 31 3.2. Methods and Materials 33 3.2.1. Douglas-Rachford Splitting 33 3.2.2. Network Architecture 34 3.2.3. Dataset and Training Details 35 3.2.4. Image Analysis 36 3.3. Results 37 3.4. Discussions 38 Chapter 4. Conclusions 40 Bibliography 41 Abstract in Korean (국문 초록) 52Docto

    Postreconstruction filtering of 3D PET images by using weighted higher-order singular value decomposition

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    Additional file 1. Original 3D PET images data used in this work to generate the results

    Multimodality imaging of brown adipose tissue

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    There are two types of adipose tissue in the human body. White adipose tissue (WAT) stores energy, while brown adipose tissue (BAT) consumes it. BAT can be activated by exposure to cold to generate heat. Human adults seem to have recruitable ‘beige’ or ‘brite’ fat, which is derived from WAT. The cells can take on the appearance and function of BAT upon prolonged stimulation by cold, but the process can also be reversed. Thus adult human BAT contains a mixture of brown and white adipocytes at different stages, the triglyceride content being a continuous spectrum. Human BAT is highly insulin-sensitive. Decreases in BAT mass and activity may have a role in the development of obesity and diabetes in adulthood. The prevalence of these conditions is growing worldwide, leading to a global health issue and socioeconomic problem. This poses a great need for rapid and affordable means of studying fat tissue. The in vivo localization and activation state of BAT has been assessed by 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET/CT), which involves the intravenous injection of radioactive tracer, as well as exposure to radiation by computed tomography. Due to the differences in the tissue structure, water and iron content, magnetic resonance imaging (MRI) can reliably measure BAT volume and water content regardless of the activation state. As a method it is noninvasive, safe and more readily available than 18FDG-PET. The aim was to develop and test MRI methods for detecting, quantifying and examining BAT. The methods include in-phase and out-of-phase (in/opp) imaging, signal-fat-fraction (SFF) analysis based on the Dixon method, T2* relaxation time mapping and single-voxel proton magnetic resonance spectroscopy (1H MRS). Our results suggest that MRI methods can identify BAT and quantify fat deposit triglyceride content independent of cold-induced BAT activation and without radiation burden. It was also shown that the triglyceride content in supraclavicular fat deposits measured by 1H-MRS may be an independent marker of whole-body insulin sensitivity.Ruskean rasvakudoksen kuvantaminen eri kuvantamismenetelmillä Ihmiskehossa on kahdenlaista rasvakudosta. Valkoinen rasva (WAT) varastoi energiaa, ruskea rasva (BAT) kuluttaa sitä. Kylmäaltistus voi aktivoida ruskean rasvan tuottamaan lämpöä. Aikuisilla on hankinnaista "beige" tai "brite" rasvaa, joka on muuntunutta valkoista rasvaa. Pitkittyneessä kylmäaltistuksessa WAT-solut voivat ulkonäöltään ja toiminnaltaan muuntua BAT-solujen kaltaisiksi, mutta tapahtumasarja voi kulkea myös toiseen suuntaan. Aikuisella ihmisellä BAT on siis sekoitus eri asteisesti kypsyneitä ruskeita ja valkoisia rasvasoluja, joiden triglyseridipitoisuus muuttuu liukuvasti. Ihmisellä BAT on vahvasti insuliiniherkkää. BAT:n määrän ja aktiivisuuden vähentymisellä voi olla merkitystä lihavuudessa ja aikuisiän diabeteksessa. Näiden esiintyminen on globaalisti kasvussa, mikä johtaa maailmanlaajuisiin terveysongelmiin ja sosioekonomisiin ongelmiin. On siis tarvetta nopeille ja edullisille tavoille tutkia rasvakudosta. BAT:n sijaintia ja aktivoitumista on tutkittu 18F-fluorodeoksiglukoosipositroniemissio-tomografialla (18FDG-PET/CT), jossa aiheutuu sädealtistus suonensisäisestä radioaktiivisesta merkkiaineesta ja tietokonetomografiakuvauksesta. Kudoksen rakenne-eroista sekä vesi- ja rautapitoisuudesta johtuen magneettiresonanssikuvantaminen (MRI) mittaa luotettavasti BAT:n tilavuutta ja vesipitoisuutta. Menetelmänä se on kajoamaton, turvallinen ja helpommin saatavissa kuin PET. Tavoitteena oli kehittää ja testata MRI-menetelmiä ruskean rasvan toteamiseen, mittaamiseen ja arviointiin. Menetelminä olivat in-phase/out-of-phase (in/opp) -kuvantaminen, dixon-menetelmään perustuva signaalirasvasuhdekuvantaminen (SFF), T2*-relaksaatioaikakartoitus sekä yksittäisen vokselin protonimagneettispektroskopia (1H MRS). Tuloksiemme mukaan MRI-menetelmät pystyvät arvioimaan rasvan triglyseridipitoisuutta kylmäaktivaatiosta riippumatta ja ilman säderasitusta. Osoitimme myös, että solisluun alapuolisen BAT-kertymän triglyseridipitoisuus voi olla kehon insuliiniherkkyyden itsenäinen merkkitekijä

