Imaging Mapping Research Articles

Land use and cover changes and sand fly (Diptera: Psychodidae) assemblages in an emerging focus of leishmaniasis.

Land use and cover changes lead to fragmentation of the natural habitats of sand flies and modify the epidemiological profile of leishmaniasis. This process contributes to the infestation of adjacent rural settlements by vector sand fly species with different degrees of adaptation, promoting leishmaniasis outbreaks. This study aimed to assess land use and cover changes over a 12-year period and investigate the diversity and abundance of sand fly assemblages in the rural area of Codó, Maranhão State, Brazil. Temporal analysis of land use and cover changes was conducted using Sentinel-2 satellite imagery treated in QGIS software (free version 3.10) and classified using Orfeo Toolbox. Sand flies were sampled in alternate months between August 2022 and June 2023 using Centers for Disease Control and Prevention (CDC) light traps and white and black Shannon traps installed in peridomestic and extradomestic environments. Map images showed that the predominant land covers in 2012 and 2014 were dense and sparse vegetation, with few buildings. In 2021 and 2023, areas of sparse and dense vegetation were fragmented as new settlements were established. The entomological survey resulted in the capture of 3375 sand flies (CDC = 856, white Shannon = 650, black Shannon = 1969) belonging to 20 species. The most abundant species were Psychodopygus wellcomei Fraiha, Shaw & Lainson, 1971 (78.19%), followed by Nyssomyia whitmani (Antunes & Coutinho, 1939) (7.53%). Additionally, Ny. whitmani was the most abundant species (84.97%) in peridomestic environments, whereas Ps. wellcomei was the most abundant species (96.51%) in extradomestic environments. The sand fly assemblage was highly diverse, with a high abundance of competent vectors of Leishmania spp. These findings can promote community participation in surveillance and control efforts to prevent leishmaniasis cases.

Gas-Phase Scattering of Transition Metal Atoms Fe, Ir, and Pt with CH4, O2, and CO2.

Understanding the interactions between transition metal atoms and molecules is important for the study of various related chemical and physical processes. In this study, we have investigated collisions between iron (Fe), iridium (Ir), and platinum (Pt) and the small molecules CH4, O2, and CO2 using a crossed-beam and time-sliced ion velocity map imaging technique. Elastic collisions were observed in all cases, except for collisions of Pt with O2 and CO2. Collisions of Fe or Ir with CH4, O2, and CO2 show mainly long-range attractive potentials at large impact parameters leading to forward scattering, whereas sideways and backward scatterings indicate the formation of short-lived complexes with lifetimes comparable to their rotational periods. In collisions of Pt with O2 and CO2, Pt may react with the gases and become chemically bound to them, which can deplete the nonreactive scattering signal. The insights gained from this study provide a foundation for improved understanding of the complex interactions between transitional metal atoms and other molecules.

Dynamical study of the reaction of N+ ions with O2 neutrals

Abstract The ion-molecule reaction N+ + O2 has been investigated experimentally using crossed beam velocity map imaging in the collision energy range from 0.15 eV to 3.0 eV. From the differential scattering cross sections, information on the branching ratios, the electronic states and vibrational levels populated in the two product channels, O2+ + N and NO+ + O, have been obtained. These data are compared with previous results obtained using a rotatable ion source setup developed many years ago at the University of Freiburg, Germany. Furthermore, a small fraction of NO+ products populate excited electronic states as observed in chemiluminescence measurements.

Construction and Characterization of Multi‐Stimuli Responsive Shape Memory Assisted Self‐Healing Thermoplastic Elastomeric Materials Based on TPU/ENR/Fe3O4 Blends

