Evaluation of Atlas-Based Attenuation Correction for Integrated PET/MR in Human Brain: Application of a Head Atlas and Comparison to True CT-Based Attenuation Correction

Strategies for deep learning-based attenuation and scatter correction of brain 18 F-FDG PET images in the image domain

BACKGROUND

Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging.

PURPOSE

In this study, different input settings were considered in the model training to investigate deep learning-based AC in the image space.

METHODS

Three different deep learning methods were developed for direct AC in the image space: (i) use of non-attenuation-corrected PET images as input (NonAC-PET), (ii) use of attenuation-corrected PET images with a simple two-class AC map (composed of soft-tissue and background air) obtained from NonAC-PET images (PET segmentation-based AC [SegAC-PET]), and (iii) use of both NonAC-PET and SegAC-PET images in a Double-Channel fashion to predict ground truth attenuation corrected PET images with Computed Tomography images (CTAC-PET). Since a simple two-class AC map (generated from NonAC-PET images) can easily be generated, this work assessed the added value of incorporating SegAC-PET images into direct AC in the image space. A 4-fold cross-validation scheme was adopted to train and evaluate the different models based using 80 brain 18 F-Fluorodeoxyglucose PET/CT images. The voxel-wise and region-wise accuracy of the models were examined via measuring the standardized uptake value (SUV) quantification bias in different regions of the brain.

RESULTS

The overall root mean square error (RMSE) for the Double-Channel setting was 0.157 ± 0.08 SUV in the whole brain region, while RMSEs of 0.214 ± 0.07 and 0.189 ± 0.14 SUV were observed in NonAC-PET and SegAC-PET models, respectively. A mean SUV bias of 0.01 ± 0.26% was achieved by the Double-Channel model regarding the activity concentration in cerebellum region, as opposed to 0.08 ± 0.28% and 0.05 ± 0.28% SUV biases for the network that uniquely used NonAC-PET or SegAC-PET as input, respectively. SegAC-PET images with an SUV bias of -1.15 ± 0.54%, served as a benchmark for clinically accepted errors. In general, the Double-Channel network, relying on both SegAC-PET and NonAC-PET images, outperformed the other AC models.

CONCLUSION

Since the generation of two-class AC maps from non-AC PET images is straightforward, the current study investigated the potential added value of incorporating SegAC-PET images into a deep learning-based direct AC approach. Altogether, compared with models that use only NonAC-PET and SegAC-PET images, the Double-Channel deep learning network exhibited superior attenuation correction accuracy.

The quest for multifunctional and dedicated PET instrumentation with irregular geometries

We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging. We will also review challenges and possible artifacts by image reconstruction algorithms for PET scanners with irregular geometries, such as non-cylindrical and partial angular coverage geometries and how they can be addressed. Then, we attempt to address some open issues about cost/benefits analysis of dedicated PET scanners, how far are the theoretical conceptual designs from the market/clinic, and strategies to reduce fabrication cost without compromising performance.

Comparison of quantitative [11C]PE2I brain PET studies between an integrated PET/MR and a stand-alone PET system

PET/MR systems demanded great efforts for accurate attenuation correction (AC) but differences in technology, geometry and hardware attenuation may also affect quantitative results. Dedicated PET systems using transmission-based AC are regarded as the gold standard for quantitative brain PET. The study aim was to investigate the agreement between quantitative PET outcomes from a PET/MR scanner against a stand-alone PET system. Nine patients with Parkinsonism underwent two 80-min dynamic PET scans with the dopamine transporter ligand [11C]PE2I. Images were reconstructed with resolution-matched settings using 68Ge-transmission (stand-alone PET), and zero-echo-time MR (PET/MR) scans for AC. Non-displaceable binding potential (BPND) and relative delivery (R1) were evaluated using volumes of interest and voxel-wise analysis. Correlations between systems were high (r ≥ 0.85) for both quantitative outcome parameters in all brain regions. Striatal BPND was significantly lower on PET/MR than on stand-alone PET (-7%). R1 was significantly overestimated in posterior cortical regions (9%) and underestimated in striatal (-9%) and limbic areas (-6%). The voxel-wise evaluation revealed that the MR-safe headphones caused a negative bias in both parametric BPND and R1 images. Additionally, a significant positive bias of R1 was found in the auditory cortex, most likely due to the acoustic background noise during MR imaging. The relative bias of the quantitative [11C]PE2I PET data acquired from a SIGNA PET/MR system was in the same order as the expected test-retest reproducibility of [11C]PE2I BPND and R1, compared to a stand-alone ECAT PET scanner. MR headphones and background noise are potential sources of error in functional PET/MR studies.

Deep-learning-based methods of attenuation correction for SPECT and PET

Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.

The performance of machine learning approaches for attenuation correction of PET in neuroimaging: A meta-analysis

PURPOSE

This systematic review provides a consensus on the clinical feasibility of machine learning (ML) methods for brain PET attenuation correction (AC). Performance of ML-AC were compared to clinical standards.

METHODS

Two hundred and eighty studies were identified through electronic searches of brain PET studies published between January 1, 2008, and August 1, 2022. Reported outcomes for image quality, tissue classification performance, regional and global bias were extracted to evaluate ML-AC performance. Methodological quality of included studies and the quality of evidence of analysed outcomes were assessed using QUADAS-2 and GRADE, respectively.

RESULTS

A total of 19 studies (2371 participants) met the inclusion criteria. Overall, the global bias of ML methods was 0.76 ± 1.2%. For image quality, the relative mean square error (RMSE) was 0.20 ± 0.4 while for tissues classification, the Dice similarity coefficient (DSC) for bone/soft tissue/air were 0.82 ± 0.1 / 0.95 ± 0.03 / 0.85 ± 0.14.

CONCLUSIONS

In general, ML-AC performance is within acceptable limits for clinical PET imaging. The sparse information on ML-AC robustness and its limited qualitative clinical evaluation may hinder clinical implementation in neuroimaging, especially for PET/MRI or emerging brain PET systems where standard AC approaches are not readily available.

DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants

Purpose

Conventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[¹⁸F]fluoroethyl)-L-tyrosine ([¹⁸F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [¹⁸F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants.

Methods

The MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [¹⁸F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC.

Results

Ninety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method.

Conclusion

We evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.

Attenuation Correction Using Template PET Registration for Brain PET: A Proof-of-Concept Study

NeuroLF is a dedicated brain PET system with an octagonal prism shape housed in a scanner head that can be positioned around a patient's head. Because it does not have MR or CT capabilities, attenuation correction based on an estimation of the attenuation map is a crucial feature. In this article, we demonstrate this method on [¹⁸F]FDG PET brain scans performed with a low-resolution proof of concept prototype of NeuroLF called BPET. We perform an affine registration of a template PET scan to the uncorrected emission image, and then apply the resulting transform to the corresponding template attenuation map. Using a whole-body PET/CT system as reference, we quantitively show that this method yields comparable image quality (0.893 average correlation to reference scan) to using the reference µ-map as obtained from the CT scan of the imaged patient (0.908 average correlation). We conclude from this initial study that attenuation correction using template registration instead of a patient CT delivers similar results and is an option for patients undergoing brain PET.

Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.

Unified Deep Learning-Based Mouse Brain MR Segmentation: Template-Based Individual Brain Positron Emission Tomography Volumes-of-Interest Generation Without Spatial Normalization in Mouse Alzheimer Model

Although skull-stripping and brain region segmentation are essential for precise quantitative analysis of positron emission tomography (PET) of mouse brains, deep learning (DL)-based unified solutions, particularly for spatial normalization (SN), have posed a challenging problem in DL-based image processing. In this study, we propose an approach based on DL to resolve these issues. We generated both skull-stripping masks and individual brain-specific volumes-of-interest (VOIs—cortex, hippocampus, striatum, thalamus, and cerebellum) based on inverse spatial normalization (iSN) and deep convolutional neural network (deep CNN) models. We applied the proposed methods to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer’s disease. Eighteen mice underwent T2-weighted MRI and ¹⁸F FDG PET scans two times, before and after the administration of human immunoglobulin or antibody-based treatments. For training the CNN, manually traced brain masks and iSN-based target VOIs were used as the label. We compared our CNN-based VOIs with conventional (template-based) VOIs in terms of the correlation of standardized uptake value ratio (SUVR) by both methods and two-sample t-tests of SUVR % changes in target VOIs before and after treatment. Our deep CNN-based method successfully generated brain parenchyma mask and target VOIs, which shows no significant difference from conventional VOI methods in SUVR correlation analysis, thus establishing methods of template-based VOI without SN.

Neurovascular, muscle, and skin changes on [18F]FDG PET/MRI in complex regional pain syndrome of the foot: A Prospective Clinical Study

Objective

The goal of this study is to demonstrate the feasibility of simultaneous [¹⁸F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) and magnetic resonance imaging (MRI) for noninvasive visualization of muscular, neurovascular, and skin changes secondary to complex regional pain syndrome (CRPS).

Subjects

Seven adult patients with CRPS of the foot and seven healthy adult controls participated in our [¹⁸F]FDG PET/MRI study.

Methods

All participants received whole-body PET/MRI scans 1 hour after the injection of 370MBq [¹⁸F]FDG. Resulting PET/MRI images were reviewed by two radiologists. Metabolic and anatomic abnormalities identified, were grouped into muscular, neurovascular, and skin lesions. The [¹⁸F]FDG uptake of each lesion was compared with that of corresponding areas in controls using a Mann-Whitney U-test.

Results

On PET images, muscular abnormalities were found in five patients, neurovascular abnormalities in four patients, and skin abnormalities in two patients. However, on MRI images, no muscular abnormalities were detected. Neurovascular abnormalities and skin abnormalities in the affected limb were identified on MRI in one and two patients, respectively. The difference in [¹⁸F]FDG uptake between the patients and the controls was significant in muscle (P = .018) and neurovascular bundle (P = .0005).

Conclusions

The increased uptake of [¹⁸F]FDG in the symptomatic areas likely reflects the increased metabolism due to the inflammatory response causing pain. Therefore, our approach combining metabolic [¹⁸F]FDG PET and anatomic MR imaging may offer noninvasive monitoring of the distribution and progression of inflammatory changes associated with CRPS.

Abnormal [18F]FDG PET/MRI findings in paraspinal structures of patients with suspected cerebrospinal fluid leak

A combination of magnetic resonance imaging (MRI), computed tomography (CT), and radionuclide cisternography are typically used to locate a cerebrospinal fluid (CSF) leak. However, the site of leakage cannot be determined, making treatment more difficult. Therefore, more sensitive imaging tools are needed. A whole-body [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/MRI was conducted on six patients with suspected CSF leak and the resulting images were reviewed in comparison with those from six healthy controls. Paraspinal regions of focally increased uptake of [18F]FDG were quantified using maximum standardized uptake values (SUVmax) and compared to the SUVmax of corresponding regions in the healthy controls. All six patients with suspected CSF leak showed paraspinal regions of significantly greater [18F]FDG uptake compared to the corresponding areas in controls (P < 0.05). Two patients treated with local injections (epidural blood patches and/or epidural fibrin patches) on the site of abnormal PET/MRI findings reported temporary but significant improvement in symptoms. Our results suggest [18F]FDG PET/MRI is sensitive to abnormalities potentially due to suspected CSF leak, which are not necessarily visible on conventional MRI alone or by the standard-of-care imaging methods.

Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

Hippocampal subfield imaging and fractional anisotropy show parallel changes in Alzheimer's disease tau progression using simultaneous tau-PET/MRI at 3T

INTRODUCTION

Alzheimer's disease (AD) is the most common form of dementia, characterized primarily by abnormal aggregation of two proteins, tau and amyloid beta. We assessed tau pathology and white matter connectivity changes in subfields of the hippocampus simultaneously in vivo in AD.

METHODS

Twenty-four subjects were scanned using simultaneous time-of-flight 18F-PI-2620 tau positron emission tomography/3-Tesla magnetic resonance imaging and automated segmentation.

RESULTS

We observed extensive tau elevation in the entorhinal/perirhinal regions, intermediate tau elevation in cornu ammonis 1/subiculum, and an absence of tau elevation in the dentate gyrus, relative to controls. Diffusion tensor imaging showed parahippocampal gyral fractional anisotropy was lower in AD and mild cognitive impairment compared to controls and strongly correlated with early tau accumulation in the entorhinal and perirhinal cortices.

DISCUSSION

This study demonstrates the potential for quantifiable patterns of 18F-PI2620 binding in hippocampus subfields, accompanied by diffusion and volume metrics, to be valuable markers of AD.

A Path to Qualification of PET/MR Scanners for Multicenter Brain Imaging Studies: Evaluation of MR-based Attenuation Correction Methods Using a Patient Phantom

PET/MRI scanners cannot be qualified in the manner adopted for hybrid PET/CT devices. The main hurdle with qualification in PET/MRI is that attenuation correction (AC) cannot be adequately measured in conventional PET phantoms because of the difficulty in converting the MR images of the physical structures (e.g., plastic) into electron density maps. Over the last decade, a plethora of novel MRI-based algorithms has been developed to more accurately derive the attenuation properties of the human head, including the skull. Although promising, none of these techniques has yet emerged as an optimal and universally adopted strategy for AC in PET/MRI. In this work, we propose a path for PET/MRI qualification for multicenter brain imaging studies. Specifically, our solution is to separate the head AC from the other factors that affect PET data quantification and use a patient as a phantom to assess the former. The emission data collected on the integrated PET/MRI scanner to be qualified should be reconstructed using both MRI- and CT-based AC methods, and whole-brain qualitative and quantitative (both voxelwise and regional) analyses should be performed. The MRI-based approach will be considered satisfactory if the PET quantification bias is within the acceptance criteria specified here. We have implemented this approach successfully across 2 PET/MRI scanner manufacturers at 2 sites.