    Absorbed dose maps of patients submitted to 68Ga-PSMA-11 PET/CT

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    Although nuclear medicine (NM) procedures are highly effective diagnostic tools, they have been contributing significantly, together with other medical diagnostic and therapeutic methodologies, to the increase in exposure to ionizing radiation in recent years. There is an urgent need to opti mize NM techniques, to maintain diagnostic quality at a minimum possible radiation absorbed dose. 68Ga-prostate-specific membrane antigen positron emission tomography/computed tomog raphy (68Ga-PSMA-11 PET/CT) imaging has rapidly gained notoriety in the NM field and, at the same time, personalized dosimetry studies using voxel-based methods have been performed. This study aimed to calculate the absorbed dose at the voxel level in the kidneys, liver, spleen, and red bone marrow, compare the results with other studies and draw conclusions regarding the safety of using 68Ga-PSMA-11 in NM clinics. Whole-body PET/CT images from six patients were acquired after a single 68Ga-PSMA 11 injection. After registration of the CT and PET images, the target organs were manually seg mented in the CT and resampled to the PET voxel size. Voxel S-values were computed for spe cific tissues using the Monte-Carlo N-Particle transport 6.1 code. The absorbed dose rates were obtained by convolution of the PET activity images with the specific S-values of each tissue. A time integral was then applied to each distribution to account for all 68Ga decay. Statistical dose values were computed and compared with the available literature. Considering all the patients included in this study, the kidneys received the highest radi ation, with a mean overall absorbed dose of 0.0561 mGy/MBq and a median overall absorbed dose of 0.0499 mGy/MBq. In contrast, the red bone marrow received the lowest absorbed dose values (mean dose: 0.0015 mGy/MBq, median dose: 0.0013 mGy/MBq). The present study showed lower dosimetry values than the literature, resulting in deviations ranging from -38.1% (in the liver) to -91.3% (in the red bone marrow). The present study employs a voxel-based approach, which considers a non-uniform bio distribution of the radiopharmaceutical in the organs and leads to dosimetry estimates closer to the real ones. The reasonable low absorbed doses in the four organs herein studied is an argument in favor of using 68Ga-PSMA-11 in NM clinics. In the Future Work chapter, a more specific dynamic NM imaging methodology, taking into consideration the radiopharmaceutical pharma cokinetics, is presented

    On pattern recognition of brain connectivity in resting-state functional MRI

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    Dissertação de mestrado integrado em Biomedical Engineering (specialization on Medical Informatics)The human urge and pursuit for information have led to the development of increasingly complex technologies, and new means to study and understand the most advanced and intricate biological system: the human brain. Large-scale neuronal communication within the brain, and how it relates to human behaviour can be inferred by delving into the brain network, and searching for patterns in connectivity. Functional connectivity is a steady characteristic of the brain, and it has been proved to be very useful for examining how mental disorders affect connections within the brain. The detection of abnormal behaviour in brain networks is performed by experts, such as physicians, who limit the process with human subjectivity, and unwittingly introduce errors in the interpretation. The continuous search for alternatives to obtain faster and robuster results have put Machine Learning and Deep Learning in the leading position of computer vision, as they enable the extraction of meaningful patterns, some beyond human perception. The aim of this dissertation is to design and develop an experiment setup to analyse functional connectivity at a voxel level, in order to find functional patterns. For the purpose, a pipeline was outlined to include steps from data download to data analysis, resulting in four methods: Data Download, Data Preprocessing, Dimensionality Reduction, and Analysis. The proposed experiment setup was modeled using as materials resting state fMRI data from two sources: Life and Health Sciences Research Institute (Portugal), and Human Connectome Project (USA). To evaluate its performance, a case study was performed using the In-House data for concerning a smaller number of subjects to study. The pipeline was successful at delivering results, although limitations concerning the memory of the machine used restricted some aspects of this experiment setup’s testing. With appropriate resources, this experiment setup may support the process of analysing and extracting patterns from any resting state functional connectivity data, and aid in the detection of mental disorders.O desejo e a busca intensos do ser humano por informação levaram ao desenvolvimento de tecnologias cada vez mais complexas e novos meios para estudar e entender o sistema biológico mais avançado e intrincado: o cérebro humano. A comunicação neuronal em larga escala no cérebro, e como ela se relaciona com o comportamento humano, pode ser inferida investigando a rede neuronal cerebral e procurando por padrões de conectividade. A conectividade funcional é uma característica constante do cérebro e provou ser muito útil para examinar como os distúrbios mentais afetam as conexões cerebrais. A deteção de anormalidades em imagens de ressonância magnética é realizada por especialistas, como médicos, que limitam o processo com a subjetividade humana e, inadvertidamente, introduzem erros na interpretação. A busca contínua de alternativas para obter resultados mais rápidos e robustos colocou as técnicas de machine learning e deep learning na posição de liderança de visão computacional, pois permitem a extração de padrões significativos e alguns deles para além da percepção humana. O objetivo desta dissertação é projetar e desenvolver uma configuração experimental para analisar a conectividade funcional ao nível do voxel, a fim de encontrar padrões funcionais. Nesse sentido, foi delineado um pipeline para incluir etapas a começar no download de dados até à análise desses mesmos dados, resultando assim em quatro métodos: Download de Dados, Pré-processamento de Dados, Redução de Dimensionalidade e Análise. A configuração experimental proposta foi modelada usando dados de ressonância magnética funcional de resting-state de duas fontes: Instituto de Ciências da Vida e Saúde (Portugal) e Human Connectome Project (EUA). Para avaliar o seu desempenho, foi realizado um estudo de caso usando os dados internos por considerar um número menor de participantes a serem estudados. O pipeline foi bem-sucedido em fornecer resultados, embora limitações relacionadas com a memória da máquina usada tenham restringido alguns aspetos do teste desta configuração experimental. Com recursos apropriados, esta configuração experimental poderá servir de suporte para o processo de análise e extração de padrões de qualquer conjunto de dados de conectividade funcional em resting-state e auxiliar na deteção de transtornos mentais
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