AbstractIn this work, multi‐stimuli responsive shape memory‐assisted self‐healing thermoplastic elastomeric materials are designed by incorporating Fe3O4 particles in dynamically vulcanized thermoplastic elastomeric materials based on thermoplastic polyurethane (TPU)/epoxidized natural rubber (ENR) blends. A thermodynamic perspective is employed to predict the potential distribution of Fe3O4 in the developed blends in which Fe3O4 is selectively distributed in the ENR phase. Interestingly, the Fe3O4‐loaded TPV exhibited multi‐stimuli responsive shape memory‐assisted self‐healing functionality, more specifically in the presence of an alternating magnetic field, infrared light, and heat. The resulting dynamically vulcanized TPU/ENR/Fe3O4 blends demonstrated healing efficiencies of ≈53%, ≈78%, and ≈69% under oven heat, under a magnetic field, and under infrared light exposure, respectively. Thermal image mapping is used to understand the stimulus‐dependent healing mechanisms. The developed Fe3O4 filled dynamically vulcanized TPV also shows excellent shape memory properties with a shape recovery ratio in the range of 88%–90% under the aforementioned conditions. The incorporation of Fe3O4 possibly induces coordination interactions with zinc dimethacrylate (ZDMA), used as a coagent during dynamic vulcanization, as evidenced by FTIR and XRD analyses, thereby enhancing the functional properties of the developed TPVs.

Quantitative susceptibility mapping is more sensitive and specific than phase imaging in detecting chronic active multiple sclerosis lesion rims: pathological validation

Abstract Quantitative susceptibility mapping and phase imaging are used to identify multiple sclerosis lesions with paramagnetic rims that slowly expand over time, and are associated with earlier progression to disability, decreased brain volume, and increased frequency of clinical relapse. However, the presence of iron-laden microglia/macrophages at the lesion rim and demyelination within the lesion both contribute to phase and quantitative susceptibility mapping images. Therefore, simultaneous pathological validation is needed to assess accuracies in identifying iron-positive lesions. MRI was performed on fifteen multiple sclerosis brain slabs; thirty-two lesions of interest were processed for myelin, iron, and microglial markers. Three experienced readers classified lesions as rim positive or negative on quantitative susceptibility mapping and phase; these classifications were compared with Perls’ stain as the gold standard. All ten of the quantitative susceptibility mapping-positive lesions had iron-positive rims on histology. Of the sixteen phase-positive lesions, only ten had iron-positive rims on histology. Using Perls’ stain as the ground truth, the positive predictive value was 100% for quantitative susceptibility mapping and 63% for phase; the negative predictive value was 95% for quantitative susceptibility mapping and 94% for phase. Post-mortem imaging results demonstrate that quantitative susceptibility mapping is a more reliable indicator of an iron-positive rim compared to phase imaging.

T1 mapping using MP2RAGE in degenerative cervical myelopathy: a longitudinal study.

Degenerative cervical myelopathy (DCM) is the most common cause of spinal cord (SC) dysfunction. In routine clinical practice, SC changes are well depicted using conventional MRI, especially T2-weighted imaging. However, this modality usually fails to provide satisfactory clinico-radiological correlations. In this context we assessed the potential value of quantitative changes measured with a T1 MP2RAGE sequence. 18 patients diagnosed with chronic onset of DCM and 17 healthy controls (HC) were enrolled in the study. Clinical presentation was assessed using the modified Japanese Orthopaedic Association (mJOA) scale. Sagittal cervical SC T2-w 3D SPACE imaging and T1 MP2RAGE mapping were performed at baseline and 3-months postoperatively. Data were processed using Matlab and the SC Toolbox. mJOA score increased from 13.3 ± 2.1 preoperatively to 14.4 ± 1.9 at follow-up (p = 0.027). Site of maximum compression (Cmax) was at C3-C4 cervical levels in 4 patients, C4-C5 in 8 patients, C5-C6 in 5 patients and C6-C7 in 1 patient. SC compression was multi-level in 7 patients and single-level in 11 patients. T2-w hyperintensity was present in 15 patients. Mean SC T1 values in the whole SC in the DCM group at baseline showed significant difference as compared to mean SC T1 values in HC group (962.2 ± 62 vs. 924.9 ± 34 ms, respectively (p < 0.0001)) but no differences could be observed between baseline and 3-month follow-up (962.4 ± 59 ms (p = 0.86)). Z-scores at baseline were - 0.05 ± 1 for mild, 1.2 ± 1.9 for moderate and 2.5 ± 1.2 for severe. Mean baseline and 3-month follow-up SC T1 values were weakly but significantly correlated to preoperative (R2 = 0.33 (p = 0.013) and postoperative mJOA (R2 = 0.29 (p = 0.024). Baseline T1 value at C2 level was significantly correlated with mJOA at 3-month follow-up (p = 0.048). T1-MP2RAGE mapping in patients with DCM demonstrated both focal and diffuse cervical SC alteration. It could thus be a biomarker for patients with DCM managed surgically.

