MR-based Attenuation Correction Methods Research Articles

Synthetic PET via Domain Translation of 3-D MRI.

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.

Deep learning for whole-body medical image generation.

Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and training approaches which do not require paired data. Recently, these techniques have been applied in the medical field for cross-domain image translation. This study investigated deep learning transformation in medical imaging. It was motivated to identify generalizable methods which would satisfy the simultaneous requirements of quality and anatomical accuracy across the entire human body. Specifically, whole-body MR patient data acquired on a PET/MR system were used to generate synthetic CT image volumes. The capacity of these synthetic CT data for use in PET attenuation correction (AC) was evaluated and compared to current MR-based attenuation correction (MR-AC) methods, which typically use multiphase Dixon sequences to segment various tissue types. This work aimed to investigate the technical performance of a GAN system for general MR-to-CT volumetric transformation and to evaluate the performance of the generated images for PET AC. A dataset comprising matched, same-day PET/MR and PET/CT patient scans was used for validation. A combination of training techniques was used to produce synthetic images which were of high-quality and anatomically accurate. Higher correlation was found between the values of mu maps calculated directly from CT data and those derived from the synthetic CT images than those from the default segmented Dixon approach. Over the entire body, the total amounts of reconstructed PET activities were similar between the two MR-AC methods, but the synthetic CT method yielded higher accuracy for quantifying the tracer uptake in specific regions. The findings reported here demonstrate the feasibility of this technique and its potential to improve certain aspects of attenuation correction for PET/MR systems. Moreover, this work may have larger implications for establishing generalized methods for inter-modality, whole-body transformation in medical imaging. Unsupervised deep learning techniques can produce high-quality synthetic images, but additional constraints may be needed to maintain medical integrity in the generated data.

Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging

BackgroundA valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study, we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain.MethodsNine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain.ResultsFor BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions. Atlas-MRAC exhibited a significant bias in caudate nucleus (− 12%) while MaxProb-MRAC revealed a substantial, non-significant bias in the putamen (9%). R1 estimates had a marginal bias for all MRAC methods (− 1.0–3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest intersubject variability. Atlas-MRAC had highest variation in bias over time (+10 to − 10%), followed by MaxProb-MRAC (+5 to − 5%), but MaxProb showed the lowest mean bias. For the cerebellum, MaxProb-MRAC showed the highest variability while bias was constant over time for Atlas- and ZTE-MRAC.ConclusionsBoth Maxprob- and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.

An Aristotelian View on MR-Based Attenuation Correction (ARISTOMRAC): Combining the Four Elements

MR-based attenuation correction (MRAC) is important for accurate quantification of the uptake of PET tracers in combined PET/MR scanners. However, current techniques for MRAC usually require multiple acquisitions or complex post-processing to discriminate the different tissues. Inspired by the ancient Greeks, who believed that matter was made of the combination of four elements (earth, water, air and fire), we formulated a multi-component Magnetic Resonance (MR) Fingerprinting framework, where every voxel was considered a weighted combination of four base elements: bone, water, air and fat. We named our approach Aristotelian MR based attenuation correction (ARISTOMRAC). We used a 3D radial acquisition scheme at 1.5T, acquiring a transient-state spoiled acquisition with variable flip angles and echo times (TE), with the shortest TEs being ultra-short echo times (UTE). We simulated a multi-tissue MR signal model using the Bloch equations and used dictionary matching to extract tissue fraction maps for bone water and fat, while air fractions were obtained by thresholding the UTE parts of our acquisitions at higher spatial resolution. Compared to previous methods for MR-based Attenuation Correction (MRAC), our approach used a full multi-component signal model, including multiple tissues per voxel. For this reason, rather than reconstructing high resolutions images, MR data can be acquired more efficiently, directly at the resolution needed for PET attenuation maps. The ARISTOMRAC method allows to accurately estimate the air, water, bone and fat fractions (Concordance Correlation Coefficient = 0.81/0.91/0.98 for bone, water and fat respectively). Attenuation maps could be obtained in the head and neck with a single 1-minute acquisition.