Multi-Atlas MRI-Based Striatum Segmentation for 123I-FP-CIT SPECT (DAT-SPECT) Compared With the Bolt Method and SPECT-Atlas-Based Segmentation Method Toward the Accurate Diagnosis of Parkinson's Disease/Syndrome

Aims: This study aimed to analyze the performance of multi-atlas MRI-based parcellation for ¹²³I-FP-CIT SPECT (DAT-SPECT) in healthy volunteers. The proposed method was compared with the SPECT-atlas-based and Bolt methods. ¹⁸F-FE-PE2I-PET (DAT-PET) was used as a reference.

Methods: Thirty healthy subjects underwent DAT-SPECT, DAT-PET, and 3D-T1WI-MRI. We calculated the striatum uptake ratio (SUR/SBR), caudate uptake ratio (CUR), and putamen uptake ratio (PUR) for DAT-SPECT using the multi-atlas MRI-based method, SPECT-atlas-based method, and Bolt method. In the multi-atlas MRI-based method, the cerebellum, occipital cortex, and whole-brain were used as reference regions. The correlation of age with DAT-SPECT activity and the correlations of SUR/SBR, CUR, and PUR between DAT-SPECT and DAT-PET were calculated by each of the three methods.

Results: The correlation between age and SUR/SBR for DAT-SPECT based on the multi-atlas MRI-based method was comparable to that based on the SPECT-atlas-based method (r = −0.441 to −0.496 vs. −0.488). The highest correlation between DAT-SPECT and DAT-PET was observed using the multi-atlas MRI-based method with the occipital lobe defined as the reference region compared with the SPECT-atlas-based and Bolt methods (SUR, CUR, and PUR: 0.687, 0.723, and 0.676 vs. 0.698, 0.660, and 0.616 vs. 0.655).

Conclusion: Multi-atlas MRI-based parcellation with the occipital lobe defined as the reference region was at least comparable to the clinical methods.

Evaluation of Arterial Spin Labeling MRI-Comparison with 15O-Water PET on an Integrated PET/MR Scanner

Cerebral blood flow (CBF) measurements are of high clinical value and can be acquired non-invasively with no radiation exposure using pseudo-continuous arterial spin labeling (ASL). The aim of this study was to evaluate accordance in resting state CBF between ASL (CBF_ASL) and ¹⁵O-water positron emission tomography (PET) (CBF_PET) acquired simultaneously on an integrated 3T PET/MR system. The data comprised ASL and dynamic ¹⁵O-water PET data with arterial blood sampling of eighteen subjects (eight patients with focal epilepsy and ten healthy controls, age 21 to 61 years). ¹⁵O-water PET parametric CBF images were generated using a basis function implementation of the single tissue compartment model. Cortical and subcortical regions were automatically segmented using Freesurfer. Average CBF_ASL and CBF_PET in grey matter were 60 ± 20 and 75 ± 22 mL/100 g/min respectively, with a relatively high correlation (r = 0.78, p < 0.001). Bland-Altman analysis revealed poor agreement (bias = −15 mL/100 g/min, lower and upper limits of agreements = −16 and 45 mL/100 g/min, respectively) with a negative relationship. Accounting for the negative relationship, the width of the limits of agreement could be narrowed from 61 mL/100 g/min to 35 mL/100 g/min using regression-based limits of agreements. Although a high correlation between CBF_ASL and CBF_PET was found, the agreement in absolute CBF values was not sufficient for ASL to be used interchangeably with ¹⁵O-water PET.

Quantitative PET in the 2020s: a roadmap

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning

PURPOSE

Synthetic CT generation is the focus of many studies, however, only models on data with the same dataset were tested. Therefore, how well the trained model will work for data from different hospitals and MR protocols is still unknown. In this study, we addressed the model generalization problem for the MR-to-CT conversion task.

METHODS

Brain T2 MR and corresponding CT images were collected from one hospital and brain T1-FLAIR, T1-POST MR, and corresponding CT images were collected from another hospital. To investigate the model's generalizability ability, four potential solutions were proposed: source model, target model, combined model, and adapted model. All models were trained using the CycleGAN network. The source model was trained with a source domain dataset from scratch and tested with a target domain dataset. The target model was trained with a target domain dataset and tested with a target domain dataset. The combined model was trained with both source domain and target domain datasets, and tested with the target domain dataset. The adapted model used a transfer learning strategy to train a CycleGAN model with a source domain dataset and retrain the pre-trained model with a target domain dataset. MAE, RMSE, PSNR, and SSIM were used to quantitatively evaluate model performance on a target domain dataset.

RESULTS

The adapted model achieved best quantitative results of 74.56±8.61, 193.18±17.98, 28.30±0.83, and 0.84±0.01 for MAE, RMSE, PSNR, and SSIM using the T1-FLAIR dataset and 74.89±15.64, 195.73±31.29, 27.72±1.43, and 0.83±0.04 for MAE, RMSE, PSNR, and SSIM using the T1-POST dataset. The source model had the poorest performance.

CONCLUSIONS

This work indicates high generalization ability to generate synthetic CT images from small training datasets of MR images using pre-trained CycleGAN. The quantitative results of the test data, including different scanning protocols and different acquisition centers, indicated the proof of this concept.

Evaluation of a model-based attenuation correction method on whole-body 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging

The bone cannot be evaluated using magnetic resonance attenuation correction (MRAC) with the Dixon sequence. To solve this issue, the present study aimed to evaluate model-based AC for whole-body 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) by creating bone segmentation. We analyzed and evaluated the data of 31 consecutive patients. The Biograph mMR (Siemens Healthcare) was used for clinical whole-body ¹⁸F-FDG PET/MRI with the conventional MRAC method, and OSIRIX MD software was used to analyze the images. After the examination, the new model-based post-processing MRAC was applied to create μ-maps with bone segmentation, and retrospective PET reconstruction was performed using this μ-map. The bone structures of all patients created using model-based MRAC were visually evaluated. Standard uptake values (SUVs) at 11 anatomical positions in PET images, corrected using the μ-map with and without bone segmentation, were measured and compared. The model-based post-processing MRAC was run for all patients, without errors. Visual evaluation revealed that the model-based post-processing MRAC exhibited poor results for six patients. Furthermore, it exhibited an increasing trend of SUV in the brain compared to the conventional method. Locations other than the brain indicated a similar or decreasing trend. The two methods showed a good linear correlation for all patients. However, patients aged < 20 years exhibited a different trend from those aged ≥ 20 years. We should exercise caution when applying this model-based MRAC for younger patients.

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

BACKGROUND

A 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.

METHODS

Nine 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.

RESULTS

For 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.

CONCLUSIONS

Both 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.

