Microstructure Map Research Articles

Diffusion MRI with machine learning

Abstract Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high intersession and interscanner variability in the data, as well as intersubject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

Submicrometer Scale Mapping of Microstructure and Mechanical Properties of Cold Sprayed Copper

Submicrometer Scale Mapping of Microstructure and Mechanical Properties of Cold Sprayed Copper

Impact of atmospheric plasma spraying parameters on microstructure, mechanical properties and thermal cycling performance of YSZ coatings

Yttria-stabilized zirconia (YSZ) coatings are widely utilized thermal protective layers for metal and superalloy surfaces. These coatings are employed as thermal barrier coating (TBCs) to insulate metallic parts from higher thermal energy, thereby minimizing the cooling need for the topcoat ceramic layer. The choice of material is 7 wt% YSZ due to its exceptional capability to withstand challenging conditions characterized by extremely high temperatures and pressures, typically employed by the atmospheric plasma spraying (APS) in gas turbines, effectively extending their lifespan and improving performance. The deposition process of TBCs is significantly influenced by various spraying parameters, including gas flow rate and current (amp). These key parameters mutually play a significant role in controlling thermal stability, phase composition, and improved mechanical and microstructure properties. The aim of this research is to evaluate and contrast the impact of various spraying parameters on YSZ TBC performance. Accordingly, the influence of Hydrogen (H2), Argon (Ar) flow rate, and Current (amp) was investigated. Microstructure and elemental mapping studying was conducted using Field Emission Scanning Electron Microscopy FESEM/EDS and phase studying was examined with X-ray diffraction (XRD). Porosity was measured by IMAGE J software while hardness measurement was calculated by Vickers hardness tester. Additionally, thermal cycling tests were conducted between 700 °C and 1200 °C. The porous coatings exhibited poor performance, delaminating early during the tests. In contrast, dense coatings performed significantly better, enduring up to 140 cycles before failure.

Detecting Cellular Microstructural Changes of Liver Fibrosis with Time-Dependent Diffusion MRI.

Background The potential of time-dependent diffusion MRI in imaging the progression from liver fibrosis to cirrhosis has not been established. Purpose To assess the effectiveness of time-dependent diffusion MRI in mapping the microstructure and characterizing cellular attributes during the progression of liver fibrosis to cirrhosis and to investigate its potential in grading liver fibrosis. Materials and Methods This prospective study, performed between December 2022 and October 2023, used 60 rats to establish a liver fibrosis model by means of diethylnitrosamine administration, with five additional rats serving as control animals. Time-dependent diffusion MRI was performed with equivalent diffusion time of 5.4, 10.7, and 69.3 msec on a 3.0-T scanner. Time-dependent diffusion MRI-based microstructural parameters, including cell diameter, intracellular volume fraction (ICVF), cellularity, and extracellular diffusivity, were estimated with use of the imaging microstructural parameters using limited spectrally edited diffusion, or IMPULSED, model. The fitted microstructural parameters were validated with histopathologic measurements. Results All 60 rats developed liver fibrosis, with a noticeable decrease in cell diameter and an increase in ICVF and cellularity observed as liver fibrosis progressed. The diameter measured at pathologic examination ranged from 11.4 μm to 35.4 μm, aligning with the range of 12.4-33.4 μm observed in time-dependent diffusion MRI, which indicated a strong correlation (r = 0.84; P < .001). The quantified ICVF at pathologic examination ranged from 0.28 to 0.89 and varied from 0.23 to 0.85 at time-dependent diffusion MRI, showing a high correlation (r = 0.62; P < .001). The cellularity observed at pathologic examination increased from 0.74 to 5.85, while the cellularity measured at time-dependent diffusion MRI ranged from 0.77 to 3.70, showing a correlation (r = 0.44; P < .001). Conclusion This study revealed the changes in quantitative microstructural mapping across the spectrum from liver fibrosis to cirrhosis. Cell diameter, ICVF, and cellularity are reliable markers for liver fibrosis, with diameter and ICVF presenting good discrimination ability. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Matos and Metens in this issue.