Developing an efficient phase-matched attenuation correction method for quiescent period PET in abdominal PET/MRI

Respiratory motion causes misalignments between positron emission tomography (PET) and magnetic resonance (MR)-derived attenuation maps (µ-maps) in addition to artifacts on both PET and MR images in simultaneous PET/MRI for organs such as liver that can experience motion of several centimeters. To address this problem, we developed an efficient MR-based attenuation correction (MRAC) method to generate phase-matched µ-maps for quiescent period PET (PETQ) in abdominal PET/MRI. MRAC data was acquired with CIRcular Cartesian UnderSampling (CIRCUS) sampling during 100 s in free-breathing as an accelerated data acquisition strategy for phase-matched MRAC (MRACPM-CIRCUS). For comparison, MRAC data with raster (Default) k-space sampling was also acquired during 100 s in free-breathing (MRACPM-Default), and used to evaluate MRACPM-CIRCUS as well as un-matched MRAC (MRACUM) that was un-gated. We purposefully oversampled the MRACPM data to ensure we had enough information to capture all respiratory phases to make this comparison as robust as possible. The proposed MRACPM-CIRCUS was evaluated in 17 patients with 68Ga-DOTA-TOC PET/MRI exams, suspected of having neuroendocrine tumors or liver metastases. Effects of CIRCUS sampling for accelerating a data acquisition were evaluated by simulating the data acquisition time retrospectively in increments of 5 s. Effects of MRACPM-CIRCUS on PETQ were evaluated using uptake differences in the liver lesions (n = 35), compared to PETQ with MRACPM-Default and MRACUM. A Wilcoxon signed-rank test was performed to compare lesion uptakes between the MRAC methods. MRACPM-CIRCUS showed higher image quality compared to MRACPM-Default for the same acquisition times, demonstrating that a data acquisition time of 30 s was reasonable to achieve phase-matched µ-maps. Lesion update differences between MRACPM-CIRCUS (30 s) versus MRACPM-Default (reference, 100 s) were 0.1% ± 1.4% (range of −2.7% to 3.2%) and not significant (P > .05); while, the differences between MRACUM versus MRACPM-Default were 0.6% ± 11.4% with a large variation (range of −37% to 20%) and significant (P < .05). In conclusion, we demonstrated that a data acquisition of 30 s achieved phase-matched µ-maps when using specialized CIRCUS data sampling and phase-matched µ-maps improved PETQ quantification significantly.

An Investigation of the Required MR Bone Attenuation Correction for Quantitative Whole-Body PET/MR Imaging Using Clinical NaF PET/CT Studies

Tissue-classification-based attenuation correction strategies have been previously proposed to correct for bone attenuation in PET/MR imaging and simulated using computed tomography. However, the complication of voxel averaging uniquely associated with bone has not been considered explicitly in the past. This study investigated the effect of voxel averaging between bone and soft tissue in attenuation images and determined how accurately bone must be detected in MR images in order to perform acceptable attenuation correction of PET data by using CT-simulated attenuation correction. We found out that treating bone as soft tissue caused a mean quantification difference of -9.9% ± 5.5% in all 119 bone lesions. There were no significant differences between lesions in the pelvis and the vertebrae. The nominal difference in lesions in the ribs was significantly lower, likely due to the spatial misregistration between the emission and attenuation images. Interestingly, a non-monotonic relationship between the bone imaging ability and the absolute PET quantification accuracy was observed, with the minimal quantification difference achieved at a BVF around 40% for skull lesions (2.6% ± 2.1%), and 30% for non-skull lesions (1.4% ± 1.1%) and all lesions (1.5% ± 1.3%). This study established that a bone classification sensitivity of approximately 30% BVF is required in order for MR-based attenuation correction methods to achieve optimal quantification in whole-body PET/MR studies. For this purpose, higher bone imaging ability of MR may not be necessary.