Zero Echo Time MRAC on FDG-PET/MR Maintains Diagnostic Accuracy for Alzheimer's Disease; A Simulation Study Combining ADNI-Data

Aim

Attenuation correction using zero-echo time (ZTE) - magnetic resonance imaging (MRI) (ZTE-MRAC) has become one of the standard methods for brain-positron emission tomography (PET) on commercial PET/MR scanners. Although the accuracy of the net tracer-uptake quantification based on ZTE-MRAC has been validated, that of the diagnosis for dementia has not yet been clarified, especially in terms of automated statistical analysis. The aim of this study was to clarify the impact of ZTE-MRAC on the diagnosis of Alzheimer's disease (AD) by performing simulation study.

Methods

We recruited 27 subjects, who underwent both PET/computed tomography (CT) and PET/MR (GE SIGNA) examinations. Additionally, we extracted 107 subjects from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset. From the PET raw data acquired on PET/MR, three FDG-PET series were generated, using two vendor-provided MRAC methods (ZTE and Atlas) and CT-based AC. Following spatial normalization to Montreal Neurological Institute (MNI) space, we calculated each patient's specific error maps, which correspond to the difference between the PET image corrected using the CTAC method and the PET images corrected using the MRAC methods. To simulate PET maps as if ADNI data had been corrected using MRAC methods, we multiplied each of these 27 error maps with each of the 107 ADNI cases in MNI space. To evaluate the probability of AD in each resulting image, we calculated a cumulative t-value using a fully automated method which had been validated not only in the original ADNI dataset but several multi-center studies. In the method, PET score = 1 is the 95% prediction limit of AD. PET score and diagnostic accuracy for the discrimination of AD were evaluated in simulated images using the original ADNI dataset as reference.

Results

Positron emission tomography score was slightly underestimated both in ZTE and Atlas group compared with reference CTAC (-0.0796 ± 0.0938 vs. -0.0784 ± 0.1724). The absolute error of PET score was lower in ZTE than Atlas group (0.098 ± 0.075 vs. 0.145 ± 0.122, p < 0.001). A higher correlation to the original PET score was observed in ZTE vs. Atlas group (R ²: 0.982 vs. 0.961). The accuracy for the discrimination of AD patients from normal control was maintained in ZTE and Atlas compared to CTAC (ZTE vs. Atlas. vs. original; 82.5% vs. 82.1% vs. 83.2% (CI 81.8-84.5%), respectively).

Conclusion

For FDG-PET images on PET/MR, attenuation correction using ZTE-MRI had superior accuracy to an atlas-based method in classification for dementia. ZTE maintains the diagnostic accuracy for AD.

Attenuation correction for human PET/MRI studies

Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.

Diagnostic Value of FDG PET/MRI in Females With Pelvic Malignancy-A Systematic Review of the Literature

Hybrid imaging with F-18 fludeoxyglucose positron emission tomography/magnetic resonance imaging (FDG PET/MRI) has increasing clinical applications supplementing conventional ultrasound, CT, and MRI imaging as well as hybrid PET/CT imaging in assessing cervical, endometrial, and ovarian cancer. This article summarizes the existing literature and discusses the emerging role of hybrid PET/MRI in gynecologic malignancies. Thus, far, the published literature on the applications of FDG PET/MRI shows that it can have a significant impact on patient management by improving the staging of the cancers compared with PET/CT, influencing clinical decision and treatment strategy. For disease restaging, current literature indicates that PET/MRI performs equivalently to PET/CT. There appears to be a mild-moderate inverse correlation between standard-uptake-value (SUV) and apparent-diffusion-coefficient (ADC) values, which could be used to predict tumor grading and risk stratification. It remains to be seen as to whether multi-parametric PET/MRI imaging could prove valuable for prognostication and outcome. PET/MRI provides the opportunity for reduced radiation exposure, which is particularly relevant for a young female in need of multiple scans for treatment monitoring and follow-up. Fast acquisition protocols and optimized methods for attenuation correction are still evolving. Major limitations of PET/MRI remains such as suboptimal detection of small pulmonary nodules and lack of utility for radiation treatment planning, which pose an impediment in making PET/MRI a viable one-stop-shop imaging option to compete with PET/CT.

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging

Purpose:

PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer’s disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/Multi-Echo Dixon (mUTE) sequence for amyloid and tau imaging.

Methods:

Thirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as other magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon- based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy.

Results:

The Dice coefficients of the deep learning methods based on MPRAGE, Dixon and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices.

Conclusion:

Deep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.

Simultaneous FDG-PET/MRI detects hippocampal subfield metabolic differences in AD/MCI

The medial temporal lobe is one of the most well-studied brain regions affected by Alzheimer’s disease (AD). Although the spread of neurofibrillary pathology in the hippocampus throughout the progression of AD has been thoroughly characterized and staged using histology and other imaging techniques, it has not been precisely quantified in vivo at the subfield level using simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI). Here, we investigate alterations in metabolism and volume using [¹⁸F]fluoro-deoxyglucose (FDG) and simultaneous time-of-flight (TOF) PET/MRI with hippocampal subfield analysis of AD, mild cognitive impairment (MCI), and healthy subjects. We found significant structural and metabolic changes within the hippocampus that can be sensitively assessed at the subfield level in a small cohort. While no significant differences were found between groups for whole hippocampal SUVr values (p = 0.166), we found a clear delineation in SUVr between groups in the dentate gyrus (p = 0.009). Subfield analysis may be more sensitive for detecting pathological changes using PET-MRI in AD compared to global hippocampal assessment.

The impact of atlas-based MR attenuation correction on the diagnosis of FDG-PET/MR for Alzheimer's diseases- A simulation study combining multi-center data and ADNI-data

Background

The purpose of this study was to assess the impact of vendor-provided atlas-based MRAC on FDG PET/MR for the evaluation of Alzheimer’s disease (AD) by using simulated images.

Methods

We recruited 47 patients, from two institutions, who underwent PET/CT and PET/MR (GE SIGNA) examination for oncological staging. From the PET raw data acquired on PET/MR, two FDG-PET series were generated, using vendor-provided MRAC (atlas-based) and CTAC. The following simulation steps were performed in MNI space: After spatial normalization and smoothing of the PET datasets, we calculated the error map for each patient, PET_MRAC/PET_CTAC. We multiplied each of these 47 error maps with each of the 203 Alzheimer’s Disease Neuroimaging Initiative (ADNI) cases after the identical normalization and smoothing. This resulted in 203*47 = 9541 datasets. To evaluate the probability of AD in each resulting image, a cumulative t-value was calculated automatically using commercially-available software (PMOD PALZ) which has been used in multiple large cohort studies. The diagnostic accuracy for the discrimination of AD and predicting progression from mild cognitive impairment (MCI) to AD were evaluated in simulated images compared with ADNI original images.

