Human Brain Imaging Research Articles

Phase-based fast 3D high-resolution quantitative T2 MRI in 7 T human brain imaging

Magnetic resonance imaging (MRI) is a powerful and versatile technique that offers a range of physiological, diagnostic, structural, and functional measurements. One of the most widely used basic contrasts in MRI diagnostics is transverse relaxation time (T2)-weighted imaging, but it provides only qualitative information. Realizing quantitative high-resolution T2 mapping is imperative for the development of personalized medicine, as it can enable the characterization of diseases progression. While ultra-high-field (≥ 7 T) MRI offers the means to gain new insights by increasing the spatial resolution, implementing fast quantitative T2 mapping cannot be achieved without overcoming the increased power deposition and radio frequency (RF) field inhomogeneity at ultra-high-fields. A recent study has demonstrated a new phase-based T2 mapping approach based on fast steady-state acquisitions. We extend this new approach to ultra-high field MRI, achieving quantitative high-resolution 3D T2 mapping at 7 T while addressing RF field inhomogeneity and utilizing low flip angle pulses; overcoming two main ultra-high field challenges. The method is based on controlling the coherent transverse magnetization in a steady-state gradient echo acquisition; achieved by utilizing low flip angles, a specific phase increment for the RF pulses, and short repetition times. This approach simultaneously extracts both T2 and RF field maps from the phase of the signal. Prior to in vivo experiments, the method was assessed using a 3D head-shaped phantom that was designed to model the RF field distribution in the brain. Our approach delivers fast 3D whole brain images with submillimeter resolution without requiring special hardware, such as multi-channel transmit coil, thus promoting high usability of the ultra-high field MRI in clinical practice.

A Hybrid Neural Network Electromagnetic Inversion Scheme (HNNEMIS) for Super-Resolution 3-D Microwave Human Brain Imaging

Super-resolution three-dimensional (3-D) electromagnetic (EM) inversion for microwave human brain imaging is a typical high contrast EM inverse problem and requires huge computational costs. This work proposes a hybrid neural network electromagnetic inversion scheme (HNNEMIS) which contains shallow and deep neural networks to alleviate the required huge computational costs and solve this high contrast inverse problem. In the proposed scheme, semi-join back propagation neural network (SJ-BPNN) is employed to nonlinearly map the measured scattered electric field to two output channels, namely the permittivity and conductivity of scatterers, respectively. Such a semi-join strategy decreases the computational burden in training and testing processes. Then, a deep learning technique, termed U-Net, is employed to further enhance the imaging quality of the output from SJ-BPNN. To decrease the training cost and make neural networks fast convergent for human brain inversion, a novel training dataset construction strategy which contains the characteristics of human brain is also proposed. Noise-free and noisy numerical examples demonstrate that HNNEMIS has superior super-resolution inversion capabilities for human brain imaging.

An optimized quadrature RF receive coil for very-low-field (50.4 mT) magnetic resonance brain imaging

The radiofrequency (RF) receive coil is a direct probe for magnetic resonance imaging (MRI), and its performance determines the quality of MRI results. The RF coil employed for low-field MRI has a low working frequency, which makes its characteristic different from the RF coil exploited for conventional clinic MRI and may result in a different optimum RF coil configuration. To design and optimize a head RF receive coil for a very-low-field (50.4 mT) MRI system, we investigated the relationship between the structure and performance of the RF coil. Specifically, we focused on a quadrature RF coil consisting of a saddle coil and a modified Helmholtz coil wound around the surface of an elliptical cylinder. First, we evaluated the efficiency and RF magnetic field inhomogeneity of one-loop RF coil and determined the optimum dimension for saddle coil and modified Helmholtz RF coil. Then, we further studied the performance of the optimum-dimension RF coil from the perspective of the number of RF coil loops and revealed that the number of loops of RF coil for very-low-field MRI was a remarkable feature influencing the alternative current (AC) resistance of the RF coil and therefore make the SNR increase first and then decrease with the number of RF coil loops. We finally obtained the optimum number of loops for the saddle coil, modified Helmholtz coil, and fabricated a quadrature RF coil. The performance of the quadrature coil was evaluated through CuSO4 phantom imaging and in vivo human brain imaging. In phantom imaging, the SNR of quadrature RF coil increased by about 40% compared with that of single-channel RF coil.