Commentary to Quantitative PET/MRI Evaluation and Application in Neurology

Introduction: With the high spatial resolution of magnetic resonance imaging (MRI) particularly for the soft tissue such as in brain and non-ion radiation involved, the integrative positron emission tomography (PET)/ MRI system is expected to provide almost equivalent image quality compared with PET alone or PET/CT system, and simultaneous MRI information that is not conventionally available [1]. PET-MRI opens new horizons in multi-parametric neuroimaging for clinical research that allows simultaneous imaging of multiple parametric changes such as blood flow and metabolism at the same time. While MRI could provide superior structural information, applying MRI anatomical priors to reduce the PET partial volume effect could be achieved to improve spatial resolution of PET images for clinical and research usages. Objectives: Further applications in brain tumors and cancer response monitoring have reported complementary and valid information that this relatively new modality could achieve based on newly developed techniques. Newer and better MRI-based attenuation correction (AC) such as zero TE (ZTE) for more bone-related tissue signal detection and faster reconstruction compared to UTE/Dixon has been reported [3-6]. Advanced MRI-based motion correction for PET image reconstruction, newer PET time of flight reconstruction and incorporation with compressed sensing techniques have offered attractive superior temporal and spatial resolutions for disease diagnosis and prevention [7-9]. We briefly review the applications of PET/MRI in neurology with two examples in Alzheimer’s disease and brain tumor cases in this commentary. Results: Numerous imaging studies in Alzheimer’s disease (AD) have been performed to study characteristic brain alterations including brain atrophy with earlier involvement of medial temporal lobe [10], brain hypo- perfusion in the posterior cingulate and parietal regions [11], reduced functional and structural connectivity in the default mode network (DMN) based on MRI findings [12] and hypo-metabolism based on PET imaging findings [13]. A regional coupling between metabolism of glucose (via PET kinetic analysis) and cerebral blood flow (CBF) had been reported with reductions post-caffeine compared to precaffeine conditions in controls [14], and in demented patients [15] based on separate scans. An evaluation of the real-time coupling of these two key imaging parameters with simultaneous acquisitions had not been performed before. For example, FDG metabolism had been found to increase twice as the energy brain activation needs (i.e., larger change in metabolism than change in CBF), and at a later time the unmetabolited fraction of the tracer was cleared off so that the tissue concentration of the tracer could reflect the metabolic rate [16,17]. Neurovascular coupling between neurochemistry (e.g., dopamine receptor occupancy) and hemodynamic change (e.g., cerebral blood volume) using simultaneous PET/fMRI acquisitions had been reported recently with similar temporal profile and dose response in non-human primates Conclusions: he article by Zhou [2] had evaluated the most up-to-date PET/MRI scanner performance with multiple functional and structural metrics together with application demonstrations. Voxel-wise analyses in the template space also showed the majority of brain voxels (>95%) with significant correlations (r>0.54, P<0.05) between PET/MRI with Dixon MR-based AC method and PET/CT based on SUVR (standard uptake value ratio, a quantitative metric of normalized PET tracer dosage uptake) choosing cerebellum as the reference region (Figure 1A). A tight coupling between PET/MRI PET image and PET/CT PET image with a slope of fitting close to 1 in the middle temporal gyrus (MTG) was demonstrated in (Figure 1B). Furthermore, the global mean difference based on SUVR between PET/MRI and PET/CT was small using Dixon MR-based AC and PET/CT (difference=4%), And our findings agree with currently accepted notion that the MRI-based AC could achieve comparable imaging quality to standard-CT AC, with ≤ 5% differences between the PET/MRI and PET/CT PET images. The integrated PET/ MRI images showed comparable image quality to stand-alone imaging modality (both MRI and PET) with the gains of simultaneous multiparametric acquisitions, reduced scan time and potential patient discomfort. Furthermore, tight correlation between blood flow and metabolism was found in several brain gray matter regions including the temporal lobe in patients.

Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI.

Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone is neglected. Ultrashort-echo-time or zero-echo-time (ZTE) pulse sequences can capture bone information. We propose the use of patient-specific multiparametric MRI consisting of Dixon MRI and proton-density-weighted ZTE MRI to directly synthesize pseudo-CT images with a deep learning model: we call this method ZTE and Dixon deep pseudo-CT (ZeDD CT). Methods: Twenty-six patients were scanned using an integrated 3-T time-of-flight PET/MRI system. Helical CT images of the patients were acquired separately. A deep convolutional neural network was trained to transform ZTE and Dixon MR images into pseudo-CT images. Ten patients were used for model training, and 16 patients were used for evaluation. Bone and soft-tissue lesions were identified, and the SUVmax was measured. The root-mean-squared error (RMSE) was used to compare the MR-based attenuation correction with the ground-truth CT attenuation correction. Results: In total, 30 bone lesions and 60 soft-tissue lesions were evaluated. The RMSE in PET quantification was reduced by a factor of 4 for bone lesions (10.24% for Dixon PET and 2.68% for ZeDD PET) and by a factor of 1.5 for soft-tissue lesions (6.24% for Dixon PET and 4.07% for ZeDD PET). Conclusion: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods.

Hybrid ZTE/Dixon MR-based attenuation correction for quantitative uptake estimation of pelvic lesions in PET/MRI.