Results

The accuracy and specificity for the discrimination of AD-patients from normal controls were not substantially impaired, but sensitivity was slightly impaired in 5 out of 47 datasets (original vs. error; 83.2% [CI 75.0%-89.0%], 83.3% [CI 74.2%-89.8%] and 83.1% [CI 75.6%-88.3%] vs. 82.7% [range 80.4–85.0%], 78.5% [range 72.9–83.3%,] and 86.1% [range 81.4–89.8%]). The accuracy, sensitivity and specificity for predicting progression from MCI to AD during 2-year follow-up was not impaired (original vs. error; 62.5% [CI 53.3%-69.3%], 78.8% [CI 65.4%-88.6%] and 54.0% [CI 47.0%-69.1%] vs. 64.8% [range 61.5–66.7%], 75.7% [range 66.7–81.8%,] and 59.0% [range 50.8–63.5%]). The worst 3 error maps show a tendency towards underestimation of PET scores.

Conclusion

FDG-PET/MR based on atlas-based MR attenuation correction showed similar diagnostic accuracy to the CT-based method for the diagnosis of AD and the prediction of progression of MCI to AD using commercially-available software, although with a minor reduction in sensitivity.

Identifying Hypoperfusion in Moyamoya Disease With Arterial Spin Labeling and an [15O]-Water Positron Emission Tomography/Magnetic Resonance Imaging Normative Database

Background and Purpose- Noninvasive imaging of brain perfusion has the potential to elucidate pathophysiological mechanisms underlying Moyamoya disease and enable clinical imaging of cerebral blood flow (CBF) to select revascularization therapies for patients. We used hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) technology to characterize the distribution of hypoperfusion in Moyamoya disease and its relationship to vessel stenosis severity, through comparisons with a normative perfusion database of healthy controls. Methods- To image CBF, we acquired [¹⁵O]-water PET as a reference and simultaneously acquired arterial spin labeling (ASL) MRI scans in 20 Moyamoya patients and 15 age-matched, healthy controls on a PET/MRI scanner. The ASL MRI scans included a standard single-delay ASL scan with postlabel delay of 2.0 s and a multidelay scan with 5 postlabel delays (0.7-3.0s) to estimate and account for arterial transit time in CBF quantification. The percent volume of hypoperfusion in patients (determined as the fifth percentile of CBF values in the healthy control database) was the outcome measure in a logistic regression model that included stenosis grade and location. Results- Logistic regression showed that anterior ( P<0.0001) and middle cerebral artery territory regions ( P=0.003) in Moyamoya patients were susceptible to hypoperfusion, whereas posterior regions were not. Cortical regions supplied by arteries with stenosis on MR angiography showed more hypoperfusion than normal arteries ( P=0.001), but the extent of hypoperfusion was not different between mild-moderate versus severe stenosis. Multidelay ASL did not perform differently from [¹⁵O]-water PET in detecting perfusion abnormalities, but standard ASL overestimated the extent of hypoperfusion in patients ( P=0.003). Conclusions- This simultaneous PET/MRI study supports the use of multidelay ASL MRI in clinical evaluation of Moyamoya disease in settings where nuclear medicine imaging is not available and application of a normative perfusion database to automatically identify abnormal CBF in patients.

Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction

One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomplished by generating an MRI-based surrogate computed tomography (CT) from which AC-maps are derived. Nevertheless, all techniques currently implemented in clinical routine suffer from bias. We present here a convolutional neural network (CNN) that generated AC-maps from Zero Echo Time (ZTE) MR images. Seventy patients referred to our institution for ¹⁸FDG-PET/MR exam (SIGNA PET/MR, GE Healthcare) as part of the investigation of suspected dementia, were included. 23 patients were added to the training set of the manufacturer and 47 were used for validation. Brain computed tomography (CT) scan, two-point LAVA-flex MRI (for atlas-based AC) and ZTE-MRI were available in all patients. Three AC methods were evaluated and compared to CT-based AC (CTAC): one based on a single head-atlas, one based on ZTE-segmentation and one CNN with a 3D U-net architecture to generate AC maps from ZTE MR images. Impact on brain metabolism was evaluated combining voxel and regions-of-interest based analyses with CTAC set as reference. The U-net AC method yielded the lowest bias, the lowest inter-individual and inter-regional variability compared to PET images reconstructed with ZTE and Atlas methods. The impact on brain metabolism was negligible with average errors of -0.2% in most cortical regions. These results suggest that the U-net AC is more reliable for correcting photon attenuation in brain FDG-PET/MR than atlas-AC and ZTE-AC methods.

Impact of non-uniform attenuation correction in a dynamic [18F]-FDOPA brain PET/MRI study

Background

PET (positron emission tomography) biokinetic modelling relies on accurate quantitative data. One of the main corrections required in PET imaging to obtain high quantitative accuracy is tissue attenuation correction (AC). Incorrect non-uniform PET-AC may result in local bias in the emission images, and thus in relative activity distributions and time activity curves for different regions. MRI (magnetic resonance imaging)-based AC is an active area of research in PET/MRI neuroimaging, where several groups developed in the last few years different methods to calculate accurate attenuation (μ-)maps. Some AC methods have been evaluated for different PET radioisotopes and pathologies. However, AC in PET/MRI has scantly been investigated in dynamic PET studies where the aim is to get quantitative kinetic parameters, rather than semi-quantitative parameters from static PET studies. In this work, we investigated the impact of AC accuracy in PET image absolute quantification and, more importantly, in the slope of the Patlak analysis based on the simplified reference tissue model, from a dynamic [¹⁸F]-fluorodopa (FDOPA) PET/MRI study. In the study, we considered the two AC methods provided by the vendor and an in-house AC method based on the dual ultrashort time echo MRI sequence, using as reference a multi-atlas-based AC method based on a T1-weighted MRI sequence.

Results

Non-uniform bias in absolute PET quantification across the brain, from − 20% near the skull to − 10% in the central region, was observed using the two vendor’s μ-maps. The AC method developed in-house showed a − 5% and 1% bias, respectively. Our study resulted in a 5–9% overestimation of the PET kinetic parameters with the vendor-provided μ-maps, while our in-house-developed AC method showed < 2% overestimation compared to the atlas-based AC method, using the cerebellar cortex as reference region. The overestimation obtained using the occipital pole as reference region resulted in a 7–10% with the vendor-provided μ-maps, while our in-house-developed AC method showed < 6% overestimation.

Conclusions

PET kinetic analyses based on a reference region are especially sensitive to the non-uniform bias in PET quantification from AC inaccuracies in brain PET/MRI. Depending on the position of the reference region and the bias with respect to the analysed region, kinetic analyses suffer different levels of bias. Considering bone in the μ-map can potentially result in larger errors, compared to the absence of bone, when non-uniformities in PET quantification are introduced.

