Optimizing the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence

Quantifying cerebral microbleeds using quantitative susceptibility mapping from magnetization-prepared rapid gradient-echo

T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) is commonly included in brain studies for structural imaging using magnitude images; however, its phase images can provide an opportunity to assess microbleed burden using quantitative susceptibility mapping (QSM). This potential application for MPRAGE-based QSM was evaluated using in vivo and simulated measurements. Possible factors affecting image quality were also explored. Detection sensitivity was evaluated against standard multiecho gradient echo (MEGE) QSM using 3-T in vivo data of 15 subjects with a combined total of 108 confirmed microbleeds. The two methods were compared based on the microbleed size and susceptibility measurements. In addition, simulations explored the detection sensitivity of MPRAGE-QSM at different representative magnetic field strengths and echo times using microbleeds of different size, susceptibility, and location. Results showed that in vivo microbleeds appeared to be smaller (× 0.54) and of higher mean susceptibility (× 1.9) on MPRAGE-QSM than on MEGE-QSM, but total susceptibility estimates were in closer agreement (slope: 0.97, r2: 0.94), and detection sensitivity was comparable. In simulations, QSM at 1.5 T had a low contrast-to-noise ratio that obscured the detection of many microbleeds. Signal-to-noise ratio (SNR) levels at 3 T and above resulted in better contrast and increased detection. The detection rates for microbleeds of minimum one-voxel diameter and 0.4-ppm susceptibility were 0.55, 0.80, and 0.88 at SNR levels of 1.5, 3, and 7 T, respectively. Size and total susceptibility estimates were more consistent than mean susceptibility estimates, which showed size-dependent underestimation. MPRAGE-QSM provides an opportunity to detect and quantify the size and susceptibility of microbleeds of at least one-voxel diameter at B0 of 3 T or higher with no additional time cost, when standard T2*-weighted images are not available or have inadequate spatial resolution. The total susceptibility measure is more robust against sequence variations and might allow combining data from different protocols.

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach

BACKGROUND

MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications.

METHOD

The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features.

RESULTS

The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN.

CONCLUSION

The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

Altered neuronal response to visual food stimuli in adolescents undergoing chronic sleep restriction

STUDY OBJECTIVES

Poor sleep in adolescents can increase the risk of obesity, possibly due to changes in dietary patterns. Prior neuroimaging evidence, mostly in adults, suggests that lacking sleep results in increased response to food cues in reward-processing brain regions. Needed is a clarification of the mechanisms by which food reward processing is altered by the kind of chronic sleep restriction (SR) typically experienced by adolescents. This study aimed to elucidate the impact of sleep duration on response to visual food stimuli in healthy adolescents using functional neuroimaging, hypothesizing increased reward processing response after SR compared to a well-rested condition.

METHODS

Thirty-nine healthy adolescents, 14-17 years old, completed a 3-week protocol: (1) sleep phase stabilization; (2) SR (~6.5 h nightly); and (3) healthy sleep (HS) duration (~9 h nightly). Participants underwent functional MRI while performing a visual food paradigm. Contrasts of food versus nonfood responses were compared within-subject between conditions of SR and HS.

RESULTS

Under SR, there was a greater response to food stimuli compared to HS in a voxel cluster including the left ventral tegmental area and substantia nigra. No change in food appeal rating due to the sleep manipulation was detected.

CONCLUSIONS

Outcomes of this study suggest that SR, as commonly experienced by healthy adolescents, results in the elevated dopaminergic drive of the reward network that may augment motivation to seek food in the context of individual food appeal and inhibitory profiles. Countermeasures that reduce food salience could include promoting consistent HS habits.

Differential Assessment of Internal Jugular Vein Stenosis in Patients Undergoing CT and MRI with Contrast

OBJECTIVE

Internal Jugular Vein Stenosis (IJVS) is hypothesized to play a role in the pathogenesis of diverse neurological diseases. We sought to evaluate differences in IJVS assessment between CT and MRI in a retrospective patient cohort.

METHODS

We included consecutive patients who had both MRI of the brain and CT of the head and neck with contrast from 1 June 2021 to 30 June 2022 within the same admission. The degree of IJVS was categorized into five grades (0-IV).