This study introduces a new hybrid ZTE/Dixon MR-based attenuation correction (MRAC) method including bone density estimation for PET/MRI and quantifies the effects of bone attenuation on metastatic lesion uptake in the pelvis. Six patients with pelvic lesions were scanned using fluorodeoxyglucose (18F-FDG) in an integrated time-of-flight (TOF) PET/MRI system. For PET attenuation correction, MR imaging consisted of two-point Dixon and zero echo-time (ZTE) pulse sequences. A continuous-value fat and water pseudoCT was generated from a two-point Dixon MRI. Bone was segmented from the ZTE images and converted to Hounsfield units (HU) using a continuous two-segment piecewise linear model based on ZTE MRI intensity. The HU values were converted to linear attenuation coefficients (LAC) using a bilinear model. The bone voxels of the Dixon-based pseudoCT were replaced by the ZTE-derived bone to produce the hybrid ZTE/Dixon pseudoCT. The three different AC maps (Dixon, hybrid ZTE/Dixon, CTAC) were used to reconstruct PET images using a TOF-ordered subset expectation maximization algorithm with a point-spread function model. Metastatic lesions were separated into two classes, bone lesions and soft tissue lesions, and analyzed. The MRAC methods were compared using a root-mean-squared error (RMSE), where the registered CTAC was taken as ground truth. The RMSE of the maximum standardized uptake values (SUVmax ) is 11.02% and 7.79% for bone (N=6) and soft tissue lesions (N=8), respectively, using Dixon MRAC. The RMSE of SUVmax for these lesions is significantly reduced to 3.28% and 3.94% when using the new hybrid ZTE/Dixon MRAC. Additionally, the RMSE for PET SUVs across the entire pelvis and all patients are 8.76% and 4.18%, for the Dixon and hybrid ZTE/Dixon MRAC methods, respectively. A hybrid ZTE/Dixon MRAC method was developed and applied to pelvic regions in an integrated TOF PET/MRI, demonstrating improved MRAC. This new method included bone density estimation, through which PET quantification is improved.

Optimized MLAA for quantitative non-TOF PET/MR of the brain

For quantitative tracer distribution in positron emission tomography, attenuation correction is essential. In a hybrid PET/CT system the CT images serve as a basis for generation of the attenuation map, but in PET/MR, the MR images do not have a similarly simple relationship with the attenuation map. Hence attenuation correction in PET/MR systems is more challenging. Typically either of two MR sequences are used: the Dixon or the ultra-short time echo (UTE) techniques. However these sequences have some well-known limitations. In this study, a reconstruction technique based on a modified and optimized non-TOF MLAA is proposed for PET/MR brain imaging. The idea is to tune the parameters of the MLTR applying some information from an attenuation image computed from the UTE sequences and a T1w MR image. In this MLTR algorithm, an parameter is introduced and optimized in order to drive the algorithm to a final attenuation map most consistent with the emission data. Because the non-TOF MLAA is used, a technique to reduce the cross-talk effect is proposed. In this study, the proposed algorithm is compared to the common reconstruction methods such as OSEM using a CT attenuation map, considered as the reference, and OSEM using the Dixon and UTE attenuation maps. To show the robustness and the reproducibility of the proposed algorithm, a set of 204 [18F]FDG patients, 35 [11C]PiB patients and 1 [18F]FET patient are used. The results show that by choosing an optimized value of in MLTR, the proposed algorithm improves the results compared to the standard MR-based attenuation correction methods (i.e. OSEM using the Dixon or the UTE attenuation maps), and the cross-talk and the scale problem are limited.

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging

The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data.A total of 164 [18F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of values to CT Hounsfield Units (HU) to measure the density in bone voxels.The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within ±10% of PETCT, compared to 72% when using UTE.The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.

Quantitative Evaluation of Segmentation- and Atlas-Based Attenuation Correction for PET/MR on Pediatric Patients.

Pediatric imaging is regarded as a key application for combined PET/MR imaging systems. Because existing MR-based attenuation-correction methods were not designed specifically for pediatric patients, we assessed the impact of 2 potentially influential factors: inter- and intrapatient variability of attenuation coefficients and anatomic variability. Furthermore, we evaluated the quantification accuracy of 3 methods for MR-based attenuation correction without (SEGbase) and with bone prediction using an adult and a pediatric atlas (SEGwBONEad and SEGwBONEpe, respectively) on PET data of pediatric patients. The variability of attenuation coefficients between and within pediatric (5-17 y, n = 17) and adult (27-66 y, n = 16) patient collectives was assessed on volumes of interest (VOIs) in CT datasets for different tissue types. Anatomic variability was assessed on SEGwBONEad/pe attenuation maps by computing mean differences to CT-based attenuation maps for regions of bone tissue, lungs, and soft tissue. PET quantification was evaluated on VOIs with physiologic uptake and on 80% isocontour VOIs with elevated uptake in the thorax and abdomen/pelvis. Inter- and intrapatient variability of the bias was assessed for each VOI group and method. Statistically significant differences in mean VOI Hounsfield unit values and linear attenuation coefficients between adult and pediatric collectives were found in the lungs and femur. The prediction of attenuation maps using the pediatric atlas showed a reduced error in bone tissue and better delineation of bone structure. Evaluation of PET quantification accuracy showed statistically significant mean errors in mean standardized uptake values of -14% ± 5% and -23% ± 6% in bone marrow and femur-adjacent VOIs with physiologic uptake for SEGbase, which could be reduced to 0% ± 4% and -1% ± 5% using SEGwBONEpe attenuation maps. Bias in soft-tissue VOIs was less than 5% for all methods. Lung VOIs showed high SDs in the range of 15% for all methods. For VOIs with elevated uptake, mean and SD were less than 5% except in the thorax. The use of a dedicated atlas for the pediatric patient collective resulted in improved attenuation map prediction in osseous regions and reduced interpatient bias variation in femur-adjacent VOIs. For the lungs, in which intrapatient variation was higher for the pediatric collective, a patient- or group-specific attenuation coefficient might improve attenuation map accuracy. Mean errors of -14% and -23% in bone marrow and femur-adjacent VOIs can affect PET quantification in these regions when bone tissue is ignored.

Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging

The joint maximum-likelihood reconstruction of activity and attenuation (MLAA) for emission-based attenuation correction has regained attention since the advent of time-of-flight PET/MR imaging. Recently, we improved the performance of the MLAA algorithm using an MR imaging-constrained gaussian mixture model (GMM). In this study, we compare the performance of our proposed algorithm with standard 4-class MR-based attenuation correction (MRAC) implemented on commercial systems. Five head and neck (18)F-FDG patients were scanned on PET/MR imaging and PET/CT scanners. Dixon fat and water MR images were registered to CT images. MRAC maps were derived by segmenting the MR images into 4 tissue classes and assigning predefined attenuation coefficients. For MLAA-GMM, MR images were segmented into known tissue classes, including fat, soft tissue, lung, background air, and an unknown MR low-intensity class encompassing cortical bones, air cavities, and metal artifacts. A coregistered bone probability map was also included in the unknown tissue class. Finally, the GMM prior was constrained over known tissue classes of attenuation maps using unimodal gaussians parameterized over a patient population. The results showed that the MLAA-GMM algorithm outperformed the MRAC method by differentiating bones from air gaps and providing more accurate patient-specific attenuation coefficients of soft tissue and lungs. It was found that the MRAC and MLAA-GMM methods resulted in average standardized uptake value errors of -5.4% and -3.5% in the lungs, -7.4% and -5.0% in soft tissues/lesions, and -18.4% and -10.2% in bones, respectively. The proposed MLAA algorithm is promising for accurate derivation of attenuation maps on time-of-flight PET/MR systems.

MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units

AimMR-based correction for photon attenuation in PET/MRI remains challenging, particularly for neurological applications requiring quantitation of data. Existing methods are either not sufficiently accurate or are limited by the computation time required. The goal of this study was to develop an MR-based attenuation correction method that accurately separates bone tissue from air and provides continuous-valued attenuation coefficients for bone. Materials and methodsPET/MRI and CT datasets were obtained from 98 subjects (mean age [±SD]: 66yrs [±9.8], 57 females) using an IRB-approved protocol and with informed consent. Subjects were injected with 352±29MBq of 18F-Florbetapir tracer, and PET acquisitions were begun either immediately or 50min after injection. CT images of the head were acquired separately using a PET/CT system. Dual echo ultrashort echo-time (UTE) images and two-point Dixon images were acquired. Regions of air were segmented via a threshold of the voxel-wise multiplicative inverse of the UTE echo 1 image. Regions of bone were segmented via a threshold of the R2* image computed from the UTE echo 1 and UTE echo 2 images. Regions of fat and soft tissue were segmented using fat and water images decomposed from the Dixon images. Air, fat, and soft tissue were assigned linear attenuation coefficients (LACs) of 0, 0.092, and 0.1cm−1, respectively. LACs for bone were derived from a regression analysis between corresponding R2* and CT values. PET images were reconstructed using the gold standard CT method and the proposed CAR-RiDR method. ResultsThe RiDR segmentation method produces mean Dice coefficient±SD across subjects of 0.75±0.05 for bone and 0.60±0.08 for air. The CAR model for bone LACs greatly improves accuracy in estimating CT values (28.2%±3.0 mean error) compared to the use of a constant CT value (46.9%±5.8, p<10−6). Finally, the CAR-RiDR method provides a low whole-brain mean absolute percent-error (MAPE±SD) in PET reconstructions across subjects of 2.55%±0.86. Regional PET errors were also low and ranged from 0.88% to 3.79% in 24 brain ROIs. ConclusionWe propose an MR-based attenuation correction method (CAR-RiDR) for quantitative PET neurological imaging. The proposed method employs UTE and Dixon images and consists of two novel components: 1) accurate segmentation of air and bone using the inverse of the UTE1 image and the R2* image, respectively and 2) estimation of continuous LAC values for bone using a regression between R2* and CT-Hounsfield units. From our analysis, we conclude that the proposed method closely approaches (<3% error) the gold standard CT-scaled method in PET reconstruction accuracy.