Epilepsy detection and classification based on the contour maps of brain MR images

Epilepsy detection and classification based on the contour maps of brain MR images

Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.

Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Materials and Methods Male patients with suspected prostate cancer who underwent multiparametric MRI were retrospectively included from three centers from April 2020 to April 2023. A deep learning model (pix2pix algorithm) was trained to synthesize contrast-enhanced MRI scans from four noncontrast MRI sequences (T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps) and then tested on an internal and two external datasets. The reference standard for model training was the second postcontrast phase of the dynamic contrast-enhanced sequence. Similarity between simulated and acquired contrast-enhanced images was evaluated using the multiscale structural similarity index. Three radiologists independently scored T2-weighted and diffusion-weighted MRI with either simulated or acquired contrast-enhanced images using PI-RADS, version 2.1; agreement was assessed with Cohen κ. Results A total of 567 male patients (mean age, 66 years ± 11 [SD]) were divided into a training test set (n = 244), internal test set (n = 104), external test set 1 (n = 143), and external test set 2 (n = 76). Simulated and acquired contrast-enhanced images demonstrated high similarity (multiscale structural similarity index: 0.82, 0.71, and 0.69 for internal test set, external test set 1, and external test set 2, respectively) with excellent reader agreement of PI-RADS scores (Cohen κ, 0.96; 95% CI: 0.94, 0.98). When simulated contrast-enhanced imaging was added to biparametric MRI, 34 of 323 (10.5%) patients were upgraded to PI-RADS 4 from PI-RADS 3. Conclusion It was feasible to generate simulated contrast-enhanced prostate MRI using deep learning. The simulated and acquired contrast-enhanced MRI scans exhibited high similarity and demonstrated excellent agreement in assessing clinically significant prostate cancer based on PI-RADS, version 2.1. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Neji and Goh in this issue.

DFCL: Dual-pathway fusion contrastive learning for blind single-image visible watermark removal.

Digital image watermarking is a prevalent method for image copyright protection. As watermark embedding techniques evolve, research in copyright protection has increasingly extended into watermark removal. Recent advancements in deep learning and generative technologies have led to the development of public watermark removal solutions, addressing issues such as plagiarized, illegal, or outdated watermarks while driving significant improvements in robust watermark embedding. Traditional image restoration often requires the manual selection of watermark mask regions, while common blind visible watermark removal techniques struggle with watermark detection accuracy and post-removal visual quality. To address these challenges, this paper introduces a dual-pathway fusion contrastive learning approach for blind single-image visible watermark removal. We conduct dual-pathway training of the image and gradient map, enhancing high-frequency feature acquisition and the accuracy of watermark spatial positioning through feature fusion. Additionally, contrastive learning ensures that the results closely resemble the original watermark-free images while distancing themselves from watermark content, resulting in improved background visual quality. Importantly, our blind watermark removal algorithm does not require additional watermark images or mask regions. Extensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our approach in overcoming the limitations of existing methods.

SERS-based quantitative vertical flow assay using dual Au-Ag SERS nanoprobes

A surface-enhanced Raman scattering (SERS)-based vertical flow assay (VFA) platform was developed for the point-of-care testing (POCT) to perform multiplex diagnosis and distinguish each disease with similar symptoms (e.g., analysis of respiratory diseases including SARS-CoV-2, influenza and RSV). To evaluate the feasibility of multiplexing performance of SERS-VFA platform, vertical flow cartridges with 2 × 2 test areas on the nitrocellulose membrane and two different Raman reporter-labeled SERS nanoprobes were prepared, to detect samples including two different immunoglobulins (mouse IgG and rabbit IgG). For the preparation of SERS nanoprobes, we made use of the gold-silver/core–shell metal nanostructures for the local electromagnetic-field enhancement effect, and the Raman signal could be improved. SERS mapping images for each IgG in the sample were generated by measuring Raman peak position originated from each individual SERS nanoprobes. As a result, the quantitative analysis of each individual IgG could be performed at once, with a single sample. The detection limit for each rabbit IgG and mouse IgG was calculated as 0.030 μg/mL and 0.18 μg/mL, respectively. This result shows the SERS-based VFA platform could be of significant utility in multiplexed detection of chemical or biological targets.