Time-Dependent Diffusion MRI Helps Predict Molecular Subtypes and Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer.

Background Time-dependent diffusion MRI has the potential to help characterize tumor cell properties; however, to the knowledge of the authors, its usefulness for breast cancer diagnosis and prognostic evaluation is unknown. Purpose To investigate the clinical value of time-dependent diffusion MRI-based microstructural mapping for noninvasive prediction of molecular subtypes and pathologic complete response (pCR) in participants with breast cancer. Materials and Methods Participants with invasive breast cancer who underwent pretreatment with time-dependent diffusion MRI between February 2021 and May 2023 were prospectively enrolled. Four microstructural parameters were estimated using the IMPULSED method (a form of time-dependent diffusion MRI), along with three apparent diffusion coefficient (ADC) measurements and a relative ADC diffusion-weighted imaging parameter. Multivariable logistic regression analysis was used to identify parameters associated with each molecular subtype and pCR. A predictive model based on associated parameters was constructed, and its performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared by using the DeLong test. The time-dependent diffusion MRI parameters were validated based on correlation with pathologic measurements. Results The analysis included 408 participants with breast cancer (mean age, 51.9 years ± 9.1 [SD]). Of these, 221 participants were administered neoadjuvant chemotherapy and 54 (24.4%) achieved pCR. The time-dependent diffusion MRI parameters showed reasonable performance in helping to identify luminal A (AUC, 0.70), luminal B (AUC, 0.78), and triple-negative breast cancer (AUC, 0.72) subtypes and high performance for human epidermal growth factor receptor 2 (HER2)-enriched breast cancer (AUC, 0.85), outperforming ADC measurements (all P < .05). Progesterone receptor status (odds ratio [OR], 0.08; P = .02), HER2 status (OR, 3.36; P = .009), and the cellularity index (OR, 0.01; P = .02) were independently associated with the odds of achieving pCR. The combined model showed high performance for predicting pCR (AUC, 0.88), outperforming ADC measurements and the clinical-pathologic model (AUC, 0.73 and 0.79, respectively; P < .001). The time-dependent diffusion MRI-estimated parameters correlated well with the pathologic measurements (n = 100; r = 0.67-0.81; P < .001). Conclusion Time-dependent diffusion MRI-based microstructural mapping was an effective method for helping to predict molecular subtypes and pCR to neoadjuvant chemotherapy in participants with breast cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Partridge and Xu in this issue.

Microstructure and stress mapping in 3D at industrially relevant degrees of plastic deformation

Strength, ductility, and failure properties of metals are tailored by plastic deformation routes. Predicting these properties requires modeling of the structural dynamics and stress evolution taking place on several length scales. Progress has been hampered by a lack of representative 3D experimental data at industrially relevant degrees of deformation. We present an X-ray imaging based 3D mapping of an aluminum polycrystal deformed to the ultimate tensile strength (32% elongation). The extensive dataset reveals significant intra-grain stress variations (36 MPa) up to at least half of the inter-grain variations (76 MPa), which are dominated by grain orientation effects. Local intra-grain stress concentrations are candidates for damage nucleation. Such data are important for models of structure-property relations and damage.

Electrolyte jet tomography: Three-dimensional microstructure mapping with an electrochemical machine tool and an optical microscope

There is a general separation between the manufacturing processes that add value to materials on the factory floor and the techniques engineers use in the laboratory to evaluate the microstructures and the surface integrity that results. These techniques are often destructive or require a vacuum and are incompatible with production lines. However, this information has intrinsic value and could be exploited to inform production decisions during manufacture. In this study, a novel approach to acquire this information is presented that is underpinned by electrolyte jet machine tool coupled with optical microscopy, which can allow the extraction of both grain-wise partial orientation and morphological information, and crystallographic macro textures in three dimensions. Here, iterative sections are precisely machined into the near surface of a commercially pure titanium alloy using an electrochemical jet and subsequently imaged, allowing the reconstruction of high-fidelity microstructure models rapidly and under ambient conditions. In doing so, new insights into the specific orientation-dependent dissolution mechanisms are offered, and the acquisition of appropriate conditions that result in nanoscale roughness surfaces (avoiding the dominance of pitting and preferential grain removal) is firstly explored. Building on prior work, a piecewise approach is presented to analyse the acquired image stacks to map partial crystal orientations, while different approaches are proposed to account for jet-specific surface artefacts and waviness. This is repeated over 20 layers in an individual specimen and layer-wise orientation maps are used to construct volumetric models of the specimen. These data sets are then explored from the perspective of materials/manufacturing engineers, who may use to this information to effect advancements to materials processing technologies.