New SPM8-based MRAC method for simultaneous PET/MR brain images: comparison with state-of-the-art non-rigid registration methods

We describe a new MR-based attenuation correction (MRAC) method for neurological studies performed using integrated PET/MR scanners. The method, combining the advantages of image segmentation and atlas-based approaches to generate a high-resolution template, is based on the widely available SPM8 software and provides robust and accurate linear attenuation coefficients (LACs) for head while requiring minimal user interaction. Atlas generation: 3T MR and CT images from 15 glioblastoma subjects were used to generate the high-resolution atlas. MR images were segmented into 6 tissue classes: GM, WM, CSF, soft tissue, bone and air)[1]. Tissue classes were then coregistered using an iterative diffeomorphic image registration algorithm [2] to form the template. Atlas validation: The template was validated on 16 subjects. SyN [3] and IRTK [4], considered state-of-the-art for non-rigid image registration[5], were used for comparison. Final attenuation maps were created from the warped CT atlas following [6]. PET images were then reconstructed using the proposed methods as well as the manufacturer’s built-in method (dual-echo Dixon-VIBE sequence) [7] and compared to the gold standard CT-based attenuation correction (CTAC). The qualitative and quantitative analysis of the attenuation maps revealed that the SPM8-based method produces very robust results (Figure ​(Figure1).1). In terms of the PET data quantification, we observed improvements of > 70% compared to the VIBE-based method (Table ​(Table11 and Figure ​Figure2).2). When compared to SyN-based image registration, the SPM8 approach showed improved global results on the brain area (Figures ​(Figures11 and ​and22). Figure 1 Comparison of LACs from a validation subject for our proposed method (A), the SyN method (B) and the manufacturer’s built-in Dixon method (C) to the gold standard CTAC (D). Image differences with respect to the gold standard CTAC of our method ... Table 1 Summary of voxel- and ROI-based results between our method (atlas) and the current manufacturer’s method (Dixon) Figure 2 PET images from a validation subject reconstructed with our proposed method (A), with the SyN method (B) and with the manufacturer’s built-in Dixon method (C), compared with the gold standard CTAC (D). Relative changes (in % with respect to gold ... We presented a new MRAC technique for brain images acquired on simultaneous PET/MR scanners. The new approach relies on segmentation- and atlas-based features to provide robust and more accurate LACs than using state-of-art non-rigid image registration while avoiding sophisticated user input or interaction.

MR constrained simultaneous reconstruction of activity and attenuation maps in brain TOF-PET/MR imaging.

The maximum likelihood estimation of attenuation and activity (MLAA) algorithm has been proposed to jointly estimate activity and attenuation from emission data only. Salomon et al employed the MLAA to estimate activity and attenuation from time-of-flight PET data with spatial MR prior information on attenuation. Recently, we proposed a novel algorithm to impose both spatial and statistical constraints on attenuation estimation within the MLAA algorithm using Dixon MR images and a constrained Gaussian mixture model (GMM). In this study, we compare the proposed algorithm with MLAA and MLAA_Salomon in brain TOF-PET/MR imaging. A clinical FDG head PET/CT/MR dataset was used to simulate a 40M-count PET data acquisition with TOF resolution of 580 ps. In MLAA_GMM, Dixon MR images are segmented into outside air, fat/soft tissue classes and an MR low-intensity class corresponding to air cavities, bone and susceptibility artifacts. A mixture of 3 Gaussians (air, fat/soft tissue and bone) was used for the low-intensity class, while uni-modal Gaussians were used for other classes. Bias performance of the algorithms was evaluated against CT-based and 4-class MR-based attenuation correction methods. Region-of-interest analysis of our simulations showed that the 4-class and MLAA algorithms result in –4.9% and –5.8% bias in soft tissue and –18.5% and –12.4% bias in bone, respectively. Inclusion of MR constrains in MLAA_Salomon and MLAA_GMM resulted in –6.6% and –4.1% bias in soft tissue and –16.1% and –13.0% in bone, respectively. It was found that the performance of MLAA_Salomon depends highly on the robustness of MR segmentation, particularly at air/bone interfaces. The proposed approach effectively exploits MR prior information and produces attenuation maps that are spatially and statistically more consistent with true attenuation maps.

