Hybrid PET/MR Imaging in Neurology: Present Applications and Prospects for the Future

Topographic metabolism-function relationships in Alzheimer's disease: A simultaneous PET/MRI study

Disruptions of neural metabolism and function occur in parallel during Alzheimer's disease (AD). While many studies have shown diverse metabolic-functional relationships in specific brain regions, much less is known about how large-scale network-level functional activity is associated with the topology of metabolism in AD. In this study, we took the advantages of simultaneous PET/MRI and multivariate analyses to investigate the associations between AD-related stereotypical spatial patterns (topographies) of glucose metabolism, measured by fluorodeoxyglucose PET, and functional connectivity, measured by resting-state functional MRI. A total of 101 participants, including 37 patients with AD, 25 patients with mild cognitive impairment (MCI), and 39 cognitively normal controls, underwent PET/MRI scans and cognitive assessments. Three pairs of distinct but optimally correlated metabolic and functional topographies were identified, encompassing large-scale networks including the default-mode, executive and control, salience, attention, and subcortical networks. Importantly, the metabolic-functional associations were not only limited to one-to-one-corresponding regions, but also occur in remote and non-overlapping regions. Furthermore, both glucose metabolism and functional connectivity, as well as their linkages, exhibited various degrees of disruptions in patients with MCI and AD, and were correlated with cognitive decline. In conclusion, our results support distributed and heterogeneous topographic associations between metabolism and function, which are jeopardized by AD. Findings of this study may deepen our understanding of the pathological mechanism of AD through the perspectives of both local energy efficiency and long-term interactions between synaptic disruption and functional disconnection contributing to the clinical symptomatology in AD.

Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics

OBJECTIVES

To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics.

METHODS

One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models.

RESULTS

The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model.

CONCLUSIONS

Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics.

CLINICAL RELEVANCE STATEMENT

This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance.

KEY POINTS

•Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.

Combining amino acid PET and MRI imaging increases accuracy to define malignant areas in adult glioma

Accurate determination of the extent and grade of adult-type diffuse gliomas is critical to patient management. In clinical practice, contrast-enhancing areas of diffuse gliomas in magnetic resonance imaging (MRI) sequences are usually used to target biopsy, surgery, and radiation therapy, but there can be discrepancies between these areas and the actual tumor extent. Here we show that adding ¹⁸F-fluoro-ethyl-tyrosine positron emission tomography (FET-PET) to MRI sequences accurately locates the most malignant areas of contrast-enhancing gliomas, potentially impacting subsequent management and outcomes. We present a prospective analysis of over 300 serial biopsy specimens from 23 patients with contrast-enhancing adult-type diffuse gliomas using a hybrid PET-MRI scanner to compare T2-weighted and contrast-enhancing MRI images with FET-PET. In all cases, we observe and confirm high FET uptake in early PET acquisitions (5-15 min after ¹⁸F-FET administration) outside areas of contrast enhancement on MRI, indicative of high-grade glioma. In 30% cases, inclusion of FET-positive sites changes the biopsy result to a higher tumor grade.

Disrupted coupling between salience network segregation and glucose metabolism is associated with cognitive decline in Alzheimer's disease - A simultaneous resting-state FDG-PET/fMRI study

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Hybrid PET/MRI was used to explore network segregation and glucose metabolism in AD.

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DMN, CEN, and SN showed reduced segregation in AD.

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In salience network, segregation coupled with glucose metabolism in CN group.

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The coupled segregation and glucose metabolism in CN disappeared in MCI and AD.

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Reduced segregation and hypometabolism were associated with cognitive impairments.

The aberrant organization and functioning of three core neurocognitive networks (NCNs), i.e., default-mode network (DMN), central executive network (CEN), and salience network (SN), are among the prominent features in Alzheimer’s disease (AD). The dysregulation of both intra- and inter-network functional connectivities (FCs) of the three NCNs contributed to AD-related cognitive and behavioral abnormalities. Brain functional network segregation, integrating intra- and inter-network FCs, is essential for maintaining the energetic efficiency of brain metabolism. The association of brain functional network segregation, together with glucose metabolism, with age-related cognitive decline was recently shown. Yet how these joint functional-metabolic biomarkers relate to cognitive decline along with mild cognitive impairment (MCI) and AD remains to be elucidated. In this study, under the framework of the triple-network model, we performed a hybrid FDG-PET/fMRI study to evaluate the concurrent changes of resting-state brain intrinsic FCs and glucose metabolism of the three NCNs across cognitively normal (CN) (N = 24), MCI (N = 21), and AD (N = 21) groups. Lower network segregation and glucose metabolism were observed in all three NCNs in patients with AD. More interestingly, in the SN, the coupled relationship between network segregation and glucose metabolism existed in the CN group (r = 0.523, p = 0.013) and diminished in patients with MCI (r = 0.431, p = 0.065) and AD (r = 0.079, p = 0.748). Finally, the glucose metabolism of the DMN (r = 0.380, p = 0.017) and the network segregation of the SN (r = 0.363, p = 0.023) were significantly correlated with the general cognitive status of the patients. Our findings suggest that the impaired SN segregation and its uncoupled relationship with glucose metabolism contribute to the cognitive decline in AD.