BundleAGE: Predicting White Matter Age using Along-Tract Microstructural Profiles from Diffusion MRI.

Brain Age Gap Estimation (BrainAGE) is an estimate of the gap between a person's chronological age (CA) and a measure of their brain's 'biological age' (BA). This metric is often used as a marker of accelerated aging, albeit with some caveats. Age prediction models trained on brain structural and functional MRI have been employed to derive BrainAGE biomarkers, for predicting the risk of neurodegeneration. While voxel-based and along-tract microstructural maps from diffusion MRI have been used to study brain aging, no studies have evaluated along-tract microstructure for computing BrainAGE. In this study, we train machine learning models to predict a person's age using along-tract microstructural profiles from diffusion tensor imaging. We were able to demonstrate differential aging patterns across different white matter bundles and microstructural measures. The novel Bundle Age Gap Estimation (BundleAGE) biomarker shows potential in quantifying risk factors for neurodegenerative diseases and aging, while incorporating finer scale information throughout white matter bundles.

Physical chitosan hydrogels loaded with TiO2 nanoparticles/resveratrol hydroalcoholic extract: Hemocompatibility, swelling, microstructural, and EDX mapping analysis

Physical hydrogels produced by chitosan low (LCh) and medium molecular weight (MCh) were loaded with TiO2 nanoparticles (TiO2NPs) and TiO2 nanoparticles loaded with resveratrol extract (Polygonum cuspidatum) (TiO2NPsRes). Equilibrium water content, EDX mapping analysis, and hemocompatibility of chitosan hydrogels were evaluated. Chitosan hydrogels preserved their porous structure, displaying mean pore size between 111.7 and 139 µm. The equilibrium water content of the physical chitosan hydrogels reached values of 24 times their mass in distilled water, decreasing with the addition of TiO2. The EDX analysis corroborated that chitosan hydrogels displayed high titanium concentration (8.8–9.5 % w/w), while resveratrol extract reduced the load of titanium in the hydrogels (0.2 and 2.1 % w/w). The hemocompatibility of hydrogels was suitable with hemolytic values as low as 2.1 and 3.1 %. Physical chitosan hydrogels loaded with TiO2 and TiO2Res can be candidates for biomaterials in biomedical applications.

Time-dependent diffusion MRI-based microstructural mapping for differentiating high-grade serous ovarian cancer from serous borderline ovarian tumor

PurposeTo investigate the value of microstructural characteristics derived from time-dependent diffusion MRI in distinguishing high-grade serous ovarian cancer (HGSOC) from serous borderline ovarian tumor (SBOT) and the associations of immunohistochemical markers with microstructural features. MethodsTotally 34 HGSOC and 12 SBOT cases who received preoperative pelvic MRI were retrospectively included in this study. Two radiologists delineated the tumors to obtain the regions of interest (ROIs). Time-dependent diffusion MRI signals were fitted by the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model, to extract microstructural parameters, including fraction of the intracellular component (fin), cell diameter (d), cellularity and extracellular diffusivity (Dex). Apparent diffusion coefficient (ADC) values were obtained from standard diffusion-weighted imaging (DWI). The parameters of HGSOCs and SBOTs were compared, and the diagnostic performance was evaluated. The associations of microstructural indexes with immunopathological parameters were assessed, including Ki-67, P53, Pax-8, ER and PR. ResultsIn this study, fin, cellularity, Dex and ADC had good diagnostic performance levels in differentiating HGSOC from SBOT, with AUCs of 0.936, 0.909, 0.902 and 0.914, respectively. There were no significant differences in diagnostic performance among these parameters. Spearman analysis revealed in the HGSOC group, cellularity had a significant positive correlation with P53 expression (P = 0.028, r = 0.389) and Dex had a significant positive correlation with Pax-8 expression (P = 0.018, r = 0.415). ICC showed excellent agreement for all parameters. ConclusionTime-dependent diffusion MRI had value in evaluating the microstructures of HGSOC and SBOT and could discriminate between these tumors.