RESULTS

A total of 35 patients with a total of 70 internal jugular (IJ) veins were included in our analysis. There was fair intermodality agreement in stenosis grades (κ = 0.220, 95% C.I. = [0.029, 0.410]), though categorical stenosis grades were significantly discordant between imaging modalities, with higher grades more frequent in MRI (χ2 = 27.378, p = 0.002). On CT-based imaging, Grade III or IV stenoses were noted in 17/70 (24.2%) IJs, whereas on MRI-based imaging, Grade III or IV stenoses were found in 40/70 (57.1%) IJs. Among veins with Grade I-IV IJVS, MRI stenosis estimates were significantly higher than CT stenosis estimates (77.0%, 95% C.I. [35.9-55.2%] vs. 45.6%, 95% C.I. [35.9-55.2%], p < 0.001).

CONCLUSION

MRI with contrast overestimates the degree of IJVS compared to CT with contrast. Consideration of this discrepancy should be considered in diagnosis and treatment planning in patients with potential IJVS-related symptoms.

Acquisition-invariant brain MRI segmentation with informative uncertainties

Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and, therefore, any downstream analyses. Post-hoc multi-site correction methods exist but have strong assumptions that often do not hold in real-world scenarios. Algorithms should be designed in a way that can account for site-specific effects, such as those that arise from sequence parameter choices, and in instances where generalisation fails, should be able to identify such a failure by means of explicit uncertainty modelling. This body of work showcases such an algorithm that can become robust to the physics of acquisition in the context of segmentation tasks while simultaneously modelling uncertainty. We demonstrate that our method not only generalises to complete holdout datasets, preserving segmentation quality but does so while also accounting for site-specific sequence choices, which also allows it to perform as a harmonisation tool.

Improved gray-white matter contrast using magnetization prepared fast imaging with steady-state free precession (MP-FISP) brain imaging at 0.55 T

PURPOSE

To improve the gray/white matter contrast of magnetization prepared rapid gradient echo (MP-RAGE) MRI at 0.55 T by optimizing the acquisition and sequence kernel parameters.

METHODS

A segmented magnetization prepared rapid gradient echo prototype sequence was implemented with (MP-RAGE*) and without (MP-FISP*) radiofrequency spoiling. Optimized parameters were derived with the assistance of an extended phase graph signal simulation as a function of the relaxation times, the flip angle, the delay times, and the effective inversion time using segmentation. The resulting protocols were compared to the MP-RAGE product sequence offered by the vendor in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). A tissue segmentation reproducibility study was performed on three volunteers for the product MP-RAGE and the MP-FISP*.

RESULTS

The MP-RAGE simulation reproduced the parameters already used in the product MP-RAGE on the scanner. An average CNR improvement of 15% for the custom MP-RAGE* over the product MP-RAGE and additional 22% for the MP-FISP* over the MP-RAGE* were observed, which is in accordance with the simulation results. The total improvement, averaged over all volunteers and regions, was 41%. The reproducibility study did not yield a significant difference between MP-RAGE and MP-FISP*.

CONCLUSION

We presented some easy-to-implement adjustments to the MP-RAGE sequence at 0.55 T, which can lead to an overall average improvement of 41% in CNR.

Assessment of the Impact of Turbo Factor on Image Quality and Tissue Volumetrics in Brain Magnetic Resonance Imaging Using the Three-Dimensional T1-Weighted (3D T1W) Sequence

Background

The 3D T1W turbo field echo sequence is a standard imaging method for acquiring high-contrast images of the brain. However, the contrast-to-noise ratio (CNR) can be affected by the turbo factor, which could affect the delineation and segmentation of various structures in the brain and may consequently lead to misdiagnosis. This study is aimed at evaluating the effect of the turbo factor on image quality and volumetric measurement reproducibility in brain magnetic resonance imaging (MRI).

Methods

Brain images of five healthy volunteers with no history of neurological diseases were acquired on a 1.5 T MRI scanner with varying turbo factors of 50, 100, 150, 200, and 225. The images were processed and analyzed with FreeSurfer. The influence of the TFE factor on image quality and reproducibility of brain volume measurements was investigated. Image quality metrics assessed included the signal-to-noise ratio (SNR) of white matter (WM), CNR between gray matter/white matter (GM/WM) and gray matter/cerebrospinal fluid (GM/CSF), and Euler number (EN). Moreover, structural brain volume measurements of WM, GM, and CSF were conducted.

