Brain Imaging Applications Research Articles

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

PurposeTo systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI.MethodsArticles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool.ResultsIn all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02.ConclusionsMachine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.

Polymethine-Based Semiconducting Polymer Dots with Narrow-Band Emission and Absorption/Emission Maxima at NIR-II for Bioimaging.

Deep-penetration fluorescence imaging in the second near-infrared (NIR-II) window heralds a new era of clinical surgery, in which high-resolution vascular/lymphatic anatomy and detailed cancerous tissues can be visualized in real time. Described here is a series of polymethine-based semiconducting polymers with intrinsic emission maxima in the NIR-IIa (1300-1400 nm) window and absorption maxima ranging from 1082 to 1290 nm. These polymers were prepared as semiconducting polymer dots (Pdots) in aqueous solutions with fluorescence quantum yields of 0.05-0.18 %, and they demonstrate promising applications in noninvasive through-skull brain imaging in live mice with remarkable spatial resolution as well as signal-to-background contrast. This study offers a platform for future design of NIR-IIa or even NIR-IIb emitting Pdots.

(+)-[18F]Flubatine as a novel \u03b14\u03b22 nicotinic acetylcholine receptor PET ligand\u2014results of the first-in-human brain imaging application in patients with \u03b2-amyloid PET-confirmed Alzheimer\u2019s disease and healthy controls

PurposesWe present the first in-human brain PET imaging data of the new α4β2 nicotinic acetylcholine receptor (nAChR)–targeting radioligand (+)-[18F]Flubatine. Aims were to develop a kinetic modeling-based approach to quantify (+)-[18F]Flubatine and compare the data of healthy controls (HCs) and patients with Alzheimer’s disease (AD); to investigate the partial volume effect (PVE) on regional (+)-[18F]Flubatine binding; and whether (+)-[18F]Flubatine binding and cognitive test data respective β-amyloid radiotracer accumulation were correlated.MethodsWe examined 11 HCs and 9 mild AD patients. All subjects underwent neuropsychological testing and [11C]PiB PET/MRI examination. (+)-[18F]Flubatine PET data were evaluated using full kinetic modeling and regional as well as voxel-based analyses.ResultsWith 270-min p.i., the unchanged parent compound amounted to 97 ± 2%. Adequate fits of the time-activity curves were obtained with the 1 tissue compartment model (1TCM). (+)-[18F]Flubatine distribution volume (binding) was significantly reduced in bilateral mesial temporal cortex in AD patients compared with HCs (right 10.6 ± 1.1 vs 11.6 ± 1.4, p = 0.049; left 11.0 ± 1.1 vs 12.2 ± 1.8, p = 0.046; one-sided t tests each). PVE correction increased not only (+)-[18F]Flubatine binding of approximately 15% but also standard deviation of 0.4–70%. Cognitive test data and (+)-[18F]Flubatine binding were significantly correlated in the left anterior cingulate, right posterior cingulate, and right parietal cortex (r > 0.5, p < 0.05 each). In AD patients, (+)-[18F]Flubatine binding and [11C]PiB standardized uptake value ratios were negatively correlated in several regions; whereas in HCs, a positive correlation between cortical (+)-[18F]Flubatine binding and [11C]PiB accumulation in the white matter was found. No adverse event related to (+)-[18F]Flubatine occurred.Conclusion(+)-[18F]Flubatine is a safe and stable PET ligand. Full kinetic modeling can be realized by 1TCM without metabolite correction. (+)-[18F]Flubatine binding affinity was high enough to detect group differences. Of interest, correlation between white matter β-amyloid PET uptake and (+)-[18F]Flubatine binding indicated an association between white matter integrity and availability of α4β2 nAChRs. Overall, (+)-[18F]Flubatine showed favorable characteristics and has therefore the potential to serve as α4β2 nAChR–targeting PET ligand in further clinical trials.