Comparison of MR-based attenuation correction and CT-based attenuation correction of whole-body PET/MR imaging

The objective of this study was to evaluate the performance of the built-in MR-based attenuation correction (MRAC) included in the combined whole-body Ingenuity TF PET/MR scanner and compare it to the performance of CT-based attenuation correction (CTAC) as the gold standard. Included in the study were 26 patients who underwent clinical whole-body FDG PET/CT imaging and subsequently PET/MR imaging (mean delay 100 min). Patients were separated into two groups: the alpha group (14 patients) without MR coils during PET/MR imaging and the beta group (12 patients) with MR coils present (neurovascular, spine, cardiac and torso coils). All images were coregistered to the same space (PET/MR). The two PET images from PET/MR reconstructed using MRAC and CTAC were compared by voxel-based and region-based methods (with ten regions of interest, ROIs). Lesions were also compared by an experienced clinician. Body mass index and lung density showed significant differences between the alpha and beta groups. Right and left lung densities were also significantly different within each group. The percentage differences in uptake values using MRAC in relation to those using CTAC were greater in the beta group than in the alpha group (alpha group -0.2 ± 33.6%, R(2) = 0.98, p < 0.001; beta group 10.31 ± 69.86%, R(2) = 0.97, p < 0.001). In comparison to CTAC, MRAC led to underestimation of the PET values by less than 10% on average, although some ROIs and lesions did differ by more (including the spine, lung and heart). The beta group (imaged with coils present) showed increased overall PET quantification as well as increased variability compared to the alpha group (imaged without coils). PET data reconstructed with MRAC and CTAC showed some differences, mostly in relation to air pockets, metallic implants and attenuation differences in large bone areas (such as the pelvis and spine) due to the segmentation limitation of the MRAC method.

MR-Based Attenuation Correction Methods for Improved PET Quantification in Lesions Within Bone and Susceptibility Artifact Regions

Hybrid PET/MR systems have recently entered clinical practice. Thus, the accuracy of MR-based attenuation correction in simultaneously acquired data can now be investigated. We assessed the accuracy of 4 methods of MR-based attenuation correction in lesions within soft tissue, bone, and MR susceptibility artifacts: 2 segmentation-based methods (SEG1, provided by the manufacturer, and SEG2, a method with atlas-based susceptibility artifact correction); an atlas- and pattern recognition-based method (AT&PR), which also used artifact correction; and a new method combining AT&PR and SEG2 (SEG2wBONE). Attenuation maps were calculated for the PET/MR datasets of 10 patients acquired on a whole-body PET/MR system, allowing for simultaneous acquisition of PET and MR data. Eighty percent iso-contour volumes of interest were placed on lesions in soft tissue (n = 21), in bone (n = 20), near bone (n = 19), and within or near MR susceptibility artifacts (n = 9). Relative mean volume-of-interest differences were calculated with CT-based attenuation correction as a reference. For soft-tissue lesions, none of the methods revealed a significant difference in PET standardized uptake value relative to CT-based attenuation correction (SEG1, -2.6% ± 5.8%; SEG2, -1.6% ± 4.9%; AT&PR, -4.7% ± 6.5%; SEG2wBONE, 0.2% ± 5.3%). For bone lesions, underestimation of PET standardized uptake values was found for all methods, with minimized error for the atlas-based approaches (SEG1, -16.1% ± 9.7%; SEG2, -11.0% ± 6.7%; AT&PR, -6.6% ± 5.0%; SEG2wBONE, -4.7% ± 4.4%). For lesions near bone, underestimations of lower magnitude were observed (SEG1, -12.0% ± 7.4%; SEG2, -9.2% ± 6.5%; AT&PR, -4.6% ± 7.8%; SEG2wBONE, -4.2% ± 6.2%). For lesions affected by MR susceptibility artifacts, quantification errors could be reduced using the atlas-based artifact correction (SEG1, -54.0% ± 38.4%; SEG2, -15.0% ± 12.2%; AT&PR, -4.1% ± 11.2%; SEG2wBONE, 0.6% ± 11.1%). For soft-tissue lesions, none of the evaluated methods showed statistically significant errors. For bone lesions, significant underestimations of -16% and -11% occurred for methods in which bone tissue was ignored (SEG1 and SEG2). In the present attenuation correction schemes, uncorrected MR susceptibility artifacts typically result in reduced attenuation values, potentially leading to highly reduced PET standardized uptake values, rendering lesions indistinguishable from background. While AT&PR and SEG2wBONE show accurate results in both soft tissue and bone, SEG2wBONE uses a two-step approach for tissue classification, which increases the robustness of prediction and can be applied retrospectively if more precision in bone areas is needed.