Results

Turbo factor 200 produced the best SNR (median = 17.01) and GM/WM CNR (median = 2.29), but turbo factor 100 offered the most reproducible SNR (IQR = 2.72) and GM/WM CNR (IQR = 0.14). Turbo factor 50 had the worst and the least reproducible SNR, whereas turbo factor 225 had the worst and the least reproducible GM/WM CNR. Turbo factor 200 again had the best GM/CSF CNR but offered the least reproducible GM/CSF CNR. Turbo factor 225 had the best performance on EN (-21), while turbo factor 200 was next to the most reproducible turbo factor on EN (11). The results showed that turbo factor 200 had the least data acquisition time, in addition to superior performance on SNR, GM/WM CNR, GM/CSF CNR, and good reproducibility characteristics on EN. Both image quality metrics and volumetric measurements did not vary significantly (p > 0.05) with the range of turbo factors used in the study by one-way ANOVA analysis.

Conclusion

Since no significant differences were observed in the performance of the turbo factors in terms of image quality and volume of brain structure, turbo factor 200 with a 74% acquisition time reduction was found to be optimal for brain MR imaging at 1.5 T.

Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning

PURPOSE

To develop a deep learning method to synthesize conventional contrast-weighted images in the brain from MR multitasking spatial factors.

METHODS

Eighteen subjects were imaged using a whole-brain quantitative T1 -T2 -T1ρ MR multitasking sequence. Conventional contrast-weighted images consisting of T1 MPRAGE, T1 gradient echo, and T2 fluid-attenuated inversion recovery were acquired as target images. A 2D U-Net-based neural network was trained to synthesize conventional weighted images from MR multitasking spatial factors. Quantitative assessment and image quality rating by two radiologists were performed to evaluate the quality of deep-learning-based synthesis, in comparison with Bloch-equation-based synthesis from MR multitasking quantitative maps.

RESULTS

The deep-learning synthetic images showed comparable contrasts of brain tissues with the reference images from true acquisitions and were substantially better than the Bloch-equation-based synthesis results. Averaging on the three contrasts, the deep learning synthesis achieved normalized root mean square error = 0.184 ± 0.075, peak SNR = 28.14 ± 2.51, and structural-similarity index = 0.918 ± 0.034, which were significantly better than Bloch-equation-based synthesis (p < 0.05). Radiologists' rating results show that compared with true acquisitions, deep learning synthesis had no notable quality degradation and was better than Bloch-equation-based synthesis.

CONCLUSION

A deep learning technique was developed to synthesize conventional weighted images from MR multitasking spatial factors in the brain, enabling the simultaneous acquisition of multiparametric quantitative maps and clinical contrast-weighted images in a single scan.

Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network

Alzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach-"fusion of end-to-end and transfer learning"-to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.

Avoiding data loss: Synthetic MRIs generated from diffusion imaging can replace corrupted structural acquisitions for freesurfer-seeded tractography

Magnetic Resonance Imaging (MRI) motion artefacts frequently complicate structural and diffusion MRI analyses. While diffusion imaging is easily ‘scrubbed’ of motion affected volumes, the same is not true for T1w or T2w ‘structural’ images. Structural images are critical to most diffusion-imaging pipelines thus their corruption can lead to disproportionate data loss. To enable diffusion-image processing when structural images are missing or have been corrupted, we propose a means by which synthetic structural images can be generated from diffusion MRI. This technique combines multi-tissue constrained spherical deconvolution, which is central to many existing diffusion analyses, with the Bloch equations that allow simulation of MRI intensities for given scanner parameters and magnetic resonance (MR) tissue properties. We applied this technique to 32 scans, including those acquired on different scanners, with different protocols and with pathology present. The resulting synthetic T1w and T2w images were visually convincing and exhibited similar tissue contrast to acquired structural images. These were also of sufficient quality to drive a Freesurfer-based tractographic analysis. In this analysis, probabilistic tractography connecting the thalamus to the primary sensorimotor cortex was delineated with Freesurfer, using either real or synthetic structural images. Tractography for real and synthetic conditions was largely identical in terms of both voxels encountered (Dice 0.88–0.95) and mean fractional anisotropy (intrasubject absolute difference 0.00–0.02). We provide executables for the proposed technique in the hope that these may aid the community in analysing datasets where structural image corruption is common, such as studies of children or cognitively impaired persons.