A data-driven intravoxel mean diffusivities distribution approach for molecular classifications and MIB-1 prediction of gliomas.

Measuring non-parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes. We developed a data-driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB-1 labeling index (LI) levels and molecular status of gliomas. An FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR . Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo-echo planar imaging (SE-EPI) diffusion-weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C-means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions ( ) and diffusivities of the maximum peaks ( ). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann-Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation. The simulation results showed that the FCN-based MDD approach (FCN-MDD) achieved higher CSS than non-negative least squares-based MDD (NNLS-MDD). For in vivo data, the spectra of ET and NET obtained by FCN-MDD are more distinguishable than NNLS-MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non-enhancing tumor, edema, and necrosis). showed a positive and negative correlation with MIB-1 respectively ( , all ). The AUC of for predicting MIB-1 LI levels was 0.900 (95% CI, 0.826-0.974), versus 0.781 (0.677-0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model ( ) was 0.873 (95% CI, 0.802-0.944). The proposed FCN-MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS-MDD method. FCN-MDD also enabled qualitative and quantitative evaluation of the composition of gliomas.

Reconstruction of martensite variant microstructures in grains of deformed NiTi shape memory alloy by TEM

Two transmission electron microscopy (TEM) based methods were applied to reconstruct martensite variant microstructures in entire grains of plastically deformed nanocrystalline NiTi wires. First method consists in manual TEM analysis of composite electron diffraction patterns and dark field images of microstructures taken using carefully selected Bragg spots. Second method involves automated orientation/phase mapping of martensite variant microstructures in TEM (ASTAR). Application of these two methods helped us to discover new mechanism of the plastic deformation of monoclinic B19’ martensite in NiTi shape memory alloys called kwinking as it combines dislocation slip based kinking and deformation twinning in martensite.In this work, we describe how the martensite variant microstructures in nanograins of plastically deformed NiTi shape memory wire are reconstructed using the TEM based methods and discuss their advantages and disadvantages. The application of the first manual method requires tilting the TEM lamella so that all martensite variants in the analysed grain are oriented in a common low index zone. Such common crystal lattice direction exists in each grain of plastically deformed martensitic NiTi wire because the microstructures were created by the kwinking deformation mechanism. The requirement to orient the selected grain into a common unique direction in monoclinic lattice, on the other hand, is not unique to kwinking and represents a severe limitation of the SAED-DF method due to the constraints on tilting the TEM lamella. Application of the automated crystal orientation mapping ASTAR method enables detailed orientation analysis of nanoscale martensite variant microstructures in generally oriented grains of deformed NiTi wires as well as pointwise ex-situ analysis of interfaces with excellent resolution in real and orientation space.

Finding the limits of deep learning clinical sensitivity with fractional anisotropy (FA) microstructure maps.

Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the data,-are very useful to study neurological diseases. To fit this map accurately, acquisition times of the order of several minutes are needed because many noncollinear DW volumes must be acquired to reduce directional biases. Deep learning (DL) can be used to reduce acquisition times by reducing the number of DW volumes. We already developed a DL network named "one-minute FA," which uses 10 DW volumes to obtain FA maps, maintaining the same characteristics and clinical sensitivity of the FA maps calculated with the standard method using more volumes. Recent publications have indicated that it is possible to train DL networks and obtain FA maps even with 4 DW input volumes, far less than the minimum number of directions for the mathematical estimation of the DT. Here we investigated the impact of reducing the number of DW input volumes to 4 or 7, and evaluated the performance and clinical sensitivity of the corresponding DL networks trained to calculate FA, while comparing results also with those using our one-minute FA. Each network training was performed on the human connectome project open-access dataset that has a high resolution and many DW volumes, used to fit a ground truth FA. To evaluate the generalizability of each network, they were tested on two external clinical datasets, not seen during training, and acquired on different scanners with different protocols, as previously done. Using 4 or 7 DW volumes, it was possible to train DL networks to obtain FA maps with the same range of values as ground truth - map, only when using HCP test data; pathological sensitivity was lost when tested using the external clinical datasets: indeed in both cases, no consistent differences were found between patient groups. On the contrary, our "one-minute FA" did not suffer from the same problem. When developing DL networks for reduced acquisition times, the ability to generalize and to generate quantitative biomarkers that provide clinical sensitivity must be addressed.