Preclinical Evaluation of MR Attenuation Correction Versus CT Attenuation Correction on a Sequential Whole-Body MR/PET Scanner

The application of attenuation correction for combined magnetic resonance/positron emission tomography (MR/PET) systems is still a major challenge for accurate quantitative PET. Computed tomographic attenuation correction (CTAC) is the current clinical standard for PET/computed tomographic (CT) scans. Magnetic resonance, unlike CT, has no direct information about photon attenuation but, rather, proton densities. On combined MR/PET scanners, MR-based attenuation correction (MRAC) consists of assigning empirical attenuation coefficients to MR signal intensities. The objective of the current study was to evaluate the MRAC implemented on the combined MR/PET scanner versus the CTAC with the same PET data in an animal model. Acquisition was performed using a clinically approved sequential MR/PET scanner (Philips Ingenuity TF). Computed tomographic and MR/PET images of 20 New Zealand White rabbits were retrospectively analyzed. The animals were positioned on a customized animal bed to avoid movement between the CT and MR/PET scanners. Positron emission tomographic images from both methods (MRAC and CTAC) were generated. Voxel-by-voxel and region-of-interest (ROI) analyses were performed to determine differences in standardized uptake values (SUV). Regions of interest were drawn on the coregistered CT images for the aorta, liver, kidney, spine, and soft tissue (muscle) and superimposed on the PET images. The voxel-by-voxel comparison of PET showed excellent correlation between MRAC and CTAC SUV values (R = 0.99; P < 0.0001). The mean of the difference of SUVs between all respective MRAC and CTAC voxels was -0.94% (absolute difference [AD] ± SD, -0.06 ± 0.30), confirming slight underestimation of MRAC. The ROI-based comparison similarly showed that MRAC SUV values were underestimated compared with CTAC SUV values. The mean difference between MRAC and CTAC for all ROIs was 10.8% (AD, -0.08 ± 0.06; R = 0.99; P < 0.0001) and -9.7% (AD, -0.15 ± 0.12; R = 0.99; P < 0.0001) for the SUV mean (SUV mean) and the SUV maximum (SUV max), respectively. The highest differences were found in the spine (SUV mean -26.1% [-0.11]) and areas close to large bones such as the back muscles (SUV mean, -16.8% [-0.04]). In this study, we have compared MRAC and CTAC methods for PET attenuation correction in an animal model. We have confirmed that the MRAC method implemented on a sequential MR/PET scanner underestimates PET values by less than 10% in most regions, except the areas containing or close to large bone structures such as the spine or the back muscles. Bone segmentation is therefore suggested to be included in the MR attenuation map to minimize the quantification error of MRAC methods compared with the clinical standard CTAC. Further clinical studies need to be carried out to validate the clinical use of MRAC.

PET/MR imaging of the pelvis in the presence of endoprostheses: reducing image artifacts and increasing accuracy through inpainting

In combined whole-body PET/MR, attenuation correction (AC) is performed indirectly using the available MR image information and subsequent segmentation. Implant-induced susceptibility artifacts and subsequent signal voids may challenge MR-based AC (MR-AC). We evaluated the accuracy of MR-AC in PET/MR in patients with metallic endoprostheses, and propose a clinically feasible correction method. We selected patients with uni- or bilateral endoprostheses from 61 consecutive referrals for whole-body PET/MR imaging (mMR; Siemens Healthcare). Simultaneous whole-body PET/MR imaging was performed at 120min after injection of about 300MBq [(18)F]FDG. MR-AC was performed using (1) original MR images and subsequent Dixon water-fat segmentation, (2) as method 1 with implant-induced signal voids filled with soft tissue, (3) as method 2 with superimposed coregistered endoprostheses from the CT scan, and (4) as method 1 with implant-induced signal voids filled with metal. Following MR-AC (methods 1-4) PET emission images were reconstructed on 344 × 344 matrices using attenuation-weighted OSEM (three iterations, 21 subsets, 4mm gaussian). Maximum body-weight normalized standardized uptake values (SUVmax) were obtained for both hips. Mean SUV (SUVmean) in homogeneous reference regions in the gluteal muscle and bladder following MR-AC (methods 1-4) are also reported. In total, four patients presented with endoprostheses, unilateral in two and bilateral in two. The fraction of voxels in MR images affected by the implant was at least twice that of the voxels representing the actual implants. MR-AC using methods 2 and 3 recovered the FDG distribution pattern compared to uncorrected PET images and method 1, while method 4 resulted in severe overestimation of FDG uptake (>460% SUVmax). When compared to method 1, relative changes in SUVmean in the reference regions from method 2 and 3 were generally small albeit not correlated with the fraction of the attenuation image affected by implant-induced artifacts. Endoprostheses cause PET/MR artifacts that exceed the volume occupied by the implants, and bias PET quantification. Artifacts and bias can be corrected by semiautomated inpainting with soft tissue with a single composition prior to MR-AC, thus restoring quantitative activity distribution.