Simultaneous phase-contrast MRI and PET for noninvasive quantification of cerebral blood flow and reactivity in healthy subjects and patients with cerebrovascular disease

BACKGROUND

H₂ ¹⁵ O-positron emission tomography (PET) is considered the reference standard for absolute cerebral blood flow (CBF). However, this technique requires an arterial input function measured through continuous sampling of arterial blood, which is invasive and has limitations with tracer delay and dispersion.

PURPOSE

To demonstrate a new noninvasive method to quantify absolute CBF with a PET/MRI hybrid scanner. This blood-free approach, called PC-PET, takes the spatial CBF distribution from a static H₂ ¹⁵ O-PET scan, and scales it to the whole-brain average CBF value measured by simultaneous phase-contrast MRI.

STUDY TYPE

Observational.

SUBJECTS

Twelve healthy controls (HC) and 13 patients with Moyamoya disease (MM) as a model of chronic ischemic disease.

FIELD STRENGTH/SEQUENCES

3T/2D cardiac-gated phase-contrast MRI and H₂ ¹⁵ O-PET.

ASSESSMENT

PC-PET CBF values from whole brain (WB), gray matter (GM), and white matter (WM) in HCs were compared with literature values since 2000. CBF and cerebrovascular reactivity (CVR), which is defined as the percent CBF change between baseline and post-acetazolamide (vasodilator) scans, were measured by PC-PET in MM patients and HCs within cortical regions corresponding to major vascular territories. Statistical Tests: Linear, mixed effects models were created to compare CBF and CVR, respectively, between patients and controls, and between different degrees of stenosis.

RESULTS

The mean CBF values in WB, GM, and WM in HC were 42 ± 7 ml/100 g/min, 50 ± 7 ml/100 g/min, and 23 ± 3 ml/100 g/min, respectively, which agree well with literature values. Compared with normal regions (57 ± 23%), patients showed significantly decreased CVR in areas with mild/moderate stenosis (47 ± 17%, P = 0.011) and in severe/occluded areas (40 ± 16%, P = 0.016). Data Conclusion: PC-PET identifies differences in cerebrovascular reactivity between healthy controls and cerebrovascular patients. PC-PET is suitable for CBF measurement when arterial blood sampling is not accessible, and warrants comparison to fully quantitative H₂ ¹⁵ O-PET in future studies.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;51:183-194.

No significant difference found in PET/MRI CBF values reconstructed with CT-atlas-based and ZTE MR attenuation correction

Background

Accurate attenuation correction (AC) is one of the most important issues to be addressed in quantitative brain PET/MRI imaging. Atlas-based MRI AC (AB-MRAC), one of the representative MRAC methods, has been used to estimate the skull attenuation in brain scans. The zero echo time (ZTE) pulse sequence is also expected to provide a better MRAC estimation in brain PET scans. The difference in quantitative measurements of cerebral blood flow (CBF) using H₂¹⁵O-PET/MRI was compared between the two MRAC methods, AB and ZTE.

Method

Twelve patients with cerebrovascular disease (4 males, 43.2 ± 11.7 years) underwent H₂¹⁵O-PET/MRI studies with a 3-min PET scan and MRI scans including the ZTE sequence. Eleven of them were also studied under the conditions of baseline and 10 min after acetazolamide administration, and 2 of them were followed up after several months interval. A total of 25 PET images were reconstructed as dynamic data using 2 sets of reconstruction parameters to obtain the image-derived input function (IDIF), the time-activity curves of the major cerebral artery extracted from images, and CBF images. The CBF images from AB- and ZTE-MRAC were then compared for global and regional differences.

Results

The mean differences of IDIF curves at each point obtained from AB- and ZTE-MRAC dynamic data were less than 5%, and the differences in time-activity curves were very small. The means of CBF from AB- and ZTE-MRAC reconstructions calculated using each IDIF showed differences of less than 5% for all cortical regions. CBF images from AB-MRAC tended to show greater values in the parietal region and smaller values in the skull base region.

Conclusion

The CBF images from AB- and ZTE-MRAC reconstruction showed no significant differences in regional values, although the parietal region tended to show greater values in AB-MRAC reconstruction. Quantitative values in the skull base region were very close, and almost the same IDIFs were obtained.

A head coil system with an integrated orbiting transmission point source mechanism for attenuation correction in PET/MRI

The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) provides a benefit for diagnostic imaging. Still, attenuation correction (AC) is a challenge in PET/MRI compared to stand-alone PET and PET-computed tomography (PET/CT). In the absence of photonic transmission sources, AC in PET/MRI is usually based on retrospective segmentation of MR images or complex additional MR-sequences. However, most methods available today are still challenged by either the incorporation of cortical bone or substantial anatomical variations of subjects. This leads to a bias in quantification of tracer concentration in PET. Therefore, we have developed a fully integrated transmission source system for PET/MRI of the head to enable direct measurement of attenuation coefficients using external positron emitters, which is the reference standard in AC. Based on a setup called the 'liquid drive' presented by Jones et al (1995) two decades ago, we built a head coil system consisting of an MR-compatible hydraulic system driving a point source on a helical path around a 24-channel MR-receiver coil to perform a transmission scan. Sinogram windowing of the moving source allows for post-injection measurements. The prototype was tested in the Siemens Biograph mMR using a homogeneous water phantom and a phantom with air cavities and a Teflon (PTFE) cylinder. The second phantom was measured both with and without emission activity. For both measurements air, water and Teflon were clearly distinguishable and homogeneous regions of the phantom were successfully reproduced in the AC map. For water the linear attenuation coefficient was measured as (0.096  ±  0.005) cm^-1 in accordance with the true physical value. This combined MR head coil and transmission source system is, to our knowledge, the first working example to use an orbiting point source in PET/MRI and may be helpful in providing fully-quantitative PET data in neuro-PET/MRI.

Accurate hybrid template-based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications

Background

Attenuation correction is one of the most crucial correction factors for accurate PET data quantitation in hybrid PET/MR scanners, and computing accurate attenuation coefficient maps from MR brain acquisitions is challenging. Here, we develop a method for accurate bone and air segmentation using MR ultrashort echo time (UTE) images.

Methods

MR UTE images from simultaneous MR and PET imaging of five healthy volunteers was used to generate a whole head, bone and air template image for inclusion into an improved MR derived attenuation correction map, and applied to PET image data for quantitative analysis. Bone, air and soft tissue were segmented based on Gaussian Mixture Models with probabilistic tissue maps as a priori information. We present results for two approaches for bone attenuation coefficient assignments: one using a constant attenuation correction value; and another using an estimated continuous attenuation value based on a calibration fit. Quantitative comparisons were performed to evaluate the accuracy of the reconstructed PET images, with respect to a reference image reconstructed with manually segmented attenuation maps.

Results

The DICE coefficient analysis for the air and bone regions in the images demonstrated improvements compared to the UTE approach, and other state-of-the-art techniques. The most accurate whole brain and regional brain analyses were obtained using constant bone attenuation coefficient values.