Enhanced properties of multi‐element soluted (Nb0.8Ti0.05Zr0.05Mo0.05M0.05)4AlC3 (M = Hf, Ta) ceramics

AbstractRecently multielements solid solution has shown significant improvement to the mechanical properties of parent MAX phases. Therefore, in this work, five elements with different radii were incorporated to check the effect on properties of MAX phases. (Nb0.8Ti0.05Zr0.05Mo0.05Hf0.05)4AlC3 (MAXHf) and (Nb0.8Ti0.05Zr0.05Mo0.05Ta0.05)4AlC3 (MAXTa) ceramics were successfully synthesized using the spark plasma sintering technique. The microstructure and elemental map analysis results further confirmed that the five transition metals were successfully solid soluted at the M‐sites of the hexagonal M4AlC3 unit cell. The mean elemental compositions for M‐site elements were achieved as Nb0.85Ti0.052Zr0.035Mo0.027Hf0.036 and Nb0.847Ti0.051Zr0.043Mo0.025Ta0.033 for MAXHf and MAXTa ceramics, respectively. The electrical and thermal conductivities of multielement solid solution MAX phases were decreased compared to pure Nb4AlC3. However, Mechanical properties were significantly increased with the solid solution of five transition metals. The fracture toughness, flexural strength, compressive strength and Vickers hardness (10 N) of MAXHf and MAXTa ceramics were achieved as 8.87 MPa m1/2, 448 MPa, 867 MPa, 6.5 GPa and 10.36 MPa m1/2, 557 MPa, 1039 MPa, 8.2 GPa, respectively. The enhanced mechanical properties suggest the effectiveness of the solid solution strengthening effect and provide new opportunities to further tailor the mechanical properties of the MAX phase ceramics.

Grain boundary-mediated plasticity in aluminum films unraveled by a statistical approach combining nano-DIC and ACOM-TEM

Nanomechanical on-chip testing is combined with nanoscale in situ digital image correlation and automated crystal orientation mapping in TEM to deliver novel statistically representative quantitative data about the deformation mechanisms in nanocrystalline aluminum films. The films are very ductile, with a rare stable multiple necking process with local strains reaching up to 0.45 and macroscopic elongation up to 0.17. The strain fields with resolution below 100 nm are related to the underlying microstructure and crystallographic orientation maps. This reveals nanoscopic shear bands forming preferentially along GB with high misorientations, tilted at +/− 45° with respect to loading direction. The analysis of these data prove that the strong strain delocalization process is promoted by GB migration and grain rotation, leading to large strain rate sensitivity. The distribution of misorientation angles between grains evolve during deformation. The GBs with misorientation between 20° and 40°, which are the GBs with highest energy, involve the largest strains.

Stressful crystal histories recorded around melt inclusions in volcanic quartz

Magma ascent and eruption are driven by a set of internally and externally generated stresses that act upon the magma. We present microstructural maps around melt inclusions in quartz crystals from six large rhyolitic eruptions using synchrotron Laue X-ray microdiffraction to quantify elastic residual strain and stress. We measure plastic strain using average diffraction peak width and lattice misorientation, highlighting dislocations and subgrain boundaries. Quartz crystals across studied magma systems preserve similar and relatively small magnitudes of elastic residual stress (mean 53–135 MPa, median 46–116 MPa) in comparison to the strength of quartz (~ 10 GPa). However, the distribution of strain in the lattice around inclusions varies between samples. We hypothesize that dislocation and twin systems may be established during compaction of crystal-rich magma, which affects the magnitude and distribution of preserved elastic strains. Given the lack of stress-free haloes around faceted inclusions, we conclude that most residual strain and stress was imparted after inclusion faceting. Fragmentation may be one of the final strain events that superimposes stresses of ~ 100 MPa across all studied crystals. Overall, volcanic quartz crystals preserve complex, overprinted deformation textures indicating that quartz crystals have prolonged deformation histories throughout storage, fragmentation, and eruption.