Retracted: Denoising and segmentation of brain image by proficient blended threshold and conserve edge scrutinize technique

AbstractUnusual collection of mass tissue in human body is commonly refereed as tumor. The tumor when it is found in brain. These tumor cells have tendency to multiply and grow in rapid speed. The growth of tumors is generally uncontrollable in nature. Tumor in brain develops along the skull and it evolves to contact with the functioning of the brain. Brain tumor (BT) can be detected at the earlier stages with the help of MRI or CT scan techniques. These scanning techniques are proved to be efficient in detecting the tumor irrespective of its size. Brain tumor being a life‐threatening disease has to be diagnosed at the earliest before it turns to be malignant. The current research work focuses in proposing an efficient image processing techniques by processing the MRI images of human brain which is affected by brain tumor. This process is carried out in two stages as image denoising and image segmentation which are helpful in detecting and localizing the tumor affected region in human brain. Initially, the MRI image is read and preprocessed by converting the input image into a grayscale image and noise removed by involving the proposed method. Later, the proposed image is segmented using sobel edge detection method and the image is enhanced using the image enhancement techniques. It is achieved by using the proficient blended thresholding (PBT) Segmentation method. The performance of the proposed methods is evaluated using PSNR (peak signal noise ratio) and RMSE (root mean square error). The proposed conserve edge scrutinize (CES) filter achieved highest PSNR value. Then segmentation is evaluated by five metrices: Sensitivity, Specificity, Dice, Jaccard, and Accuracy.

Depression induced by low-frequency repetitive transcranial magnetic stimulation to ventral medial frontal cortex in monkeys

The medial frontal cortex (MFC), especially its ventral part, has long been of great interest with respect to the pathology of mood disorders. A number of human brain imaging studies have demonstrated the abnormalities of this brain region in patients with mood disorders, however, whether it is critically and causally involved in the pathogenesis of such disorders remains to be fully elucidated. In this study, we examined how the suppression of neural activity in the ventral region of the MFC (vMFC) affects the behavioral and physiological states of monkeys by using repetitive transcranial magnetic stimulation (rTMS). By using low-frequency rTMS (LF-rTMS) as an inhibitory intervention, we found that LF-rTMS targeting the vMFC temporarily induced a depression-like state in monkeys, which was characterized by a reduced movement activity level, impaired sociability, and decreased motivation level, as well as increased plasma cortisol level. On the other hand, no such significant changes in behavioral and physiological states were observed when targeting the other MFC regions, dorsal or posterior. We further found that the administration of an antidepressant agent, ketamine, ameliorated the abnormal behavioral and physiological states induced by the LF-rTMS intervention. These findings causally indicate the involvement of the vMFC in the regulation of mood and the validity of the LF-rTMS-induced dysfunction of the vMFC as a nonhuman primate model of depression.

MRI and CT image indexing and retrieval using steerable pyramid transform and local neighborhood difference pattern

This paper proposes an effective system for indexing and retrieval of medical images, which uses steerable pyramid transform (SPT) and local neighborhood difference patterns (LNDP). In the first step of the proposed system, a medical image is subdivided into several sub-bands using SPT at different resolutions and orientations. Further, each sub-band is encoded using LNDP in a binary texture pattern to extract more complex texture features. Finally, LNDP histograms are computed for each SPT sub-band. Thereafter, all the sub-band histograms are combined to form a single histogram to produce the required feature vector. The proposed SPT-LNDP technique represents a medical image in an efficient way that contains many sources of information from various scales and orientations. Two open-source medical image datasets namely OASIS- MRI and VIA/I-ELCAP-CT images of human brain were considered for efficient evaluation of the proposed method. To validate the efficacy of the proposed method two parameters namely Average Retrieval Precision (ARP) and Average Retrieval Rate (ARR) were used. A detailed comparative study of the proposed method with other popular methods, which are described in this study, are also presented in this work. The results show improvement over other existing methods considered in this study.