Conclusions

A novel attenuation correction method for PET data reconstruction is proposed. Analyses show improvements in the quantitative accuracy of the reconstructed PET images compared to other state-of-the-art AC methods for simultaneous PET/MR scanners. Further evaluation is needed with radiopharmaceuticals other than FDG, and in larger cohorts of participants.

Evaluation of zero-echo-time attenuation correction for integrated PET/MR brain imaging-comparison to head atlas and 68Ge-transmission-based attenuation correction

BACKGROUND

MRI does not offer a direct method to obtain attenuation correction maps as its predecessors (stand-alone PET and PET/CT), and bone visualisation is particularly challenging. Recently, zero-echo-time (ZTE) was suggested for MR-based attenuation correction (AC). The aim of this work was to evaluate ZTE- and atlas-AC by comparison to 68Ge-transmission scan-based AC. Nine patients underwent brain PET/MR and stand-alone PET scanning using the dopamine transporter ligand 11C-PE2I. For each of them, two AC maps were obtained from the MR images: an atlas-based, obtained from T1-weighted LAVA-FLEX imaging with cortical bone inserted using a CT-based atlas, and an AC map generated from proton-density-weighted ZTE images. Stand-alone PET 68Ge-transmission AC map was used as gold standard. PET images were reconstructed using the three AC methods and standardised uptake value (SUV) values for the striatal, limbic and cortical regions, as well as the cerebellum (VOIs) were compared. SUV ratio (SUVR) values normalised for the cerebellum were also assessed. Bias, precision and agreement were calculated; statistical significance was evaluated using Wilcoxon matched-pairs signed-rank test.

RESULTS

Both ZTE- and atlas-AC showed a similar bias of 6-8% in SUV values across the regions. Correlation coefficients with 68Ge-AC were consistently high for ZTE-AC (r 0.99 for all regions), whereas they were lower for atlas-AC, varying from 0.99 in the striatum to 0.88 in the posterior cortical regions. SUVR showed an overall bias of 2.9 and 0.5% for atlas-AC and ZTE-AC, respectively. Correlations with 68Ge-AC were higher for ZTE-AC, varying from 0.99 in the striatum to 0.96 in the limbic regions, compared to atlas-AC (0.99 striatum to 0.77 posterior cortex).

CONCLUSIONS

Absolute SUV values showed less variability for ZTE-AC than for atlas-AC when compared to 68Ge-AC, but bias was similar for both methods. This bias is largely caused by higher linear attenuation coefficients in atlas- and ZTE-AC image compared to 68Ge-images. For SUVR, bias was lower when using ZTE-AC than for atlas-AC. ZTE-AC shows to be a more robust technique than atlas-AC in terms of both intra- and inter-patient variability.

From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies

Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.

Capturing Bone Signal in MRI of Pelvis, as a Large FOV Region, Using TWIST Sequence and Generating a 5-Class Attenuation Map for Prostate PET/MRI Imaging

Purpose:

Prostate imaging is a major application of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI). Currently, MRI-based attenuation correction (MRAC) for whole-body PET/MRI in which the bony structures are ignored is the main obstacle to successful implementation of the hybrid modality in the clinical work flow. Ultrashort echo time sequence captures bone signal but needs specific hardware–software and is challenging in large field of view (FOV) regions, such as pelvis. The main aims of the work are (1) to capture a part of the bone signal in pelvis using short echo time (STE) imaging based on time-resolved angiography with interleaved stochastic trajectories (TWIST) sequence and (2) to consider the bone in pelvis attenuation map (µ-map) to MRAC for PET/MRI systems.

Procedures:

Time-resolved angiography with interleaved stochastic trajectories, which is routinely used for MR angiography with high temporal and spatial resolution, was employed for fast/STE MR imaging. Data acquisition was performed in a TE of 0.88 milliseconds (STE) and 4.86 milliseconds (long echo time [LTE]) in pelvis region. Region of interest (ROI)-based analysis was used for comparing the signal-to-noise ratio (SNR) of cortical bone in STE and LTE images. A hybrid segmentation protocol, which is comprised of image subtraction, a Fuzzy-based segmentation, and a dedicated morphologic operation, was used for generating a 5-class µ-map consisting of cortical bone, air cavity, fat, soft tissue, and background (µ-map_MR-5c). A MR-based 4-class µ-map (µ-map_MR-4c) that considered soft tissue rather than bone was generated. As such, a bilinear (µ-map_CT-ref), 5 (µ-map_CT-5c), and 4 class µ-map (µ-map_CT-4c) based on computed tomography (CT) images were generated. Finally, simulated PET data were corrected using µ-map_MR-5c (PET-MRAC5c), µ-map_MR-4c (PET-MRAC4c), µ-map_CT-5c (PET-CTAC5c), and µ-map_CT-ref (PET-CTAC).

Results:

The ratio of SNR_bone to SNR_{air cavity} in LTE images was 0.8, this factor was increased to 4.4 in STE images. The Dice, Sensitivity, and Accuracy metrics for bone segmentation in proposed method were 72.4% ± 5.5%, 69.6% ± 7.5%, and 96.5% ± 3.5%, respectively, where the segmented CT served as reference. The mean relative error in bone regions in the simulated PET images were −13.98% ± 15%, −35.59% ± 15.41%, and 1.81% ± 12.2%, respectively, in PET-MRAC5c, PET-MRAC4c, and PET-CTAC5c where PET-CTAC served as the reference. Despite poor correlation in the joint histogram of µ-map_MR-4c versus µ-map_CT-5c (R² > 0.78) and PET-MRAC4c versus PET-CTAC5c (R² = 0.83), high correlations were observed in µ-map_MR-5c versus µ-map_CT-5c (R² > 0.94) and PET-MRAC5c versus PET-CTAC5c (R² > 0.96).

Conclusions:

According to the SNR_{STE, pelvic bone}, the cortical bone can be separate from air cavity in STE imaging based on TWIST sequence. The proposed method generated an MRI-based µ-map containing bone and air cavity that led to more accurate tracer uptake estimation than MRAC4c. Uptake estimation in hybrid PET/MRI can be improved by employing the proposed method.

Improving PET/MR brain quantitation with template-enhanced ZTE

PURPOSE

The impact of MR-based attenuation correction on PET quantitation accuracy is an ongoing cause of concern for advanced brain research with PET/MR. The purpose of this study was to evaluate a new, template-enhanced zero-echo-time attenuation correction method for PET/MR scanners.

METHODS

30 subjects underwent a clinically-indicated ¹⁸F-FDG-PET/CT, followed by PET/MR on a GE SIGNA PET/MR. For each patient, a 42-s zero echo time (ZTE) sequence was used to generate two attenuation maps: one with the standard ZTE segmentation-based method; and another with a modification of the method, wherein pre-registered anatomical templates and CT data were used to enhance the segmentation. CT data, was used as gold standard. Reconstructed PET images were qualified visually and quantified in 68 volumes-of-interest using a standardized brain atlas.