Sensory and motor electrophysiological mapping of the cerebellum in humans

Cerebellar damage during posterior fossa surgery in children can lead to ataxia and risk of cerebellar mutism syndrome. Compartmentalisation of sensorimotor and cognitive functions within the cerebellum have been demonstrated in animal electrophysiology and human imaging studies. Electrophysiological monitoring was carried out under general anaesthesia to assess the limb sensorimotor representation within the human cerebellum for assessment of neurophysiological integrity to reduce the incidence of surgical morbidities. Thirteen adult and paediatric patients undergoing posterior fossa surgery were recruited. Sensory evoked field potentials were recorded in response to mapping (n = 8) to electrical stimulation of limb nerves or muscles. For motor mapping (n = 5), electrical stimulation was applied to the surface of the cerebellum and evoked EMG responses were sought in facial and limb muscles. Sensory evoked potentials were found in two patients (25%). Responses were located on the surface of the right inferior posterior cerebellum to stimulation of the right leg in one patient, and on the left inferior posterior lobe in another patient to stimulation of left forearm. No evoked EMG responses were found for the motor mapping. The present study identifies challenges with using neurophysiological methods to map functional organization within the human cerebellum and considers ways to improve success.

Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review

Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.

Acoustic simulation for transcranial focused ultrasound using GAN-based synthetic CT

Transcranial focused ultrasound (tFUS) is a promising non-invasive technique for treating neurological and psychiatric disorders. One of the challenges for tFUS is the disruption of wave propagation through the skull. Consequently, despite the risks associated with exposure to ionizing radiation, computed tomography (CT) is required to estimate the acoustic transmission through the skull. This study aims to generate synthetic CT (sCT) from T1-weighted magnetic resonance imaging (MRI) and investigate its applicability to tFUS acoustic simulation. We trained a 3D conditional generative adversarial network (3D-cGAN) with 15 subjects. We then assessed image quality with 15 test subjects: mean absolute error (MAE) = 85.72± 9.50 HU (head) and 280.25±24.02 HU (skull), dice coefficient similarity (DSC) = 0.88±0.02 (skull). In terms of skull density ratio (SDR) and skull thickness (ST), no significant difference was found between sCT and real CT (rCT). When the acoustic simulation results of rCT and sCT were compared, the intracranial peak acoustic pressure ratio was found to be less than 4%, and the distance between focal points less than 1 mm.

Gray Matter Abnormalities of Orbitofrontal Cortex and Striatum in Drug-Naïve Adult Patients With Obsessive-Compulsive Disorder

Objective: This study examined whether obsessive-compulsive disorder (OCD) patients have gray matter abnormalities in regions related to executive function, and whether such abnormalities are associated with impaired executive function. Methods: Multiple scales were administered to 27 first-episode drug-naïve OCD patients and 29 healthy controls. Comprehensive brain morphometric indicators of orbitofrontal cortex (OFC) and three striatum areas (caudate, putamen, and pallidum) were determined. Hemisphere lateralization index was calculated for each region of interest. Correlations between lateralization index and psychological variables were examined in OCD group. Results: The OCD group had greater local gyrification index for the right OFC and greater gray matter volumes of the bilateral putamen and left pallidum than healthy controls. They also had weaker left hemisphere superiority for local gyrification index of the OFC and gray matter volume of the putamen, but stronger left hemisphere superiority for gray matter volume of the pallidum. Patients' lateralization index for local gyrification index of the OFC correlated negatively with Yale-Brown Obsessive Compulsive Scale and Dysexecutive Questionnaire scores, respectively. Conclusion: Structural abnormalities of the bilateral putamen, left pallidum, and right OFC may underlie OCD pathology. Abnormal lateralization in OCD may contribute to the onset of obsessive-compulsive symptoms and impaired executive function.

Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels' intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.