Photogrammetry-based stereoscopic optode registration method for functional near-infrared spectroscopy

.Significance: Functional near-infrared spectroscopy (fNIRS) is an emerging brain imaging technique due to its small size, low cost, minimum scanning sonic noise, and portability. Unfortunately, because this technique does not provide neuroanatomical information to accompany the functional data, its data interpretation remains a persistent challenge in fNIRS brain imaging applications. The two most popular approaches for fNIRS anatomical registration are magnetic resonance imaging (MRI) and three-dimensional (3-D) digitization. MRI scanning yields high-precision registration but reduces the cost-effectiveness and accessibility of fNIRS imaging. Alternatively, the low cost and portable 3-D digitizers are affected by magnetic properties of ambient metal objects, including participant clothing, testing equipment, medical implants, and so forth.Aim: To overcome these obstacles and provide accessible and reliable neuroanatomical registration for fNIRS imaging, we developed and explored a photogrammetry optode registration (POR) method.Approach: The POR method uses a consumer-grade camera to reconstruct a 3-D image of the fNIRS optode-set, including light emitters and detectors, on a participant’s head. This reconstruction process uses a linear-time incremental structure from motion (LTI-SfM) algorithm, based on 100 to 150 digital photos. The POR method then aligns the reconstructed image with an anatomical template of the brain.Results: To validate this method, we tested 22 adult and 19 child participants using the POR method and MRI imaging. The results comparisons suggest on average 55% and 46% overlap across all data channel measurements registered by the two methods in adult and children, respectively. Importantly, this overlap reached 65% and 60% in only the frontal channels.Conclusions: These results suggested that the mismatch in registration was partially due to higher variation in backward optode placement rather than the registration efficacy. Therefore, the photo-based registration method can offer an accessible and reliable approach to neuroanatomical registration of fNIRS as well as other surface-based neuroimaging and neuromodulation methods.

Eight‐channel parallel transmit‐receive system for 7 T MRI with optically controlled and monitored on‐coil current‐mode RF amplifiers

To demonstrate a practical implementation of an eight-channel parallel-transmit system for brain imaging at 7 T based on on-coil amplifier technology. An eight-channel parallel transmit-receive system was built with optimized on-coil switch-mode current RF power amplifiers. The amplifiers were optically controlled from an eight-channel interface that was connected to a 7 T MRI scanner. The interface also optically received a down-converted version of the coil current sensed in each amplifier for monitoring and feedback adjustments. Each on-coil amplifier delivered more than 100 W peak power and provided enough amplifier decoupling (<-15 dB) for the implemented eight-channel array configuration. Phantom and human images were acquired to demonstrate practical operation of this new technology in a 7 T MRI scanner. Further development and improvement of previously demonstrated on-coil technology led to successful implementation of an eight-channel parallel-transmit system able to deliver strong B1 fields for typical brain imaging applications. This is an important step forward toward implementation of on-coil RF amplification for high-field MRI.

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.

A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass problems. This method starts with a bagging procedure which calculates the sign consistency of the multivariate analysis (MVA) projection matrix feature-wise to determine the relevance of each feature. This relevance measure provides a parsimonious matrix, which is combined with a hypothesis test to automatically determine the number of selected features. Then, a novel MVA regularized with the sign and magnitude consistency of the features is used to generate a reduced set of summary components providing a compact data description. We evaluated the proposed method with two multiclass brain imaging problems: 1) the classification of the elderly subjects in four classes (cognitively normal, stable mild cognitive impairment (MCI), MCI converting to AD in 3 years, and Alzheimer's disease) based on structural brain imaging data from the ADNI cohort; 2) the classification of children in 3 classes (typically developing, and 2 types of Attention Deficit/Hyperactivity Disorder (ADHD)) based on functional connectivity. Experimental results confirmed that each brain image (defined by 29.852 features in the ADNI database and 61.425 in the ADHD) could be represented with only 30 - 45% of the original features. Furthermore, this information could be redefined into two or three summary components, providing not only a gain of interpretability but also classification rate improvements when compared to state-of-art reference methods.

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

There are numerous studies on brain imaging applications. The statistics in Malaysia showed that glioma is one of the most common type disease in brain tumor. Glioma brain tumor is an abnormal growth of glial cells inside the brain tissues which known as cerebral tissues. Radiologist commonly used Magnetic Resonance Imaging (MRI) image sequences to diagnose the brain tumor. However, manual examination of the brain tumor diagnosis by radiologist is difficult and time-consuming task as tumors are occurred in variability of shape and appearance. They will also inject a gadolinium contrast agent to enhance the image modality which will give the side effects to the patients. Therefore, this paper presents an automated segmentation and detection of MRI brain images using Sobel edge detection and mathematical morphology operations. The total of 30 glioma T1-Weighted MRI brain images are obtained from Brain Tumor Image Segmentation Benchmark (BRATS). The results of segmentation and detection are quantitatively evaluated by using Area Overlap which produced the accuracy rate of 80.2% and shows that the presented methods are promising.