Improving Microstructural Estimation in Time-Dependent Diffusion MRI With a Bayesian Method.

Accurately fitting diffusion-time-dependent diffusion MRI (td-dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, and limited clinical data acquisition. Introduce a Bayesian methodology to refine microstructural fitting within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model and optimize the prior distribution within the Bayesian framework. Retrospective. Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3-9 years, 61% male) with 41 low-grade and 28 high-grade gliomas, of which 76.8% were identified within the brainstem or cerebellum. 3 T, oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE). The Bayesian method's performance in fitting cell diameter ( ), intracellular volume fraction ( ), and extracellular diffusion coefficient ( ) was compared against the NLLS method, considering simulated and experimental data. The tumor region-of-interest (ROI) were manually delineated on the b0 images. The diagnostic performance in distinguishing high- and low-grade gliomas was assessed, and fitting accuracy was validated against H&E-stained pathology. T-test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at P < 0.05. Bayesian methodology manifested increased accuracy with robust estimates in simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, and STD decreased by 29.2%, 43.5%, and 24.0%, respectively for , , and compared to NLLS), indicating fewer outliers and reduced error. Diagnostic performance for tumor grade was similar in both methods, however, Bayesian method generated smoother microstructural maps (outliers ratio decreased by 45.3% ± 19.4%) and a marginal enhancement in correlation with H&E staining result (r = 0.721 for compared to r = 0.698 using NLLS, P = 0.5764). The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure. 3 TECHNICAL EFFICACY: Stage 1.

Microstructural Map and Phase Chemical Compositions in Hybrid Multi-component Cast Alloys Fe–W–Mo–V–Cr–Ti–(1.5–3.5 Wt Pct)B–(0.3–1.1 Wt Pct)C

Microstructural Map and Phase Chemical Compositions in Hybrid Multi-component Cast Alloys Fe–W–Mo–V–Cr–Ti–(1.5–3.5 Wt Pct)B–(0.3–1.1 Wt Pct)C

Segregation effects of doped VN and ZrN on magnetic and microstructural characteristics of FePt-based films

Segregants are essential materials for forming the complexation or granular microstructure in thin films for magnetic recording media. One such film, FePt-BN, has a metallic L10 FePt main phase as its matrix, in addition to having BN and C segregants at the grain boundaries. Amorphous BN has sufficient mechanical strength to support the columnar growth of FePt grains, however, it has a lower deposition rate resulting in columnar grains with a nonuniform aspect ratio. Moreover, some B diffuses in the interstitial site of FePt lattices. To overcome these limitations, this study added the transition nitride VN and ZrN with equal volume percentages to replace some BN segregants in FePt(BN, Ag, C) films: substantially different segregation behaviors were observed. Microstructural imaging mapping revealed that although VN segregated the FePt grains boundary, ZrN was diffused in the lattice. The FePt-BN-ZrN film thus had more disordered grains contributing to a higher in-plane magnetization and wider hard axis magnetic hysteresis loops than did FePt-BN-VN. The FePt-BN-VN film has the highest perpendicular magnetic anisotropy constant (Ku= 2.17 × 106 J/m3) and coercivity (Hc= 4 T); moreover, parallel remanence magnetization was minimal with a linear in-plane magnetization loop. The FePt-BN-VN film was sputtered directly on MgO(100) but depositing the FePt-BN-ZrN film required a 2-nm-thick FePt nucleation layer. The high atomic number of Zr and high sputter yield ratio (YN/Ymetal) of ZrN may increase the sites occupied by N, potentially affecting ZrN segregation.

A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion–relaxation correlation MRI

Multi-dimensional diffusion–relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.