RESULTS

Attenuation maps were successfully generated in all cases, without manual intervention or parameter tuning. One patient was excluded from the quantitative analysis due to the presence of multiple brain metastases. The PET bias with template-enhanced ZTE attenuation correction was measured to be -0.9% ± 0.9%, compared with -1.4% ± 1.1% with regular ZTE attenuation correction. In terms of absolute bias, the new method yielded 1.1% ± 0.7%, compared with 1.6% ± 0.9% with regular ZTE. Statistically significant bias reduction was obtained in the frontal region (from -2.0% to -1.0%), temporal (from -1.2% to -0.2%), parietal (from -1.9% to -1.1%), occipital (from -2.0% to -1.1%) and insula (from -1.4% to -1.1%).

CONCLUSION

These results indicate that the co-registration of pre-recorded anatomical templates to ZTE data is feasible in clinical practice and can be effectively used to improve the performance of segmentation-based attenuation correction.

PET/MRI: a frontier in era of complementary hybrid imaging

With primitive approaches, the diagnosis and therapy were operated at the cellular, molecular, or even at the genetic level. As the diagnostic techniques are more concentrated towards molecular level, multi modal imaging becomes specifically essential. Multi-modal imaging has extensive applications in clinical as well as in pre-clinical studies. Positron Emission Tomography (PET) has flourished in the field of nuclear medicine, which has motivated it to fuse with Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) for PET/CT and PET/MRI respectively. However, the challenges in PET/CT are due to the inability of simultaneous acquisition and reduced soft tissue contrast, which has led to the development of PET/MRI. Also, MRI offers the better soft tissue contrast over CT. Hence, fusion of PET and MRI results in combining structural information with functional image from PET. Yet, it has many technical challenges due to the interference between the modalities. Also, it must be resolved with various approaches for addressing the shortcomings of each system and improvise on the image quantification system. This review elaborates on the various challenges in the present PET/MRI system and the future directions of the hybrid modality. Also, the different data acquisition and analysis techniques of PET/MRI system are discussed with enhanced details on the software tools.

Positron emission tomography/magnetic resonance imaging (PET/MRI): An update and initial experience at HC-FMUSP

The new technology of PET/MRI is a prototype of hybrid imaging, allowing for the combination of molecular data from PET scanning and morphofunctional information derived from MRI scanning. Recent advances regarding the technical aspects of this device, especially after the development of MRI-compatible silicon photomultipliers of PET, permitted an increase in the diagnostic performance of PET/MRI translated into dose reduction and higher imaging quality. Among several clinical applications, PET/MRI gains ground initially in oncology, where MRI per se plays an essential role in the assessment of primary tumors (which is limited in the case of PET/CT), including prostate, rectal and gynecological tumors. On the other hand, the evaluation of the lungs remains an enigma although new MRI sequences are being designed to overcome this. More clinical indications of PET/MRI are seen in the fields of neurology, cardiology and inflammatory processes, and the use of PET/MRI also opens perspectives for pediatric populations as it involves very low radiation exposure. Our review aimed to highlight the current indications of PET/MRI and discuss the challenges and perspectives of PET/MRI at HC-FMUSP.

Advances in PET/MR instrumentation and image reconstruction

The combination of positron emission tomography (PET) and MRI has attracted the attention of researchers in the past approximately 20 years in small-animal imaging and more recently in clinical research. The combination of PET/MRI allows researchers to explore clinical and research questions in a wide number of fields, some of which are briefly mentioned here. An important number of groups have developed different concepts to tackle the problems that PET instrumentation poses to the exposition of electromagnetic fields. We have described most of these research developments in preclinical and clinical experiments, including the few commercial scanners available. From the software perspective, an important number of algorithms have been developed to address the attenuation correction issue and to exploit the possibility that MRI provides for motion correction and quantitative image reconstruction, especially parametric modelling of radiopharmaceutical kinetics. In this work, we give an overview of some exemplar applications of simultaneous PET/MRI, together with technological hardware and software developments.

Visual detection of regional brain hypometabolism in cognitively impaired patients is independent of positron emission tomography-magnetic resonance attenuation correction method

Fluorodeoxyglucose (FDG) positron emission tomography-magnetic resonance (PET/MR) is useful for the evaluation of cognitively-impaired patients. This study aims to assess two different attenuation correction (AC) methods (Dixon-MR and atlas-based) versus index-standard computed tomography (CT) AC for the visual interpretation of regional hypometabolism in patients with cognitive impairment. Two board-certified nuclear medicine physicians blindly scored brain region FDG hypometabolism as normal versus hypometabolic using two-dimensional (2D) and 3D FDG PET/MR images generated by MIM software. Regions were quantitatively assessed as normal versus mildly, moderately, or severely hypometabolic. Hypometabolism scores obtained using the different methods of AC were compared, and interreader, as well as intra-reader agreement, was assessed. Regional hypometabolism versus normal metabolism was correctly classified in 16 patients on atlas-based and Dixon-based AC map PET reconstructions (vs. CT reference AC) for 94% (90%–96% confidence interval [CI]) and 93% (89%–96% CI) of scored regions, respectively. The averaged sensitivity/specificity for detection of any regional hypometabolism was 95%/94% (P = 0.669) and 90%/91% (P = 0.937) for atlas-based and Dixon-based AC maps. Interreader agreement for detection of regional hypometabolism was high, with similar outcome assessments when using atlas- and Dixon-corrected PET data in 93% (Κ =0.82) and 93% (Κ =0.84) of regions, respectively. Intrareader agreement for detection of regional hypometabolism was high, with concordant outcome assessments when using atlas- and Dixon-corrected data in 93%/92% (Κ =0.79) and 92/93% (Κ =0.78). Despite the quantitative advantages of atlas-based AC in brain PET/MR, routine clinical Dixon AC yields comparable visual ratings of regional hypometabolism in the evaluation of cognitively impaired patients undergoing brain PET/MR and is similar in performance to CT-based AC. Therefore, Dixon AC is acceptable for the routine clinical evaluation of dementia syndromes.

PET/MRI: Where might it replace PET/CT?

Simultaneous positron emission tomography and MRI (PET/MRI) is a technology that combines the anatomic and quantitative strengths of MR imaging with physiologic information obtained from PET. PET and computed tomography (PET/CT) performed in a single scanning session is an established technology already in widespread and accepted use worldwide. Given the higher cost and complexity of operating and interpreting the studies obtained on a PET/MRI system, there has been question as to which patients would benefit most from imaging with PET/MRI versus PET/CT. In this article, we compare PET/MRI with PET/CT, detail the applications for which PET/MRI has shown promise and discuss impediments to future adoption. It is our hope that future work will prove the benefit of PET/MRI to specific groups of patients, initially those in which PET/CT and MRI are already performed, leveraging simultaneity and allowing for greater degrees of multiparametric evaluation.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:1247-1262.