Real-Time Dual-Wavelength Time-Resolved Diffuse Optical Tomography System for Functional Brain Imaging Based on Probe-Hosted Silicon Photomultipliers.

Near-infrared diffuse optical tomography is a non-invasive photonics-based imaging technology suited to functional brain imaging applications. Recent developments have proved that it is possible to build a compact time-domain diffuse optical tomography system based on silicon photomultipliers (SiPM) detectors. The system presented in this paper was equipped with the same eight SiPM probe-hosted detectors, but was upgraded with six injection fibers to shine the sample at several points. Moreover, an automatic switch was included enabling a complete measurement to be performed in less than one second. Further, the system was provided with a dual-wavelength (670 and 820 ) light source to quantify the oxy- and deoxy-hemoglobin concentration evolution in the tissue. This novel system was challenged against a solid phantom experiment, and two in-vivo tests, namely arm occlusion and motor cortex brain activation. The results show that the tomographic system makes it possible to follow the evolution of brain activation over time with a 1-resolution.

Imaging System Based on Silicon Photomultipliers and Light Emitting Diodes for Functional Near-Infrared Spectroscopy

We built a fiber-less prototype of an optical system with 156 channels each one consisting of an optode made of a silicon photomultiplier (SiPM) and a pair of light emitting diodes (LEDs) operating at 700 nm and 830 nm. The system uses functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) imaging of the cortical activity of the human brain at frequencies above 1 Hz. In this paper, we discuss testing and system optimization performed through measurements on a multi-layered optical phantom with mechanically movable parts that simulate near-infrared light scattering inhomogeneities. The baseline optical characteristics of the phantom are carefully characterized and compared to those of human tissues. Here we discuss several technical aspects of the system development, such as LED light output drift and its possible compensation, SiPM linearity, corrections of channel signal differences, and signal-to-noise ratio (SNR). We implement an imaging algorithm that investigates large phantom regions. Thanks to the use of SiPMs, very large source-to-detector distances are acquired with a high SNR and 2 Hz time resolution. The overall results demonstrate the high potentialities of a system based on SiPMs for fNIRS/DOT human brain imaging applications.

Design, optimization and performance evaluation of BM-PET: A simulation study

Abstract Introduction: The aim of this work is to report on modeling and evaluation of potential performance characteristics of Brain Mapping PET (BM-PET) which is under development at our center. BM-PET is based on expansion of our recently developed small animal PET system (Xtrim-PET) and optimize the geometry of its detector system for brain imaging applications. Methods: We modeled an accurate representation of Xtrim-PET in GATE and after validation of the model, we extended our model to design and optimize the BM-PET scanner followed by defining a cost function and running series evaluation tests in terms of different geometrical, physical and detection parameters. Resolution recovery and attenuation correction techniques were developed and applied to the simulated data. Finally, the performance of the BM-PET system is evaluated and compared with two simulated models of commercially available brain scanners. Results: The optimized rotating cylindrical BM-PET based on LYSO-SiPM detectors with dimension of 2 × 2 × 20 mm 3 resulted in system sensitivity of 16 cps/kBq and 2.1 mm FWHM spatial resolution when tested using the NEMA standard. Detection efficiency of BM-PET is 1.2x and 2.1x higher than our simulated NeuroPET and HRRT scanners. The calculated spatial resolution of the BM-PET in rotation mode is approximately 0.8 and 0.2 mm better compared with that of the mentioned scanners. Conclusion: Optimization of the BM-PET geometry and detector configuration, implementation of scanner rotation/wobbling and utilization of image correction and enhancement techniques, improved the performance of our modeled brain PET and direct us to manufacture of a prototype scanner.

Accurate hybrid template\u2013based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications

BackgroundAttenuation 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.MethodsMR 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.ResultsThe 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.ConclusionsA 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.

Synthetic Polymer Nanoparticles Functionalized with Different Ligands for Receptor-mediated Transcytosis across Blood-Brain Barrier.