Do High Mental Demands at Work Protect Cognitive Health in Old Age via Hippocampal Volume? Results From a Community Sample

As higher mental demands at work are associated with lower dementia risk and a key symptom of dementia is hippocampal atrophy, the study aimed at investigating the association between mental demands at work and hippocampal volume. We analyzed data from the population-based LIFE-Adult-Study in Leipzig, Germany (n = 1,409, age 40–80). Hippocampal volumes were measured via three-dimensional Magnetic resonance imaging (MRI; 3D MP-RAGE) and mental demands at work were classified via the O*NET database. Linear regression analyses adjusted for gender, age, education, APOE e4-allele, hypertension, and diabetes revealed associations between higher demands in “language and knowledge,” “information processing,” and “creativity” at work on larger white and gray matter volume and better cognitive functioning with “creativity” having stronger effects for people not yet retired. Among retired individuals, higher demands in “pattern detection” were associated with larger white matter volume as well as larger hippocampal subfields CA2/CA3, suggesting a retention effect later in life. There were no other relevant associations with hippocampal volume. Our findings do not support the idea that mental demands at work protect cognitive health via hippocampal volume or brain volume. Further research may clarify through what mechanism mentally demanding activities influence specifically dementia pathology in the brain.

Brain MRI findings in relation to clinical characteristics and outcome of tuberculous meningitis

Neuroradiological abnormalities in tuberculous meningitis (TBM) are common, but the exact relationship with clinical and inflammatory markers has not been well established. We performed magnetic resonance imaging (MRI) at baseline and after two months treatment to characterise neuroradiological patterns in a prospective cohort of adult TBM patients in Indonesia. We included 48 TBM patients (median age 30, 52% female, 8% HIV-infected), most of whom had grade II (90%), bacteriologically confirmed (71%) disease, without antituberculotic resistance. Most patients had more than one brain lesion (83%); baseline MRIs showed meningeal enhancement (89%), tuberculomas (77%), brain infarction (60%) and hydrocephalus (56%). We also performed an exploratory analysis associating MRI findings to clinical parameters, response to treatment, paradoxical reactions and survival. The presence of multiple brain lesion was associated with a lower Glasgow Coma Scale and more pronounced motor, lung, and CSF abnormalities (p-value <0.05). After two months, 33/37 patients (89%) showed worsening of MRI findings, mostly consisting of new or enlarged tuberculomas. Baseline and follow-up MRI findings and paradoxical responses showed no association with six-month mortality. Severe TBM is characterized by extensive MRI abnormalities at baseline, and frequent radiological worsening during treatment.

Three-dimensional Yarnball k-space acquisition for accelerated MRI

PURPOSE

To introduce an efficient sampling technique named Yarnball, which may serve as a direct alternative to 3D Cones.

METHODS

Yarnball evolves through 3D k-space with increasing loop size, and the differential equations defining this flexible trajectory are presented in detail. The sampling efficiencies of Yarnball and 3D Cones were compared through point spread function analysis and simulated imaging (which highlights undersampling in the absence of other scanning effects). The feasibility of Yarnball implementation was demonstrated for fully sampled T₁ -weighted images of the human head at 3 T.

RESULTS

The mostly large 3D loops of the Yarnball trajectory facilitate rapid sampling under peripheral nerve stimulation constraint, an advantage that increases with readout duration (T_RO ). Point spread function analysis yielded 89% (T_RO = 2 ms) and 77% (T_RO = 10 ms) of Yarnball voxels with magnitude less than 0.01% of the point spread function peak. For 3D Cones, these values were only 52% and 29%. The 3D-Cones technique required 1.4 times (T_RO = 2 ms) and 1.8 times (T_RO = 10 ms) more trajectories than Yarnball to produce simulated images of a sphere free from undersampling artifact. For a prolate spheroidal (head-like) object, 1.75 times and 2.6 times more trajectories were required for 3D Cones. Yarnball produced 0.72 mm (1/2k_max ) isotropic T₁ -weighted human brain images free from undersampling artifact in only 98 seconds at 3 T.

CONCLUSION

Yarnball demonstrated greater k-space sampling efficiency than directly comparable 3D Cones, and may have value wherever 3D Cones has been considered. Yarnball may also have value in the context of rapid T₁ -weighted brain imaging.

