A primer for ZooMS applications in archaeology.

Collagen peptide mass fingerprinting by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, also known as zooarchaeology by mass spectrometry (ZooMS), is a rapidly growing analytical technique in the fields of archaeology, ecology, and cultural heritage. Minimally destructive and cost effective, ZooMS enables rapid taxonomic identification of large bone assemblages, cultural heritage objects, and other organic materials of animal origin. As its importance grows as both a research and a conservation tool, it is critical to ensure that its expanding body of users understands its fundamental principles, strengths, and limitations. Here, we outline the basic functionality of ZooMS and provide guidance on interpreting collagen spectra from archaeological bones. We further examine the growing potential of applying ZooMS to nonmammalian assemblages, discuss available options for minimally and nondestructive analyses, and explore the potential for peptide mass fingerprinting to be expanded to noncollagenous proteins. We describe the current limitations of the method regarding accessibility, and we propose solutions for the future. Finally, we review the explosive growth of ZooMS over the past decade and highlight the remarkably diverse applications for which the technique is suited.

Optical Tweezing Terahertz Probing for a Single Metal Nanoparticle.

Recently, extensive research has been reported on the detection of metal nanoparticles using terahertz waves, due to their potential for efficient and nondestructive detection of chemical and biological samples without labeling. Resonant terahertz nanoantennas can be used to detect a small amount of molecules whose vibrational modes are in the terahertz frequency range with high sensitivity. However, the positioning of target molecules is critical to obtaining a reasonable signal because the field distribution is inhomogeneous over the antenna structure. Here, we combine an optical tweezing technique and terahertz spectroscopy based on nanoplasmonics, resulting in extensive controllable tweezing and sensitive detection at the same time. We observed optical tweezing of a gold nanoparticle and detected it with terahertz waves by using a single bowtie nanoantenna. Furthermore, the calculations confirm that molecular fingerprinting is possible by using our technique. This study will be a prestep of biomolecular detection using gold nanoparticles in terahertz spectroscopy.

Structural Fingerprinting of the Frontal Aslant Tract: Predicting Cognitive Control Capacity and Obsessive-Compulsive Symptoms.

White matter of the human brain is influenced by common genetic variations and shaped by neural activity-dependent experiences. Variations in microstructure of cerebral white matter across individuals and even across fiber tracts might underlie differences in cognitive capacity and vulnerabilities to mental disorders. The frontoparietal and cingulo-opercular networks of the brain constitute the central system supporting cognitive functions, and functional connectivity of these networks has been used to distinguish individuals known as "functional fingerprinting." The frontal aslant tract (FAT) that passes through the two networks has been implicated in executive functions. However, whether FAT can be used as a "structural fingerprint" to distinguish individuals and predict an individual's cognitive function and dysfunction is unknown. Here we investigated the fingerprinting property of FAT microstructural profiles using three independent diffusion MRI datasets with repeated scans on human participants including both females and males. We found that diffusion and geometric profiles of FAT can be used to distinguish individuals with a high accuracy. Next, we demonstrated that fractional anisotropy in different FAT segments predicted distinct cognitive functions, including working memory, inhibitory control, and relational reasoning. Finally, we assessed the contribution of altered FAT microstructural profiles to cognitive dysfunction in unmedicated patients with obsessive-compulsive disorders. We found that the altered microstructure in FAT was associated with the severity of obsessive-compulsive symptoms. Collectively, our findings suggest that the microstructural profiles of FAT can identify individuals with a high accuracy and may serve as an imaging marker for predicting an individual's cognitive capacity and disease severity.SIGNIFICANCE STATEMENT The frontoparietal network and cingulo-opercular network of the brain constitute a dual-network architecture for human cognitive functions, and functional connectivity of these two networks can be used as a "functional fingerprint" to distinguish individuals. However, the structural underpinnings of these networks subserving individual heterogeneities in their functional connectivity and cognitive ability remain unknown. We show here that the frontal aslant tract (FAT) that passes through the two networks distinguishes individuals with a high accuracy. Further, we demonstrate that the diffusion profiles of FAT predict distinct cognitive functions in healthy subjects and are associated with the clinical symptoms in patients with obsessive-compulsive disorders. Our findings suggest that the FAT may serve as a unique structural fingerprint underlying individual cognitive capability.