A Novel J-Shape Antenna Array for Simultaneous MR-PET or MR-SPECT Imaging

Simultaneous MR-PET/-SPECT is an emerging technology that capitalises on the invaluable advantages of both modalities, allowing access to numerous sensitive tracers and superior soft-tissue contrast alongside versatile functional imaging capabilities. However, to optimise these capabilities, concurrent acquisitions require the MRI antenna located inside the PET/SPECT field-of-view to be operated without compromising any aspects of system performance or image quality compared to the stand-alone instrumentation. Here, we report a novel gamma-radiation-transparent antenna concept. The end-fed J-shape antenna is particularly adept for hybrid ultra-high field MR-PET/-SPECT applications as it enables all highly attenuating materials to be placed outside the imaging field-of-view. Furthermore, this unique configuration also provides advantages in stand-alone MR applications by reducing the amount of coupling between the cables and the antenna elements, and by lowering the potential specific absorption rate burden. The use of this new design was experimentally verified according to the important features for both ultra-high field MRI and the 511 keV transmission scan. The reconstructed attenuation maps evidently showed much lower attenuation (  ∼ 15 %) for the proposed array when compared to the conventional dipole antenna array since there were no high-density components. In MR, it was observed that the signal-to-noise ratio from the whole volume obtained using the proposed array was comparable to that acquired by the conventional array which was also in agreement with the simulation results. The unique feature, J-shape array, would enable simultaneous MR-PET/-SPECT experiments to be conducted without unduly compromising any aspects of system performance and image quality compared to the stand-alone instrumentation.

Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy

PURPOSE

To construct multivariate radiomics models using hybrid ¹⁸F-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA).

METHODS

Ninety patients (60 with PD and 30 with MSA) were randomized to training and test sets in a 7:3 ratio. All patients underwent ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/MRI to simultaneously obtain metabolic images (¹⁸F-FDG), structural MRI images (T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and T2-weighted fluid-attenuated inversion recovery (T2/FLAIR)) and functional MRI images (susceptibility-weighted imaging (SWI) and apparent diffusion coefficient). Using PET and five MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA).

RESULTS

The radiomics signatures showed favourable diagnostic efficacy. The optimal model comprised structural (T1WI), functional (SWI) and metabolic (¹⁸F-FDG) sequences (Radscore_{FDG_T1WI_SWI}) with the area under curves (AUCs) of the training and test sets of 0.971 and 0.957, respectively. The integrated model, incorporating Radscore_{FDG_T1WI_SWI}, three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUV_max, demonstrated satisfactory calibration and discrimination in the training and test sets (0.993 and 0.994, respectively). DCA indicated the highest clinical benefit of the clinical-radiomics integrated model.

CONCLUSIONS

The radiomics signature with metabolic, structural and functional information provided by hybrid ¹⁸F-FDG PET/MRI may achieve promising diagnostic efficacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.

Applications of PET-MRI in musculoskeletal disease

New integrated PET-MRI systems potentially provide a complete imaging modality for diagnosis and evaluation of musculoskeletal disease. MRI is able to provide excellent high-resolution morphologic information with multiple contrast mechanisms that has made it the imaging modality of choice in evaluation of many musculoskeletal disorders. PET offers incomparable abilities to provide quantitative information about molecular and physiologic changes that often precede structural and biochemical changes. In combination, hybrid PET-MRI can enhance imaging of musculoskeletal disorders through early detection of disease as well as improved diagnostic sensitivity and specificity. The purpose of this article is to review emerging applications of PET-MRI in musculoskeletal disease. Both clinical applications of malignant musculoskeletal disease as well as new opportunities to incorporate the molecular capabilities of nuclear imaging into studies of nononcologic musculoskeletal disease are discussed. Lastly, we discuss some of the technical considerations and challenges of PET-MRI as they specifically relate to musculoskeletal disease.