A neurobehavioral study on the efficacy of price interventions in promoting healthy food choices among low socioeconomic families

Given the healthcare costs associated with obesity (especially in childhood), governments have tried several fiscal and policy interventions such as lowering tax and giving rebates to encourage parents to choose healthier food for their family. The efficacy of such fiscal policies is currently being debated. Here we address this issue by investigating how behavioral and brain-based responses in parents with low socioeconomic status change when rebates and lower taxes are offered on healthy food items. We performed behavioral and brain-based experiments, with the latter employing electroencephalography (EEG) acquired from parents while they shop in a simulated shopping market as well as follow up functional magnetic resonance imaging (fMRI) in the more restricted scanner environment. Behavioral data show that lower tax and rebate on healthy foods increase their purchase significantly compared to baseline. Rebate has a higher effect than lower tax treatment. From the EEG and fMRI experiments, we first show that healthy/unhealthy foods elicit least/maximal reward response in the brain, respectively. Further, by offering lower tax or rebate on healthy food items, the reward signal for such items in the brain is significantly enhanced. Second, we demonstrate that rebate is more effective than lower tax in encouraging consumers to purchase healthy food items, driven in part, by higher reward-related response in the brain for rebate. Third, fiscal interventions decreased the amount of frontal cognitive control required to buy healthy foods despite their lower calorific value as compared to unhealthy foods. Finally, we propose that it is possible to titrate the amount of tax reductions and rebates on healthy food items so that they consistently become more preferable than unhealthy foods.

The effect of the MR pulse sequence on the regional corpus callosum morphometry

Background and purposes

Brain morphometry is an important assessment technique to assess certain morphological brain features of various brain regions, which can be quantified in vivo by using high-resolution structural magnetic resonance (MR) imaging. This study aims to investigate the effect of different types of pulse sequence on regional corpus callosum (CC) morphometry analysis.

Materials and methods

Twenty-one healthy volunteers were scanned twice on the same 3T MRI scanner (Magnetom Trio, Siemens, Erlangen, Germany) equipped with an 8-channel head coil. Two different MR pulse sequences were applied to acquire high-resolution 3D T1-weighted images: magnetization-prepared rapid gradient-echo (MP-RAGE) and modified driven equilibrium Fourier transform (MDEFT) pulse sequence. Image quality measurements such as SNR, contrast-to-noise ratio, and relative contrast were calculated for each pulse sequence images independently. The values of corpus callosum volume were calculated based on the vertex of reconstructed surfaces. The paired dependent t test was applied to compare the means of two matched groups.

Results

Three sub-regional CC, namely anterior, mid-anterior, and posterior, resulted in an estimated volume difference between MDEFT and MP-RAGE pulse sequences. Central and mid-posterior sub-regional CC volume resulted in not significant difference between the two named pulse sequences.

Conclusion

The findings of this study demonstrate that combining data from different pulse sequences in a multisite study could make some variations in the results.

PSACNN: Pulse sequence adaptive fast whole brain segmentation

With the advent of convolutional neural networks (CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging (MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as T1-weighted and T2-weighted contrasts with only T1-weighted training data. The segmentations generated are highly accurate with state-of-the-art results (overall Dice overlap=0.94), with a fast run time (≈ 45 s), and consistent across a wide range of acquisition protocols.

Network-based Responses to the Psychomotor Vigilance Task during Lapses in Adolescents after Short and Extended Sleep

Neuroimaging studies of the Psychomotor Vigilance Task (PVT) have revealed brain regions involved in attention lapses in sleep-deprived and well-rested adults. Those studies have focused on individual brain regions, rather than integrated brain networks, and have overlooked adolescence, a period of ongoing brain development and endemic short sleep. This study used functional MRI (fMRI) and a contemporary analytic approach to assess time-resolved peri-stimulus response of key brain networks when adolescents complete the PVT, and test for differences across attentive versus inattentive periods and after short sleep versus well-rested states. Healthy 14–17-year-olds underwent a within-subjects randomized protocol including 5-night spans of extended versus short sleep. PVT was performed during fMRI the morning after each sleep condition. Event-related independent component analysis (eICA) identified coactivating functional networks and corresponding time courses. Analysis of salient time course characteristics tested the effects of sleep condition, lapses, and their interaction. Seven eICA networks were identified supporting attention, executive control, motor, visual, and default-mode functions. Attention lapses, after either sleep manipulation, were accompanied by broadly increased response magnitudes post-stimulus and delayed peak responses in some networks. Well-circumscribed networks respond during the PVT in adolescents, with timing and intensity impacted by attentional lapses regardless of experimentally shortened or extended sleep.

Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI

BACKGROUND

Sequence optimization in neonates might improve detection sensitivity of abnormalities for a variety of conditions. However this has been historically challenging because tissue properties such as the longitudinal relaxation time and proton density differ significantly between neonates and adults.

