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Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible., (© 2023. The Author(s).)

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance-positron emission tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multicontrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR-guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service., (© 2019 Wiley Periodicals, Inc.)

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain-glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of "connectivity," only across-subject "covariance." Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby individual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity., (Copyright © 2020. Published by Elsevier Inc.)

Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain - cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18-20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.

Functional positron emission tomography (fPET) is a neuroimaging method involving continuous infusion of 18-F-fluorodeoxyglucose (FDG) radiotracer during the course of a PET examination. Compared with the conventional bolus administration of FDG in a static PET scan, which provides an average glucose uptake into the brain over an extended period of up to 30 ​min, fPET offers a significantly higher temporal resolution to study the dynamics of glucose uptake. Several earlier studies have applied fPET to investigate brain FDG uptake and study its relationship with functional magnetic resonance imaging (fMRI). However, due to the unique characteristics of fPET signals, modelling of the fPET signal is a complex task and poses challenges for accurate interpretation of the results from fPET experiments. This study applied independent component analysis (ICA) to analyse resting state fPET data, and to compare the performance of ICA and the general linear model (GLM) for estimation of brain activation in response to tasks. The fPET signal characteristics were compared using GLM and ICA methods to model fPET data from a visual activation experiment. Our aim was to evaluate GLM and ICA methods for analysing task fPET datasets, and to apply ICA methods to the analysis of resting state fPET datasets. Using both simulation and in-vivo experimental datasets, we show that both ICA and GLM methods can successfully identify task related brain activation. We report fPET metabolic resting state brain networks revealed by application of the fPET ICA method to a cohort of 28 healthy subjects. Functional PET provides a unique method to map dynamic changes of glucose uptake in the resting human brain and in response to extrinsic stimulation., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Mastering the "arcana of neuroimaging analysis", the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows. We present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, "end-to-end" workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites.

Functional positron emission tomography (fPET) provides a method to track molecular targets in the human brain. With a radioactively-labelled glucose analogue, 18 F-fluordeoxyglucose (FDG-fPET), it is now possible to measure the dynamics of glucose metabolism with temporal resolutions approaching those of functional magnetic resonance imaging (fMRI). This direct measure of glucose uptake has enormous potential for understanding normal and abnormal brain function and probing the effects of metabolic and neurodegenerative diseases. Further, new advances in hybrid MR-PET hardware make it possible to capture fluctuations in glucose and blood oxygenation simultaneously using fMRI and FDG-fPET. The temporal resolution and signal-to-noise of the FDG-fPET images is critically dependent upon the administration of the radiotracer. This work presents two alternative continuous infusion protocols and compares them to a traditional bolus approach. It presents a method for acquiring blood samples, time-locking PET, MRI, experimental stimulus, and administering the non-traditional tracer delivery. Using a visual stimulus, the protocol results show cortical maps of the glucose-response to external stimuli on an individual level with a temporal resolution of 16 s.

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.

Accelerated parallel MRI has advantage in imaging speed, and its image quality has been improved continuously in recent years. This paper introduces a two-dimensional infinite impulse response model of inverse filter to replace the finite impulse response model currently used in generalized autocalibrating partially parallel acquisitions class image reconstruction methods. The infinite impulse response model better characterizes the correlation of k-space data points and better approximates the perfect inversion of parallel imaging process, resulting in a novel generalized image reconstruction method for accelerated parallel MRI. This k-space-based reconstruction method includes the conventional generalized autocalibrating partially parallel acquisitions class methods as special cases and has a new infinite impulse response data estimation mechanism for effective improvement of image quality. The experiments on in vivo MRI data show that the proposed method significantly reduces reconstruction errors compared with the conventional two-dimensional generalized autocalibrating partially parallel acquisitions method, particularly at the high acceleration rates.

Dynamic magnetic resonance imaging (MRI) acquires a sequence of images for the visualization of the temporal variation of tissue or organs. Keyhole methods such as Fourier keyhole (FK) and keyhole SVD (KSVD) are the most popular methods for image reconstruction in dynamic MRI. This paper provides a class of adaptive keyhole methods, called adaptive FK (AFK) and adaptive KSVD (AKSVD), for dynamic MRI reconstruction. The proposed methods are based on the conventional Fourier encoding and SVD encoding schemes. Instead of the conventional keyhole methods' duplication of un-acquired data from the reference images, the proposed methods use a temporal model to depict the inter-frame dynamic changes and to estimate the un-acquired data in each successive frame. Because the model is online identified from the acquired data, the proposed methods do not require the pre-imaging process, the navigator signals, and any prior knowledge of the imaged objects. Furthermore, the new methods use the conventional keyhole encoding schemes without the bias to any particular object characters, and the temporal model for updating information is in the general form of AR process without the preference to any particular motion types. Hence, the proposed methods are designed as a generic approach to dynamic MRI, other than for any specific class of objects. Studies on dynamic MRI data set show that the new methods can produce images with much lower reconstruction error than the conventional FK and KSVD.