Polymeric nanoparticles have been investigated as biocompatible and promising nano-carriers to deliver drugs across the blood-brain barrier (BBB). However, most of the polymeric nanoparticles cannot be observed without attaching them with fluorescent dyes. Generally complex synthesis process is required to attach fluorescent dye tracing molecules with drug carrier nanoparticles. In this paper, we synthesized a novel fluorescent polymer based on poly [Triphenylamine-4-vinyl-(P-methoxy-benzene)] (TEB). This polymer was prepared from TEB polymer through coprecipitation. Furthermore, three types of ligands, transferrin (TfR), lactoferrin (LfR) and lipoprotein (LRP), were covalently attached on the nanoparticle surface to improve the BBB transport efficiency. All of prepared TEB-based nanoparticles were biocompatible, exhibited excellent fluorescence properties and could be observed in vivo. The transcellular transportation of these TEB-based nanoparticles across the BBB was evaluated by observing the fluorescent intensity. The translocation study was performed in an in vitro BBB model that were constructed based on mouse cerebral endothelial cells (bEnd.3). The results showed that ligand-coated TEB nanoparticles can be transported across BBB with high efficiencies (up to 29.02%). This is the first time that the fluorescent TEB nanoparticles were applied as nano-carriers for transport across the BBB. Such fluorescent polymeric nanoparticles have the potential applications in brain imaging or drug delivery.

Big Data in Child and Adolescent Psychiatry: Is It Taking Us Where No Man Has Gone Before?

“Big Data” refers to a massive volume of data that usually require large computational resources to be processed. In recent years, it has shown opportunities for data-driven approaches instead of theory-driven approaches and discovery of patterns based on data collected in routine clinical care enriched with other sources. It holds promise of guidance of personalized care based on each and every one of our patients and results that are more ecologically valid than ever before. This Symposium will present findings of some recent big data projects in child and adolescent psychiatry and cover clinical decision support systems, text mining methods, emerging applications of brain imaging and bio banking, data-sharing models, and ethical issues.

Brain Data: Scanning, Scraping and Sculpting the Plastic Learning Brain Through Neurotechnology

Neurotechnology is an advancing field of research and development with significant implications for education. As ‘postdigital’ hybrids of biological and informational codes, novel neurotechnologies combine neuroscience insights into the human brain with advanced technical development in brain imaging, brain-computer interfaces, neurofeedback platforms, brain stimulation and other neuroenhancement applications. Merging neurobiological knowledge about human life with computational technologies, neurotechnology exemplifies how postdigital science will play a significant role in societies and education in decades to come. As neurotechnology developments are being extended to education, they present potential for businesses and governments to enact new techniques of ‘neurogovernance’ by ‘scanning’ the brain, ‘scraping’ it for data and then ‘sculpting’ the brain toward particular capacities. The aim of this article is to critically review neurotechnology developments and implications for education. It examines the purposes to which neurotechnology development is being put in education, interrogating the commercial and governmental objectives associated with it and the neuroscientific concepts and expertise that underpin it. Finally, the article raises significant ethical and governance issues related to neurotechnology development and postdigital science that require concerted attention from education researchers.

Multi-task fused sparse learning for mild cognitive impairment identification.

BACKGROUND: Brain functional connectivity network (BFCN) has been widely applied to identify biomarkers for the brain function understanding and brain diseases analysis.OBJECTIVE: Building a biologically meaningful brain network is a crucial work in these applications. For this task, sparse learning has been widely applied for the network construction. If multiple time-point data is added to the brain imaging application, the disease progression pattern in the longitudinal analysis can be better revealed.METHODS: A novel longitudinal analysis for MCI classification is devised based on resting-state functional magnetic resonating imaging (rs-fMRI). Specifically, this paper proposes a novel multi-task learning method to integrate fused penalty by regularization. In addition, a novel objective function is developed for fused sparse learning via smoothness constraint.RESULTS: The proposed method achieves the best classification performance with an accuracy of 95.74% for baseline and 93.64% for year 1 data.CONCLUSIONS: The experimental results show that our proposed method achieves quite promising classification performance.

Interpretation of 2018 guidelines for the early management of patients with acute ischemic stroke

In 2018, the American Heart Association/American Stroke Association (AHA/ASA) has developed the latest 2018 guidelines for the early management of patients with acute ischemic stroke (AIS), based on the latest evidences. The 2018 guidelines including recommendations on pre-hospital and in-hospital management treatment, has revised and add new recommendations from 2013 guideline. The major changes in 2018 guideline involve applications of brain imaging in early stage, intravenous thrombolysis and mechanical thrombectomy, et al. This review interprets the 2018 guidelines for clinicians to improve the clinical diagnosis, treatment and outcome of patients with AIS.