The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device.

In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.

Bayesian reduced rank regression models generalizable neural fingerprints that differentiate between individuals in magnetoencephalography data.

Recent magnetoencephalography (MEG) studies have reported that functional connectivity (FC) and power spectra can be used as neural fingerprints in differentiating individuals. Such studies have mainly used correlations between measurement sessions to distinguish individuals from each other. However, it has remained unclear whether such correlations might reflect a more generalizable principle of individually distinctive brain patterns. Here, we evaluated a machine-learning based approach, termed latent-noise Bayesian reduced rank regression (BRRR) as a means of modelling individual differences in the resting-state MEG data of the Human Connectome Project (HCP), using FC and power spectra as neural features. First, we verified that BRRR could model and reproduce the differences between metrics that correlation-based fingerprinting yields. We trained BRRR models to distinguish individuals based on data from one measurement and used the models to identify subsequent measurement sessions of those same individuals. The best performing BRRR models, using only 20 spatiospectral components, were able to identify subjects across measurement sessions with over 90% accuracy, approaching the highest correlation-based accuracies. Using cross-validation, we then determined whether that BRRR model could generalize to unseen subjects, successfully classifying the measurement sessions of novel individuals with over 80% accuracy. The results demonstrate that individual neurofunctional differences can be reliably extracted from MEG data with a low-dimensional predictive model and that the model is able to classify novel subjects.

Reliability of energy landscape analysis of resting-state functional MRI data.

Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Exploring agro-morphological and fiber traits diversity in cotton (G. barbadense L.).

Cotton (Gossypium barbadense L.) is a leading fiber and oilseed crop globally, but genetic diversity among breeding materials is often limited. This study analyzed genetic variability in 14 cotton genotypes from Egypt and other countries, including both cultivated varieties and wild types, using agro-morphological traits and genomic SSR markers. Field experiments were conducted over two seasons to evaluate 12 key traits related to plant growth, yield components, and fiber quality. Molecular diversity analysis utilized 10 SSR primers to generate DNA profiles. The Molecular diversity analysis utilized 10 SSR primers to generate DNA profiles. Data showed wide variation for the morphological traits, with Egyptian genotypes generally exhibiting higher means for vegetative growth and yield parameters. The top-performing genotypes for yield were Giza 96, Giza 94, and Big Black Boll genotypes, while Giza 96, Giza 92, and Giza 70 ranked highest for fiber length, strength, and fineness. In contrast, molecular profiles were highly polymorphic across all genotypes, including 82.5% polymorphic bands out of 212. Polymorphism information content was high for the SSR markers, ranging from 0.76 to 0.86. Genetic similarity coefficients based on the SSR data varied extensively from 0.58 to 0.91, and cluster analysis separated genotypes into two major groups according to geographical origin. The cotton genotypes displayed high diversity in morphology and genetics, indicating sufficient variability in the germplasm. The combined use of physical traits and molecular markers gave a thorough understanding of the genetic diversity and relationships between Egyptian and global cotton varieties. The SSR markers effectively profiled the genotypes and can help select ideal parents for enhancing cotton through hybridization and marker-assisted breeding.

Hour-Level Persistent Multicolor Phosphorescence Enabled by Carbon Dot-Based Nanocomposites Through a Multi-Confinement-Based Approach.

Hour-level persistent room temperature phosphorescence (RTP) phenomena based on multi-confinement carbon dots (CDs) are reported. The CDs-based system reported here (named Si-CDs@B2O3) can be efficiently synthesized by a simple pyrolysis method compared to the established persistent RTP systems. The binding modes of CDs, silica (SiO2), and boron oxide (B2O3) are deduced from a series of characterizations including XRD, FT-IR, and TEM characterization. Further studies show that the formation of covalent bonds between B2O3, SiO2, and CDs play a key role in activating the persistent RTP and preventing its quenching. This is a rare example of a persistent RTP system that exhibits hourly persistent RTP under environmental conditions. Finally, the applications of Si-CDs@B2O3 are demonstrated for anti-counterfeiting, long-duration phosphorescence imaging, and fingerprinting. This synthetic strategy is expected to provide strong technical support for the preparation of persistent RTP CDs and pave the way for the synthesis of persistent RTP CDs in the future.