LEVEL OF EVIDENCE

5 TECHNICAL EFFICACY: Stage 3 J. Magn. Reson. Imaging 2018;48:27-47.

Report of first recurrent glioma patients examined with PET-MRI prior to re-irradiation

Background and purpose

The advantage of combined PET-MRI over sequential PET and MRI is the high spatial conformity and the absence of time delay between the examinations. The benefit of this technique for planning of re-irradiation (re-RT) treatment is unkown yet. Imaging data from a phase 1 trial of re-RT for recurrent glioma was analysed to assess whether planning target volumes and treatment margins in glioma re-RT can be adjusted by PET-MRI with rater independent PET based biological tumour volumes (BTVs).

Patients and methods

Combined PET-MRI with the tracer O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET) prior to re-RT was performed in recurrent glioma patients in a phase I trial. GTVs including all regions suspicious of tumour on contrast enhanced MRI were delineated by three experienced radiation oncologists and included into MRI based consensus GTVs (_MRGTVs). BTVs were semi-automatically delineated with a fixed threshold of 1.6 x background activity. Corresponding BTVs and _MRGTVs were fused into union volume _PET-MRGTVs. The Sørensen–Dice coefficient and the conformity index were used to assess the geometric overlap of the BTVs with the _MRGTVs. A recurrence pattern analysis was performed based on the original planning target volumes (PTVs = GTV + 10 mm margin or 5 mm in one case) and the _PET-MRGTVs with margins of 10, 8, 5 and 3 mm.

Results

Seven recurrent glioma patients, who received PET-MRI prior to re-RT, were included into the present planning study. At the time of re-RT, patients were in median 54 years old and had a median Karnofsky Performance Status (KPS) score of 80. Median post-recurrence survival after the beginning of re-RT was 13 months. Concomitant bevacizumab therapy was applied in six patients and one patient received chemoradiation with temozolomide. Median GTV volumes of the three radiation oncologists were 35.0, 37.5 and 40.5 cubic centimeters (cc) and median _MRGTV volume 41.8 cc. Median BTV volume was 36.6 cc and median _PET-MRGTV volume 59.3 cc. The median Sørensen–Dice coefficient for the comparison between _MRGTV and BTV was 0.61 and the median conformity index 0.44. Recurrence pattern analysis revealed two central, two in-field and one distant recurrence within both, the original PTV, as well as the _PET-MRGTV with a reduced margin of 3 mm.

Conclusion

PET-MRI provides radiation treatment planning imaging with high spatial and timely conformity for high-grade glioma patients treated with re-RT with potential advancements for target volume delineation. Prospective randomised trials are warranted to further investigate the treatment benefits of PET-MRI based re-RT planning.

Higher SNR PET image prediction using a deep learning model and MRI image

PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model and MRI images without requiring any higher SNR PET images in training. Our proposed DNN model consists of three modified U-Nets (3U-net). The PET training input data and targets were reconstructed using filtered-backprojection (FBP) and maximum likelihood expectation maximization (MLEM), respectively. FBP reconstruction was used because of its computational efficiency so that the trained network not only removes noise, but also accelerates image reconstruction. Digital brain phantoms downloaded from BrainWeb were used to evaluate the proposed method. Poisson noise was added into sinogram data to simulate a 6 min brain PET scan. Attenuation effect was included and corrected before the image reconstruction. Extra Poisson noise was introduced to the training inputs to improve the network denoising capability. Three independent experiments were conducted to examine the reproducibility. A lesion was inserted into testing data to evaluate the impact of mismatched MRI information using the contrast-to-noise ratio (CNR). The negative impact on noise reduction was also studied when miscoregistration between PET and MRI images occurs. Compared with 1U-net trained with only PET images, training with PET/MRI decreased the mean squared error (MSE) by 31.3% and 34.0% for 1U-net and 3U-net, respectively. The MSE reduction is equivalent to an increase in the count level by 2.5 folds and 2.9 folds for 1U-net and 3U-net, respectively. Compared with the MLEM images, the lesion CNR was improved 2.7 folds and 1.4 folds for 1U-net and 3U-net, respectively. The results show that the proposed method could improve the PET SNR without having higher SNR PET images.

Cerebral metastases: do size, peritumoral edema, or multiplicity predict infiltration into brain parenchyma?

BACKGROUND

Brain metastases (BMs) are the most frequent malignancy of the central nervous system. Previous research suggested that some metastases show infiltrative behavior rather than sharp demarcation. We hypothesized that three magnetic resonance (MR) imaging parameters-(a) tumor size, (b) extent of peritumoral edema, and (c) presence of multiple BMs-are predictors of cellular invasion beyond the surgically identifiable tumor margins.