Generic high resolution PET detector block using 12 × 12 SiPM array

With the advancement of the Silicon Photomultiplier (SiPM) technology, these devices are becoming the mainstream for use in high resolution PET detectors especially where the compatibility with magnetic field is critical. Large-area SiPM arrays are now widely used in high resolution PET detector modules. The challenges are emerged in the readout and timing circuitry by the increase of pixel density in the area. The application-specific integrated circuits (ASICs) are developed to readout arrays with standard anode/cathode outputs. SiPM detection principle, which produces dark current noise in the inactive pixels, makes the multiplexing techniques more challenging rather than the PMT signals. In this work a new generic PET detector block using SiPM arrays with three outputs (anode, cathode, and fast) is presented which is suitable for applications in small animal, brain, and clinical PET imaging. The block detector accepts a 12 × 12 array with both standard and fast output signals with an overall detector dimension of 50 × 50 mm2. A new version of the scrambled crosswire readout (SCR) method was implemented to reduce 144 ‘fast outputs’ to 9 tile signals and 144 standard outputs to 16 energy channels. The tile signals are also used to generate time pickoff information for timing resolution. We implemented time-to-digital converter (TDC) in Xilinx’s SPARTAN6 field programmable gate array (FPGA) by using the 64-tap delay line. This low cost chip performs all required processes for position, energy, time, and interface architecture. The attached 24 × 24 LYSO:Ce array with 2 × 2 × 10 mm3 pixels is imaged and pixels are resolved clearly in the room temperature. The measured energy resolution of the detector block after calibration for all crystal pixels is 12.4% at 511 keV. Also, the coincidence resolving time for two identical modules was measured at 1.85 ns.

Current Knowledge and Clinical Application of Brain Imaging in Alzheimer's Disease

Current Knowledge and Clinical Application of Brain Imaging in Alzheimer's Disease

PET/MR Tomographs: A Review with Technical, Radiochemical and Clinical Perspectives.

In the last decade, an increasing number of positron emission tomography / magnetic resonance (PET/MR) tomographs were installed and many clinical studies were performed in the neurological field. Although PET/MR has many favorable properties to support the application in brain imaging, attenuation correction, and therefore accurate quantification, is a problem that still requires optimal solution. In this review we have summarized the three main methods that are currently used to correct attenuation in PET/MR, namely atlas- or template-based methods, segmentation-based methods, and reconstruction-based methods. There is currently active ongoing research to refine available methods and improvements are reasonably expected in the next years. Clinical studies using PET/MR focused mainly on neurodegenerative and neurooncological disorders. PET/MR hybrids tomographs provided promising scientific results and were logistically more convenient for patients. Additionally, in order to explore all potential clinical benefits of this hybrid technology, the design and development of multimodal contrast agents has constantly increased the attention of radiochemists. Many PET/MR dual probes have been already devised, particularly in the nanotechnology field, sometimes preceding the identification of a clear diagnostic application in medicine. In the near future, we predict more clinical studies as the availability of PET/MR will further increase and new tracers for neurodegenerative disorders will accept broader clinical acceptance.

Coordination Chemistry of Bifunctional Chemical Agents Designed for Applications in 64Cu PET Imaging for Alzheimer's Disease.

Positron emission tomography (PET) is emerging as one of the most important diagnostic tools for brain imaging, yet the most commonly used radioisotopes in PET imaging, 11C and 18F, have short half-lives, and their usage is thus somewhat limited. By comparison, the 64Cu radionuclide has a half-life of 12.7 h, which is ideal for administering and imaging purposes. In spite of appreciable research efforts, high-affinity copper chelators suitable for brain imaging applications are still lacking. Herein, we present the synthesis and characterization of a series of bifunctional compounds (BFCs) based on macrocyclic 1,4,7-triazacyclononane and 2,11-diaza[3.3](2,6)pyridinophane ligand frameworks that exhibit a high affinity for Cu2+ ions. In addition, these BFCs contain a 2-phenylbenzothiazole fragment that is known to interact tightly with amyloid β fibrillar aggregates. Determination of the protonation constants (pKa values) and stability constants (log β values) of these BFCs, as well as characterization of the isolated copper complexes using X-ray crystallography, electron paramagnetic resonance spectroscopy, and electrochemical studies, suggests that these BFCs exhibit desirable properties for the development of novel 64Cu PET imaging agents for Alzheimer’s disease.