Epi-fluorescence imaging of the human brain through a multimode fiber

Visualization of the subcellular structures deep into the living brain is a major challenge in life science. Miniaturized microendoscopes allow for imaging of deep brain structures in vivo. Conventional approaches use gradient index (GRIN) microlenses, which unfortunately suffer from greater aberrations and restricted fields of view if they become smaller and less invasive. Multimode fiber based endoscopes offer minimal invasive access to deep tissue, and when combined with advanced wavefront engineering techniques, they provide high-resolution imaging. Here, we report auto-fluorescence human brain imaging through a single 50 μm-core multimode fiber probe with a numerical aperture of 0.22 via two approaches: raster-scan imaging by active wavefront shaping and speckle-based compressive imaging enabled by computational image recovery. The compressive imaging approach significantly decreases the acquisition time for an up to three times bigger area of interest while maintaining a high spatial resolution. Accumulation of age-related pigment lipofuscin in Alzheimer’s disease human brain has been visualized with sub-Nyquist–Shannon speed with an improvement of up to 18 times. The proposed technique offers fast, sensitive, and high-resolution endoscopic imaging through a single hair-thin fiber, which would be of broader interest in the fields of neuroimaging and (pre-)clinical research.

Double-row dipole/loop combined array for human whole brain imaging at 7T.

Important issues in designing radiofrequency (RF) coils for human head imaging at ultra-high field (UHF; ≥7T) are the inhomogeneity and longitudinal coverage (along the magnet axis) of the transmit (Tx) RF field. Both the homogeneity and coverage produced by Tx volume coils can be improved by means of three-dimensional (3D) RF shimming, which requires the use of multirow Tx-arrays. In addition, according to recent findings of the ultimate intrinsic signal-to-noise ratio (UISNR) theory, the loop-only receive (Rx) arrays do not provide optimal SNR near the brain center at UHF. The latter can be obtained by combining complementary conductive structures carrying different current patterns (e.g., loops and dipole antennas). In this work, we developed, constructed, and evaluated a novel 32-element hybrid array design for human head imaging at 7T. The array consists of 16 transceiver loops placed in two rows circumscribing the head and 16 folded-end Rx-only dipoles positioned in the centers of loops. By placing all elements in a single layer, we increased RF power deposition into the tissue and, thus, preserved the Tx-efficiency. Using this hybrid design also simplifies the coil structure by minimizing the total number of array elements. The array demonstrated whole brain coverage, 3D RF shimming capability, and high SNR. It provided ~15% higher SNR near the brain center and, depending on the RF shim mode, from 20% to 40% higher Tx-efficiency than a common commercial head array coil.

Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network.