OBJECTIVE

To optimize the magnetization-prepared rapid gradient echo (MP-RAGE) sequence to enhance both signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) efficiencies.

MATERIALS AND METHODS

We optimized neonatal MP-RAGE sequence through (1) reducing receive bandwidth to decrease noise, (2) shortening acquisition train length (acquisition number per repetition time or total number of read-out radiofrequency rephrasing pulses) using slice partial Fourier acquisition and (3) simulating the solution of Bloch's equation under optimal receive bandwidth and acquisition train length. Using the optimized sequence parameters, we scanned 12 healthy full-term infants within 2 weeks of birth and four preterm infants at 40 weeks' corrected age.

RESULTS

Compared with a previously published neonatal protocol, we were able to reduce the total scan time by reduce the total scan time by 60% and increase the average SNR efficiency by 160% (P<0.001) and the average CNR efficiency by 26% (P=0.029).

CONCLUSION

Our in vivo neonatal brain imaging experiments confirmed that both SNR and CNR efficiencies significantly increased with our proposed protocol. Our proposed optimization methodology could be readily extended to other populations (e.g., older children, adults), as well as different organ systems, field strengths and MR sequences.

Development and validation of a new MRI simulation technique that can reliably estimate optimal in vivo scanning parameters in a glioblastoma murine model

BACKGROUND

Magnetic Resonance Imaging (MRI) relies on optimal scanning parameters to achieve maximal signal-to-noise ratio (SNR) and high contrast-to-noise ratio (CNR) between tissues resulting in high quality images. The optimization of such parameters is often laborious, time consuming, and user-dependent, making harmonization of imaging parameters a difficult task. In this report, we aim to develop and validate a computer simulation technique that can reliably provide "optimal in vivo scanning parameters" ready to be used for in vivo evaluation of disease models.

METHODS

A glioblastoma murine model was investigated using several MRI imaging methods. Such MRI methods underwent a simulated and an in vivo scanning parameter optimization in pre- and post-contrast conditions that involved the investigation of tumor, brain parenchyma and cerebrospinal fluid (CSF) CNR values in addition to the time relaxation values of the related tissues. The CNR tissues information were analyzed and the derived scanning parameters compared in order to validate the simulated methodology as a reliable technique for "optimal in vivo scanning parameters" estimation.

RESULTS

The CNRs and the related scanning parameters were better correlated when spin-echo-based sequences were used rather than the gradient-echo-based sequences due to augmented inhomogeneity artifacts affecting the latter methods. "Optimal in vivo scanning parameters" were generated successfully by the simulations after initial scanning parameter adjustments that conformed to some of the parameters derived from the in vivo experiment.

CONCLUSION

Scanning parameter optimization using the computer simulation was shown to be a valid surrogate to the in vivo approach in a glioblastoma murine model yielding in a better delineation and differentiation of the tumor from the contralateral hemisphere. In addition to drastically reducing the time invested in choosing optimal scanning parameters when compared to an in vivo approach, this simulation program could also be used to harmonize MRI acquisition parameters across scanners from different vendors.

Effects of an artificial placenta on brain development and injury in premature lambs

PURPOSE

We evaluated whether brain development continues and brain injury is prevented during Artificial Placenta (AP) support utilizing extracorporeal life support (ECLS).

METHODS

Lambs at EGA 118days (term=145; n=4) were placed on AP support (venovenous ECLS with jugular drainage and umbilical vein reinfusion) for 7days and sacrificed. Early (EGA 118; n=4) and late (EGA 127; n=4) mechanical ventilation (MV) lambs underwent conventional MV for up to 48h and were sacrificed, and early (n=5) and late (n=5) tissue control (TC) lambs were sacrificed at delivery. Brains were harvested, formalin-fixed, rehydrated, and studied by magnetic resonance imaging (MRI). The gyrification index (GI), a measure of cerebral folding complexity, was calculated for each brain. Diffusion-weighted imaging was used to determine fractional anisotropy (FA) and apparent diffusion coefficient (ADC) in multiple structures to assess white matter (WM) integrity.