METHODS

We performed a post hoc analysis on prospectively collected data of patients with BMs. Biopsies beyond the resection margin and immunohistochemistry were performed to assess infiltration status. The three MR imaging parameters were dichotomized into diameters ≤ 30 mm ("small") and > 30 mm ("large"), amount of peritumoral edema "extended" and "limited," and "multiple BMs" and "single BMs," respectively. The association between infiltration status and imaging parameters was calculated using chi-square test.

RESULTS

Biopsy beyond the resection margin was performed in 77 patients; 49 (63.6%) had supramarginal infiltration and 28 patients (36.4%) showed no infiltration. Histological evidence of tumor infiltration was found in 25/41 patients with smaller lesions (61%) and in 24/36 with larger lesions (66.7%, p = 0.64), in 28/44 patients with limited (63.6%) and in 21/33 patients with extended edema (63.6%, p = 1.0), in 28/45 patients (62.2%) with single BM and in 21/32 patients (65.6%) with multiple BMs (p = 0.81).

CONCLUSIONS

Based on the post hoc analysis of our prospective trial data, we could not confirm the hypothesis that infiltration of brain parenchyma beyond the glial pseudocapsule is associated with the MR imaging parameters tumor size, extent of edema, or multiplicity of metastases.

Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers

Accurate and robust attenuation correction remains challenging in hybrid PET/MR particularly for torsos because it is difficult to segment bones, lungs and internal air in MR images. Additionally, MR suffers from susceptibility artifacts when a metallic implant is present. Recently, joint estimation (JE) of activity and attenuation based on PET data, also known as maximum likelihood reconstruction of activity and attenuation, has gained considerable interest because of (1) its promise to address the challenges in MR-based attenuation correction (MRAC), and (2) recent advances in time-of-flight (TOF) technology, which is known to be the key to the success of JE. In this paper, we implement a JE algorithm using an MR-based prior and evaluate the algorithm using whole-body PET/MR patient data, for both FDG and non-FDG tracers, acquired from GE SIGNA PET/MR scanners with TOF capability. The weight of the MR-based prior is spatially modulated, based on MR signal strength, to control the balance between MRAC and JE. Large prior weights are used in strong MR signal regions such as soft tissue and fat (i.e. MR tissue classification with a high degree of certainty) and small weights are used in low MR signal regions (i.e. MR tissue classification with a low degree of certainty). The MR-based prior is pragmatic in the sense that it is convex and does not require training or population statistics while exploiting synergies between MRAC and JE. We demonstrate the JE algorithm has the potential to improve the robustness and accuracy of MRAC by recovering the attenuation of metallic implants, internal air and some bones and by better delineating lung boundaries, not only for FDG but also for more specific non-FDG tracers such as 68Ga-DOTATOC and 18F-Fluoride.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

Glucose Metabolic Profile by Visual Assessment Combined with Statistical Parametric Mapping Analysis in Pediatric Patients with Epilepsy

PET with ¹⁸F-FDG has been used for presurgical localization of epileptogenic foci; however, in nonsurgical patients, the correlation between cerebral glucose metabolism and clinical severity has not been fully understood. The aim of this study was to evaluate the glucose metabolic profile using ¹⁸F-FDG PET/CT imaging in patients with epilepsy. Methods: One hundred pediatric epilepsy patients who underwent ¹⁸F-FDG PET/CT, MRI, and electroencephalography examinations were included. Fifteen age-matched controls were also included. ¹⁸F-FDG PET images were analyzed by visual assessment combined with statistical parametric mapping (SPM) analysis. The absolute asymmetry index (|AI|) was calculated in patients with regional abnormal glucose metabolism. Results: Visual assessment combined with SPM analysis of ¹⁸F-FDG PET images detected more patients with abnormal glucose metabolism than visual assessment only. The |AI| significantly positively correlated with seizure frequency (P < 0.01) but negatively correlated with the time since last seizure (P < 0.01) in patients with abnormal glucose metabolism. The only significant contributing variable to the |AI| was the time since last seizure, in patients both with hypometabolism (P = 0.001) and with hypermetabolism (P = 0.005). For patients with either hypometabolism (P < 0.01) or hypermetabolism (P = 0.209), higher |AI| values were found in those with drug resistance than with seizure remission. In the post-1-y follow-up PET studies, a significant change of |AI| (%) was found in patients with clinical improvement compared with those with persistence or progression (P < 0.01). Conclusion:¹⁸F-FDG PET imaging with visual assessment combined with SPM analysis could provide cerebral glucose metabolic profiles in nonsurgical epilepsy patients. |AI| might be used for evaluation of clinical severity and progress in these patients. Patients with a prolonged period of seizure freedom may have more subtle (or no) metabolic abnormalities on PET. The clinical value of PET might be enhanced by timing the scan closer to clinical seizures.