BACKGROUNDA convolutional neural network (CNN) is a deep learning algorithm based on the principle of human brain visual cortex processing and image recognition.AIMTo automatically identify the invasion depth and origin of esophageal lesions based on a CNN.METHODSA total of 1670 white-light images were used to train and validate the CNN system. The method proposed in this paper included the following two parts: (1) Location module, an object detection network, locating the classified main image feature regions of the image for subsequent classification tasks; and (2) Classification module, a traditional classification CNN, classifying the images cut out by the object detection network.RESULTSThe CNN system proposed in this study achieved an overall accuracy of 82.49%, sensitivity of 80.23%, and specificity of 90.56%. In this study, after follow-up pathology, 726 patients were compared for endoscopic pathology. The misdiagnosis rate of endoscopic diagnosis in the lesion invasion range was approximately 9.5%; 41 patients showed no lesion invasion to the muscularis propria, but 36 of them pathologically showed invasion to the superficial muscularis propria. The patients with invasion of the tunica adventitia were all treated by surgery with an accuracy rate of 100%. For the examination of submucosal lesions, the accuracy of endoscopic ultrasonography (EUS) was approximately 99.3%. Results of this study showed that EUS had a high accuracy rate for the origin of submucosal lesions, whereas the misdiagnosis rate was slightly high in the evaluation of the invasion scope of lesions. Misdiagnosis could be due to different operating and diagnostic levels of endoscopists, unclear ultrasound probes, and unclear lesions.CONCLUSIONThis study is the first to recognize esophageal EUS images through deep learning, which can automatically identify the invasion depth and lesion origin of submucosal tumors and classify such tumors, thereby achieving good accuracy. In future studies, this method can provide guidance and help to clinical endoscopists.

Artificial Intelligent Algorithm Methodology for the Human Brain Image Segmentation & Classifier in the Medical Application Domain

Artificial Neural Network has the application in classification domain. Intelligent algorithm will work in the practices where human can control the anger & it will work in the segmentation domain to check the level of the anger. Intelligent algorithm will extract the images of human brains for certain region where the brain image will classified in the certain area and the classifier will the area for the stimulation of the anger. Intelligent algorithm will give the solution on the segmentation & classification methods to reduce the anger of human being. The methodology is the classification & segmentation of human brain images. Key Words — Intelligent Algorithm, Human Brain Image, Segmentation, Classifier

Simultaneous high-resolution T2 -weighted imaging and quantitative T2 mapping at low magnetic field strengths using a multiple TE and multi-orientation acquisition approach.

PurposeLow magnetic field systems provide an important opportunity to expand MRI to new and diverse clinical and research study populations. However, a fundamental limitation of low field strength systems is the reduced SNR compared to 1.5 or 3T, necessitating compromises in spatial resolution and imaging time. Most often, images are acquired with anisotropic voxels with low through‐plane resolution, which provide acceptable image quality with reasonable scan times, but can impair visualization of subtle pathology.MethodsHere, we describe a super‐resolution approach to reconstruct high‐resolution isotropic T2‐weighted images from a series of low‐resolution anisotropic images acquired in orthogonal orientations. Furthermore, acquiring each image with an incremented TE allows calculations of quantitative T2 images without time penalty.ResultsOur approach is demonstrated via phantom and in vivo human brain imaging, with simultaneous 1.5 × 1.5 × 1.5 mm3 T2‐weighted and quantitative T2 maps acquired using a clinically feasible approach that combines three acquisition that require approximately 4‐min each to collect. Calculated T2 values agree with reference multiple TE measures with intraclass correlation values of 0.96 and 0.85 in phantom and in vivo measures, respectively, in line with previously reported brain T2 values at 150 mT, 1.5T, and 3T.ConclusionOur multi‐orientation and multi‐TE approach is a time‐efficient method for high‐resolution T2‐weighted images for anatomical visualization with simultaneous quantitative T2 imaging for increased sensitivity to tissue microstructure and chemical composition.

Quantitative ultrasound brain imaging with multiscale deconvolutional waveform inversion