RESULTS

No intracranial hemorrhage was observed. GI was similar between AP and TC groups. ADC and FA did not differ between AP and late TC groups in any structure. Compared to late MV brains, AP brains demonstrated significantly higher ADC (0.45±0.08 vs. 0.27±0.11, p=0.02) and FA (0.61±0.04 vs. 0.44±0.05; p=0.006) in the cerebral peduncles.

CONCLUSIONS

After 7days of AP support, WM integrity is preserved relative to mechanical ventilation.

TYPE OF STUDY

Research study.

A novel non-destructive manner for quantitative determination of plumpness of live Eriocheir sinensis using low-field nuclear magnetic resonance

The present study investigated the quantitative and non-destructive determination of Eriocheir sinensis' plumpness during four mature stages using low field-nuclear magnetic resonance (LF-¹H NMR). Normalized lipid volume of live E. sinensis was calculated from Sept to Dec using 3D LF-¹H nuclear magnetic imaging (MRI) and the validity of proposed technique was compared and verified with traditional Soxhlet extraction and live dissection method, respectively. The results showed the plumpness of female E. sinensis was higher than that of male ones from Sept to Dec and the highest plumpness of male and female E. sinensis reached 99,436.44 and 109,207.15mm³ in Oct. The normalized lipid volume of live male and female E. sinensis had a positive correlation with lipid content. This proposed method with short assay time, favorable selectivity, and accuracy demonstrated its application potential in grading regulation and quality evaluation of live E. sinensis.

Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke

Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP), robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels-which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR) rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI) data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training) in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC), contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1), ipsilesional ventral premotor area (iPMv), and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT), in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD signal in multiple regions of interest was reduced and re-lateralized to the ipsilesional side. Second, these changes correlated with improvement in JTHFT scores. Our findings suggest that RAVR training may lead to different neurophysiological changes when compared with traditional therapy. This effect may be attributed to the influence that augmented visual and haptic feedback during RAVR training exerts over higher-order somatosensory and visuomotor areas.

The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions

Background

Although typically measured during the resting state, a growing literature is illustrating the ability to map intrinsic connectivity with functional MRI during task and naturalistic viewing conditions. These paradigms are drawing excitement due to their greater tolerability in clinical and developing populations and because they enable a wider range of analyses (e.g., inter-subject correlations). To be clinically useful, the test-retest reliability of connectivity measured during these paradigms needs to be established. This resource provides data for evaluating test-retest reliability for full-brain connectivity patterns detected during each of four scan conditions that differ with respect to level of engagement (rest, abstract animations, movie clips, flanker task). Data are provided for 13 participants, each scanned in 12 sessions with 10 minutes for each scan of the four conditions. Diffusion kurtosis imaging data was also obtained at each session.

Findings

Technical validation and demonstrative reliability analyses were carried out at the connection-level using the Intraclass Correlation Coefficient and at network-level representations of the data using the Image Intraclass Correlation Coefficient. Variation in intrinsic functional connectivity across sessions was generally found to be greater than that attributable to scan condition. Between-condition reliability was generally high, particularly for the frontoparietal and default networks. Between-session reliabilities obtained separately for the different scan conditions were comparable, though notably lower than between-condition reliabilities.

Conclusions

This resource provides a test-bed for quantifying the reliability of connectivity indices across subjects, conditions and time. The resource can be used to compare and optimize different frameworks for measuring connectivity and data collection parameters such as scan length. Additionally, investigators can explore the unique perspectives of the brain's functional architecture offered by each of the scan conditions.

T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 μm

We present an ultrahigh resolution in vivo human brain magnetic resonance imaging (MRI) dataset. It consists of T₁-weighted whole brain anatomical data acquired at 7 Tesla with a nominal isotropic resolution of 250 μm of a single young healthy Caucasian subject and was recorded using prospective motion correction. The raw data amounts to approximately 1.2 TB and was acquired in eight hours total scan time. The resolution of this dataset is far beyond any previously published in vivo structural whole brain dataset. Its potential use is to build an in vivo MR brain atlas. Methods for image reconstruction and image restoration can be improved as the raw data is made available. Pre-processing and segmentation procedures can possibly be enhanced for high magnetic field strength and ultrahigh resolution data. Furthermore, potential resolution induced changes in quantitative data analysis can be assessed, e.g., cortical thickness or volumetric measures, as high quality images with an isotropic resolution of 1 and 0.5 mm of the same subject are included in the repository as well.