Jorge Cabello;Michael T. Jurkiewicz;Andrea Andrade;Tammie L. S. Benzinger;Hongyu An;Udunna C. Anazodo
{"title":"Evaluation of an MRI-Guided PET Image Reconstruction Approach With Adaptive Penalization Strength","authors":"Jorge Cabello;Michael T. Jurkiewicz;Andrea Andrade;Tammie L. S. Benzinger;Hongyu An;Udunna C. Anazodo","doi":"10.1109/TRPMS.2024.3352983","DOIUrl":null,"url":null,"abstract":"MRI-guided (MRIg) positron emission tomography (PET) reconstruction can potentially reduce noise and increase spatial resolution compared to standard clinical ordered-subsets expectation-maximization (OSEM) image quality. However, to adjust for the desired image quality, the balance between measured data and prior information usually requires manual tuning. This work presents an adaptive method to automatically control the influence of the magnetic resonance imaging (MRI) information on the PET emission data using maximum a posteriori (MAP) image reconstruction, robust against a wide range of counts. The method was evaluated on different static brain PET datasets using [18F]-FDG, [18F]-Florbetapir and [11C]-PiB, acquired in a simultaneous PET/MRI scanner and a PET/CT scanner, followed by an MRI scan. Noise in gray and white matter was measured for a wide range of statistics. Furthermore, noise and quantification accuracy were analyzed in different cortical and subcortical brain regions with different levels of tracer uptake, and at different levels of counts. Results demonstrated consistent improved image quality in terms of noise and spatial resolution with MRI-guided MAP PET (MRIg-MAP) reconstruction compared to OSEM. Additionally, it was shown that the number of collected counts could be reduced by ~1.6–\n<inline-formula> <tex-math>$2.3\\times $ </tex-math></inline-formula>\n using MRIg-MAP reconstruction compared to OSEM, without increasing the noise significantly, either by reducing the scan time or injected activity. In conclusion, we presented a novel method to adaptively balance the influence of the anatomical information on the emission data for MRIg-MAP reconstruction, which showed image quality improvements compared to OSEM for different radiotracers, at different levels of counts, and applicable to simultaneous and sequential PET-MRI scans.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Radiation and Plasma Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10391057/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
MRI-guided (MRIg) positron emission tomography (PET) reconstruction can potentially reduce noise and increase spatial resolution compared to standard clinical ordered-subsets expectation-maximization (OSEM) image quality. However, to adjust for the desired image quality, the balance between measured data and prior information usually requires manual tuning. This work presents an adaptive method to automatically control the influence of the magnetic resonance imaging (MRI) information on the PET emission data using maximum a posteriori (MAP) image reconstruction, robust against a wide range of counts. The method was evaluated on different static brain PET datasets using [18F]-FDG, [18F]-Florbetapir and [11C]-PiB, acquired in a simultaneous PET/MRI scanner and a PET/CT scanner, followed by an MRI scan. Noise in gray and white matter was measured for a wide range of statistics. Furthermore, noise and quantification accuracy were analyzed in different cortical and subcortical brain regions with different levels of tracer uptake, and at different levels of counts. Results demonstrated consistent improved image quality in terms of noise and spatial resolution with MRI-guided MAP PET (MRIg-MAP) reconstruction compared to OSEM. Additionally, it was shown that the number of collected counts could be reduced by ~1.6–
$2.3\times $
using MRIg-MAP reconstruction compared to OSEM, without increasing the noise significantly, either by reducing the scan time or injected activity. In conclusion, we presented a novel method to adaptively balance the influence of the anatomical information on the emission data for MRIg-MAP reconstruction, which showed image quality improvements compared to OSEM for different radiotracers, at different levels of counts, and applicable to simultaneous and sequential PET-MRI scans.
与标准临床有序子集期望最大化(OSEM)图像质量相比,磁共振成像引导(MRIg)正电子发射断层扫描(PET)重建有可能减少噪声并提高空间分辨率。然而,为了调整所需的图像质量,通常需要手动调整测量数据和先验信息之间的平衡。这项研究提出了一种自适应方法,利用最大后验(MAP)图像重建技术,自动控制磁共振成像(MRI)信息对 PET 发射数据的影响,对各种计数具有鲁棒性。该方法在使用[18F]-FDG、[18F]-Florbetapir和[11C]-PiB的不同静态脑PET数据集上进行了评估,这些数据集是在PET/MRI同步扫描仪和PET/CT扫描仪上获得的,随后进行了MRI扫描。对灰质和白质的噪声进行了广泛的统计测量。此外,还分析了不同皮质和皮质下脑区在不同示踪剂摄取水平和不同计数水平下的噪声和量化准确性。结果表明,与 OSEM 相比,MRI 引导的 MAP PET(MRIg-MAP)重建在噪声和空间分辨率方面的图像质量得到了持续改善。此外,研究还表明,与OSEM相比,使用MRIg-MAP重建可将收集到的计数数量减少约1.6-2.3倍,而不会显著增加噪声,无论是通过减少扫描时间还是注射活性都是如此。总之,我们提出了一种新方法来适应性地平衡MRIg-MAP重建中解剖信息对发射数据的影响,与OSEM相比,该方法在不同放射性核素、不同计数水平下的图像质量都有所改善,并且适用于同步和顺序PET-MRI扫描。