High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner. Sound speed mapping of brain tissues provides unique information for such a purpose. In addition, it is particularly important for building digital human acoustic models, which form a reference for future ultrasound research. Conventional ultrasound modalities can hardly image the human brain at high spatial resolution inside the skull due to the strong impedance contrast between hard tissue and soft tissue. We carry out numerical experiments to demonstrate that the time-domain waveform inversion technique, originating from the geophysics community, is promising to deliver quantitative images of human brains within the skull at a sub-millimeter level by using ultra-sound signals. The successful implementation of such an approach to brain imaging requires the following items: signals of sub-megahertz frequencies transmitting across the inside of skull, an accurate numerical wave equation solver simulating the wave propagation, and well-designed inversion schemes to reconstruct the physical parameters of targeted model based on the optimization theory. Here we propose an innovative modality of multiscale deconvolutional waveform inversion that improves ultrasound imaging resolution, by evaluating the similarity between synthetic data and observed data through using limited length Wiener filter. We implement the proposed approach to iteratively update the parametric models of the human brain. The quantitative imaging method paves the way for building the accurate acoustic brain model to diagnose associated diseases, in a potentially more portable, more dynamic and safer way than magnetic resonance imaging and x-ray computed tomography.

Deuterium metabolic imaging and hyperpolarized 13C-MRI of the normal human brain at clinical field strength reveals differential cerebral metabolism

Deuterium metabolic imaging (DMI) and hyperpolarized 13C-pyruvate MRI (13C-HPMRI) are two emerging methods for non-invasive and non-ionizing imaging of tissue metabolism. Imaging cerebral metabolism has potential applications in cancer, neurodegeneration, multiple sclerosis, traumatic brain injury, stroke, and inborn errors of metabolism. Here we directly compare these two non-invasive methods at 3 T for the first time in humans and show how they simultaneously probe both oxidative and non-oxidative metabolism. DMI was undertaken 1–2 h after oral administration of [6,6′-2H2]glucose, and 13C-MRI was performed immediately following intravenous injection of hyperpolarized [1–13C]pyruvate in ten and nine normal volunteers within each arm respectively. DMI was used to generate maps of deuterium-labelled water, glucose, lactate, and glutamate/glutamine (Glx) and the spectral separation demonstrated that DMI is feasible at 3 T. 13C-HPMRI generated maps of hyperpolarized carbon-13 labelled pyruvate, lactate, and bicarbonate. The ratio of 13C-lactate/13C-bicarbonate (mean 3.7 ± 1.2) acquired with 13C-HPMRI was higher than the equivalent 2H-lactate/2H-Glx ratio (mean 0.18 ± 0.09) acquired using DMI. These differences can be explained by the route of administering each probe, the timing of imaging after ingestion or injection, as well as the biological differences in cerebral uptake and cellular physiology between the two molecules. The results demonstrate these two metabolic imaging methods provide different yet complementary readouts of oxidative and reductive metabolism within a clinically feasible timescale. Furthermore, as DMI was undertaken at a clinical field strength within a ten-minute scan time, it demonstrates its potential as a routine clinical tool in the future.

Landmark-guided region-based spatial normalization for functional magnetic resonance imaging.

As the size of the neuroimaging cohorts being increased to address key questions in the field of cognitive neuroscience, cognitive aging, and neurodegenerative diseases, the accuracy of the spatial normalization as an essential preprocessing step becomes extremely important. Existing spatial normalization methods have poor accuracy particularly when dealing with the highly convoluted human cerebral cortex and when brain morphology is severely altered (e.g., aging populations). To address this shortcoming, we propose a novel spatial normalization technique that takes advantage of the existing surface‐based human brain parcellation to automatically identify and match regional landmarks. To simplify the nonlinear whole brain registration, the identified landmarks of each region and its counterpart are registered independently with topology‐preserving deformation. Next, the regional warping fields are combined by an inverse distance weighted interpolation technique to have a global warping field for the whole brain. To ensure that the final warping field is topology‐preserving, we used simultaneously forward and reverse maps with certain symmetric constraints to yield bijectivity. We have evaluated our proposed solution using both simulated and real (structural and functional) human brain images. Our evaluation shows that our solution can enhance structural correspondence compared to the existing methods. Such improvement also increases the sensitivity and specificity of the functional imaging studies, reducing the required number of subjects and subsequent study costs. We conclude that our proposed solution can effectively substitute existing substandard spatial normalization methods to deal with the demand of large cohorts which is now common in clinical and aging studies.