Unraveling contributions to the Z-spectrum signal at 3.5 ppm of human brain tumors.

PURPOSE: To evaluate the influence of the confounding factors, direct water saturation (DWS), and magnetization transfer contrast (MTC) effects on measured Z-spectra and amide proton transfer (APT) contrast in brain tumors. METHODS: High-grade glioma patients were scanned using an RF saturation-encoded 3D MR fingerprinting (MRF) sequence at 3 T. For MRF reconstruction, a recurrent neural network was designed to learn free water and semisolid macromolecule parameter mappings of the underlying multiple tissue properties from saturation-transfer MRF signals. The DWS spectra and MTC spectra were synthesized by solving Bloch-McConnell equations and evaluated in brain tumors. RESULTS: The dominant contribution to the saturation effect at 3.5 ppm was from DWS and MTC effects, but 25%-33% of the saturated signal in the gadolinium-enhancing tumor (13%-20% for normal tissue) was due to the APT effect. The APT# signal of the gadolinium-enhancing tumor was significantly higher than that of the normal-appearing white matter (10.1% vs. 8.3% at 1 µT and 11.2% vs. 7.8% at 1.5 µT). CONCLUSION: The RF saturation-encoded MRF allowed us to separate contributions to the saturation signal at 3.5 ppm in the Z-spectrum. Although free water and semisolid MTC are the main contributors, significant APT contrast between tumor and normal tissues was observed.

Quantification of water exchange across the blood-brain barrier using noncontrast MR fingerprinting.

PURPOSE: A method is proposed to quantify cerebral blood volume ( v b $$ {v}_b $$ ) and intravascular water residence time ( τ b $$ {\tau}_b $$ ) using MR fingerprinting (MRF), applied using a spoiled gradient echo sequence without the need for contrast agent. METHODS: An in silico study optimized an acquisition protocol to maximize the sensitivity of the measurement to v b $$ {v}_b $$ and τ b $$ {\tau}_b $$ changes. Its accuracy in the presence of variations in T 1 , t $$ {\mathrm{T}}_{1,t} $$ , T 1 , b $$ {\mathrm{T}}_{1,b} $$ , and B 1 $$ {\mathrm{B}}_1 $$ was evaluated. The optimized protocol (scan time of 19 min) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24 ± $$ \pm $$ 3, six males) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability. RESULTS: Simulations show that assuming literature values for T 1 , b $$ {\mathrm{T}}_{1,b} $$ and T 1 , t $$ {\mathrm{T}}_{1,t} $$ , no variation in B 1 $$ {\mathrm{B}}_1 $$ , while fitting only v b $$ {v}_b $$ and τ b $$ {\tau}_b $$ , leads to large errors in quantification of v b $$ {v}_b $$ and τ b $$ {\tau}_b $$ , regardless of noise levels. However, simulations also show that matching T 1 , t $$ {\mathrm{T}}_{1,t} $$ , T 1 , b $$ {\mathrm{T}}_{1,b} $$ , B 1 + $$ {\mathrm{B}}_1^{+} $$ , v b $$ {v}_b $$ and τ b $$ {\tau}_b $$ , simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and gray matter T 1 , t $$ {\mathrm{T}}_{1,t} $$ (p < 0.0001) and v b $$ {v}_b $$ (p < 0.0001) are observed, T 1 , b $$ {\mathrm{T}}_{1,b} $$ and τ b $$ {\tau}_b $$ show no significant differences, p = 0.4 and p = 0.6, respectively. Moderate to excellent repeatability was seen between repeat scans: mean intra-class correlation coefficient of T 1 , t : 0 . 91 $$ {\mathrm{T}}_{1,t}:0.91 $$ , T 1 , b : 0 . 58 $$ {\mathrm{T}}_{1,b}:0.58 $$ , v b : 0 . 90 $$ {v}_b:0.90 $$ , and τ b : 0 . 96 $$ {\tau}_b:0.96 $$ . CONCLUSION: We demonstrate that regional simultaneous quantification of v b $$ {v}_b $$ , τ b $$ {\tau}_b $$ , T 1 , b , T 1 , t $$ {\mathrm{T}}_{1,b},{T}_{1,t} $$ , and B 1 + $$ {\mathrm{B}}_1^{+} $$ using MRF is feasible in vivo.