Human song: Separate neural pathways for melody and speech

Does the brain perceive song as speech with melody? A new study using intracranial recordings and functional brain imaging in humans suggests that it does not. Instead, singing, instrumental music, and speech are represented by different neural populations.

Preclinical evaluation of a microtubule PET ligand [11C]MPC-6827 in tau and amyotrophic lateral sclerosis animal models.

Microtubules are abundant in brain and their malfunctioning occurs in the early-to-advanced stages of neurodegenerative disorders. At present, there is no in vivo test available for a definitive diagnosis of most of the neurodegenerative disorders. Herein, we present the microPET imaging of microtubules using our recently reported Positron Emission Tomography (PET) tracer, [11C]MPC-6827, in transgenic mice models of tau pathology (rTg4510) and amyotrophic lateral sclerosis pathology (SOD1*G93A) and compared to corresponding age-matched controls. Automated synthesis of [11C]MPC-6827 was achieved in a GE-FX2MeI/FX2M radiochemistry module. In vivo PET imaging studies of [11C]MPC-6827 (3.7 ± 0.8MBq) were performed in rTg4510 and SOD1*G93A mice groups and their corresponding littermates (n = 5 per group). Dynamic PET images were acquired using a microPET Inveon system (Siemens, Germany) at 55min for rTg4510 and 30min for SOD1*G93A and corresponding controls. PET images were reconstructed using the 3D-OSEM algorithm and analyzed using VivoQuant version 4 (Invicro, MA). Tracer uptake in ROIs that included whole brain was measured as %ID/g over time to generate standardized uptake values (SUV) and time-activity curves (TACs). [11C]MPC-6827 exhibit a trend of lower tracer binding in mouse models of Alzheimer's disease (tau pathology, line rTg4510) and Amyotrophic Lateral Sclerosis (line SOD1*G93A) compared to wild-type littermates. Our finding indicates a trend of loss of microtubule binding of [11C]MPC-6827 in the whole brain of AD and ALS transgenic mice models compared to control mice. The pilot studies described herein show that [11C]MPC-6827 could be used as a PET ligand for preclinical and human brain imaging of Alzheimer's disease, ALS, and other neurodegenerative diseases. Preclinical Evaluation of a Microtubule PET Ligand [11C]MPC-6827 in Tau and Amyotrophic Lateral Sclerosis Animal Models. J. S. Dileep Kumar, Andrei Molotkov, Jongho Kim, Patrick Carberry, Sidney Idumonyi, John Castrillon, Karen Duff, Neil A. Shneider, Akiva Mintz.

Differential Sphingosine-1-Phosphate Receptor-1 Protein Expression in the Dorsolateral Prefrontal Cortex Between Schizophrenia Type 1 and Type 2.

Understanding the etiology and treatment approaches in schizophrenia is challenged in part by the heterogeneity of this disorder. One encouraging progress is the growing evidence that there are subtypes of schizophrenia. Recent in vitro findings of messenger ribonucleic acid (mRNA) gene expression on postmortem dorsolateral prefrontal cortex (DLPFC) showed that schizophrenia has two subtypes, those with a relatively normal DLPFC transcriptome (Type 1) and those with differentially expressed genes (Type 2). Sphingosine-1-phosphate receptor-1 (S1PR1) is one of the genes that was highly upregulated in Type 2 compared to Type 1 and controls. The impact of that finding is limited because it only can be confirmed through analysis of autopsy tissue, and the clinical characteristics such as symptoms severity or illness duration except for cause of death was not available from that Medical Examiner based autopsy study. However, S1PR1 has great potential because it is a target gene that can be accessed via positron emission tomography (PET) in vivo using specific radioligands (starting with [11C]CS1P1) successfully developed at our center in human brain imaging. As a preliminary study to validate this PET target in schizophrenia, S1PR1 protein expression was assessed by receptor autoradiography (ARG) using [3H]CS1P1 and immunohistochemistry (IHC) in the DLPFC from patients with schizophrenia classified as Type 1 or Type 2 based on their DLPFC transcriptomes and from controls. Our analyses demonstrate that ARG S1PR1 protein expression is significantly higher in Type 2 compared to Type 1 (p < 0.05) and controls (p < 0.05), which was consistent with previous mRNA S1PR1. These findings support the possibility that PET S1PR1 can be used as a future imaging biomarker to distinguish these subgroups of schizophrenic patients during life with obvious implications for both patient management and the design of clinical trials to validate novel pharmacologic therapies.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging for Resting-State Functional and Metabolic Imaging in Human Brain: What Is Correlated and What Is Impacted.

Integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) could simultaneously obtain both functional MRI (fMRI) and 18F-fluorodeoxyglucose (FDG) PET and thus provide multiparametric information for the analysis of brain metabolism. In this study, we aimed to, for the first time, investigate the interplay of simultaneous fMRI and FDG PET scan using a randomized self-control protocol. In total, 24 healthy volunteers underwent PET/MRI scan for 30–40 min after the injection of FDG. A 22-min brain scan was separated into MRI-off mode (without fMRI pulsing) and MRI-on mode (with fMRI pulsing), with each one lasting for 11 min. We calculated the voxel-wise fMRI metrics (regional homogeneity, amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and degree centrality), resting networks, relative standardized uptake value ratios (SUVr), SUVr slope, and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, SUVr slope, correlation coefficients of fMRI metrics, and rCMRGlu between MRI-off and MRI-on modes, respectively. The voxel-wise whole-brain SUVr revealed no statistical difference (P > 0.05), while the SUVr slope was significantly elevated in sensorimotor, dorsal attention, ventral attention, control, default, and auditory networks (P < 0.05) during fMRI scan. The task-based group independent-component analysis revealed that the most active network components derived from the combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus, and occipital pole. High correlation coefficients were found among fMRI metrics with rCMRGlu in both MRI-off and MRI-on mode (P < 0.05). Our results systematically evaluated the impact of simultaneous fMRI scan on the quantification of human brain metabolism from an integrated PET/MRI system.

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [11C]PHNO brain PET-MR studies

IntroductionQ.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR.MethodsSix [11C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β100–1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BPND) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKAT™ software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from Bland–Altman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons.ResultsWhen comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43).ConclusionBPND results obtained from quantitative low count brain PET studies using [11C]PHNO and reconstructed with Q.Clear with β < 400, which is the value used for clinical [18F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM.

Whole-brain opto-fMRI map of mouse VTA dopaminergic activation reflects structural projections with small but significant deviations

Ascending dopaminergic projections from neurons located in the Ventral Tegmental Area (VTA) are key to the etiology, dysfunction, and control of motivation, learning, and addiction. Due to the evolutionary conservation of this nucleus and the extensive use of mice as disease models, establishing an assay for VTA dopaminergic signaling in the mouse brain is crucial for the translational investigation of motivational control as well as of neuronal function phenotypes for diseases and interventions. In this article we use optogenetic stimulation directed at VTA dopaminergic neurons in combination with functional Magnetic Resonance Imaging (fMRI), a method widely used in human deep brain imaging. We present a comprehensive assay producing the first whole-brain opto-fMRI map of dopaminergic activation in the mouse, and show that VTA dopaminergic system function is consistent with its structural VTA projections, diverging only in a few key aspects. While the activation map predominantly highlights target areas according to their relative projection densities (e.g., strong activation of the nucleus accumbens and low activation of the hippocampus), it also includes areas for which a structural connection is not well established (such as the dorsomedial striatum). We further detail the variability of the assay with regard to multiple experimental parameters, including stimulation protocol and implant position, and provide evidence-based recommendations for assay reuse, publishing both reference results and a reference analysis workflow implementation.