Improved reconstruction of crossing fibers in the mouse optic pathways with orientation distribution function fingerprinting.

PURPOSE: The accuracy of diffusion MRI tractography reconstruction decreases in the white matter regions with crossing fibers. The optic pathways in rodents provide a challenging structure to test new diffusion tractography approaches because of the small crossing volume within the optic chiasm and the unbalanced 9:1 proportion between the contra- and ipsilateral neural projections from the retina to the lateral geniculate nucleus, respectively. METHODS: Common approaches based on Orientation Distribution Function (ODF) peak finding or statistical inference were compared qualitatively and quantitatively to ODF Fingerprinting (ODF-FP) for reconstruction of crossing fibers within the optic chiasm using in vivo diffusion MRI ( n = 18 $$ n=18 $$ healthy C57BL/6 mice). Manganese-Enhanced MRI (MEMRI) was obtained after intravitreal injection of manganese chloride and used as a reference standard for the optic pathway anatomy. RESULTS: ODF-FP outperformed by over 100% all the tested methods in terms of the ratios between the contra- and ipsilateral segments of the reconstructed optic pathways as well as the spatial overlap between tractography and MEMRI. CONCLUSION: In this challenging model system, ODF-Fingerprinting reduced uncertainty of diffusion tractography for complex structural formations of fiber bundles.

Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T1 and T2 mapping of entire macaque brain.

PURPOSE: Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system. METHODS: Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains. RESULTS: The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35 × $$ \times $$ 0.35 × $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol. CONCLUSION: Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.

Quantifying 3D MR fingerprinting (3D-MRF) reproducibility across subjects, sessions, and scanners automatically using MNI atlases.

PURPOSE: Quantitative MRI techniques such as MR fingerprinting (MRF) promise more objective and comparable measurements of tissue properties at the point-of-care than weighted imaging. However, few direct cross-modal comparisons of MRF's repeatability and reproducibility versus weighted acquisitions have been performed. This work proposes a novel fully automated pipeline for quantitatively comparing cross-modal imaging performance in vivo via atlas-based sampling. METHODS: We acquire whole-brain 3D-MRF, turbo spin echo, and MPRAGE sequences three times each on two scanners across 10 subjects, for a total of 60 multimodal datasets. The proposed automated registration and analysis pipeline uses linear and nonlinear registration to align all qualitative and quantitative DICOM stacks to Montreal Neurological Institute (MNI) 152 space, then samples each dataset's native space through transformation inversion to compare performance within atlas regions across subjects, scanners, and repetitions. RESULTS: Voxel values within MRF-derived maps were found to be more repeatable (σT1 = 1.90, σT2 = 3.20) across sessions than vendor-reconstructed MPRAGE (σT1w = 6.04) or turbo spin echo (σT2w = 5.66) images. Additionally, MRF was found to be more reproducible across scanners (σT1 = 2.21, σT2 = 3.89) than either qualitative modality (σT1w = 7.84, σT2w = 7.76). Notably, differences between repeatability and reproducibility of in vivo MRF were insignificant, unlike the weighted images. CONCLUSION: MRF data from many sessions and scanners can potentially be treated as a single dataset for harmonized analysis or longitudinal comparisons without the additional regularization steps needed for qualitative modalities.

3D MR fingerprinting for dynamic contrast-enhanced imaging of whole mouse brain.

PURPOSE: Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping across the whole mouse brain with 4.3-min temporal resolution. METHOD: We designed a 3D MRF sequence with variable acquisition segment lengths and magnetization preparations on a 9.4T preclinical MRI scanner. Model-based reconstruction approaches were employed to improve the accuracy and speed of MRF acquisition. The method's accuracy for T1 and T2 measurements was validated in vitro, while its repeatability of T1 and T2 measurements was evaluated in vivo (n = 3). The utility of the 3D MRF sequence for dynamic tracking of intracisternally infused gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) in the whole mouse brain was demonstrated (n = 5). RESULTS: Phantom studies confirmed accurate T1 and T2 measurements by 3D MRF with an undersampling factor of up to 48. Dynamic contrast-enhanced MRF scans achieved a spatial resolution of 192 × 192 × 500 µm3 and a temporal resolution of 4.3 min, allowing for the analysis and comparison of dynamic changes in concentration and transport kinetics of intracisternally infused Gd-DTPA across brain regions. The sequence also enabled highly repeatable, high-resolution T1 and T2 mapping of the whole mouse brain (192 × 192 × 250 µm3) in 30 min. CONCLUSION: We present the first dynamic and multi-parametric approach for quantitatively tracking contrast agent transport in the mouse brain using 3D MRF.

Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index.

PURPOSE: For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design. METHODS: By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects. RESULTS: The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences. CONCLUSION: We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.

Deep image prior cine MR fingerprinting with B1 + spin history correction.

PURPOSE: To develop a deep image prior (DIP) reconstruction for B1 + -corrected 2D cine MR fingerprinting (MRF). METHODS: The proposed method combines low-rank (LR) modeling with a DIP to generate cardiac phase-resolved parameter maps without motion correction, employing self-supervised training to enforce consistency with undersampled spiral k-space data. Two implementations were tested: one approach (DIP) for cine T1 , T2 , and M0 mapping, and a second approach (DIP with effective B1 + estimation [DIP-B1]) that also generated an effective B1 + map to correct for errors due to RF transmit inhomogeneities, through-plane motion, and blood flow. Cine MRF data were acquired in 14 healthy subjects and four reconstructions were compared: LR, low-rank motion-corrected (LRMC), DIP, and DIP-B1. Results were compared to diastolic ECG-triggered MRF, MOLLI, and T2 -prep bSSFP. Additionally, bright-blood and dark-blood images calculated from cine MRF maps were used to quantify ventricular function and compared to reference cine measurements. RESULTS: DIP and DIP-B1 outperformed other cine MRF reconstructions with improved noise suppression and delineation of high-resolution details. Within-segment variability in the myocardium (reported as the coefficient of variation for T1 /T2 ) was lowest for DIP-B1 (2.3/8.3%) followed by DIP (2.7/8.7%), LRMC (3.5/10.5%), and LR (15.3/39.6%). Spatial homogeneity improved with DIP-B1 having the lowest intersegment variability (2.6/4.1%). The mean bias in ejection fraction was -1.1% compared to reference cine scans. CONCLUSION: A DIP reconstruction for 2D cine MRF enabled cardiac phase-resolved mapping of T1 , T2 , M0 , and the effective B1 + with improved noise suppression and precision compared to LR and LRMC.

Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification.

PURPOSE: To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating. METHODS: The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments. RESULTS: The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved. CONCLUSION: The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency.

Simultaneous perfusion, diffusion, T2 *, and T1 mapping with MR fingerprinting.

PURPOSE: Quantitative mapping of brain perfusion, diffusion, T2 *, and T1 has important applications in cerebrovascular diseases. At present, these sequences are performed separately. This study aims to develop a novel MRI technique to simultaneously estimate these parameters. METHODS: This sequence to measure perfusion, diffusion, T2 *, and T1 mapping with magnetic resonance fingerprinting (MRF) was based on a previously reported MRF-arterial spin labeling (ASL) sequence, but the acquisition module was modified to include different TEs and presence/absence of bipolar diffusion-weighting gradients. We compared parameters derived from the proposed method to those derived from reference methods (i.e., separate sequences of MRF-ASL, conventional spin-echo DWI, and T2 * mapping). Test-retest repeatability and initial clinical application in two patients with stroke were evaluated. RESULTS: The scan time of our proposed method was 24% shorter than the sum of the reference methods. Parametric maps obtained from the proposed method revealed excellent image quality. Their quantitative values were strongly correlated with those from reference methods and were generally in agreement with values reported in the literature. Repeatability assessment revealed that ADC, T2 *, T1 , and B1 + estimation was highly reliable, with voxelwise coefficient of variation (CoV) <5%. The CoV for arterial transit time and cerebral blood flow was 16% ± 3% and 25% ± 9%, respectively. The results from the two patients with stroke demonstrated that parametric maps derived from the proposed method can detect both ischemic and hemorrhagic stroke. CONCLUSION: The proposed method is a promising technique for multi-parametric mapping and has potential use in patients with stroke.

State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis.

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting.

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2 , and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.