Enhancing surface water mapping and monthly dynamics monitoring with a stepwise gap-filling method

ABSTRACT Optical satellite imaging for surface water mapping often encounters significant challenges owing to persistent spatial data gaps caused by clouds, shadows, and sensor errors. This study presents a novel Stepwise Gap-Filling (SGF) method, designed to enhance the monthly surface water mapping and monitoring. The SGF method leverages temporal similarities and spatial correlations to reconstruct gap pixels originally classified as invalid observations. We validated this approach against historical high-resolution Google Earth images from 2887 sample points in the Siling Co Basin of the Tibetan Plateau. The results demonstrated substantial improvements in mapping accuracy, achieving an overall accuracy of 98.93%, a producer’ accuracy of 98.59%, and a user’ accuracy of 99.11%, markedly reducing the uncertainties in the original dataset. Importantly, the SGF method offers detailed insights into monthly surface water dynamics, which are closely aligned with annual trends. This study highlights the effectiveness of the SGF method for filling data gaps and its potential for widespread application in the monitoring and management of global water resources.

The ‘Umka’ landslide

ABSTRACT We present an in-depth landslide map of the ‘Umka’ landslide near Belgrade, Serbia, at a scale of 1:5000. The map delineates elements at risk, primarily buildings and road infrastructure impacted by the landslide displacements of several cm per year, introduced during frequent reactivation stages. The Main map results from a survey of over 350 buildings and more than 7 km of state and local roads. The acquisition techniques included engineering geological field mapping, building survey, and visual interpretation of high-resolution UAV images. This is the first case of an integrated approach to mapping elements at risk within an urbanized active landslide in Serbia. Presently, over 490 people continue to reside at the site, facing daily landslide risks. This research and map hold substantial value for local authorities and decision-makers, providing a basis for conducting landslide risk analyses and implementing effective risk mitigation strategies or remedial measures.

Delineating Structural Features Related to Hydrothermal Alterations for Possible Mineralization in Share Area, Kwara State Nigeria Using Aeromagnetic Data

Mineral deposits of significant economic value are abundant in the subsurface of Nigeria, presenting a promising alternative to the nations over dependence on petroleum revenues. This study interprets aeromagnetic data from Share, Kwara State, Nigeria, to delineate subsurface structural features associated with hydrothermal zones, which are key indicators for potential mineralization. The methodologies applied upward continuation, analytic signal, tilt derivative, and first vertical derivative (FVD). These offer insights into subsurface geology that can be broadly applied in geophysical exploration and mineral resource management. The results reveal structural trends predominantly in the NE–SW direction, with some NW–SE alignments, indicative of hydrothermal alterations linked to mineral deposits. The analytical signal map identified amplitude values ranging from 0.004 nT/m to 0.073 nT/m, with low and intermediate magnetic intensities linked to sediment-filled basement rocks and possible limestone and sandstone formations. High-gradient anomalies, 1.280 nT/m to 1.374 nT/m, were attributed to geological contacts, fractures, dykes, and hydrothermal vents. Depth estimates from the source parameter imaging map revealed hydrothermal and structural zones at depths ranging from 287.9 m to 1360.7 m, with deeper sources &gt;1202.1 m indicating tectonic activity and mineralization potential. The FVD and Tilt Derivative maps further highlighted faulted zones, shear structures, and intrusive bodies with intensities between 0.031 nT/m and 0.041 nT/m, suggesting active tectonics. High magnetic anomalies in the central, northeastern, and southeastern regions were identified as prime targets for exploration, indicating magnetite-rich bodies, igneous intrusions, and hydrothermal zones. Integrated exploration strategies combining geophysical, geochemical, and structural data are recommended to refine anomaly delineation, prioritize field validation, and enhance mineralization discovery. These findings establish the Share area as a promising site for regional mineral exploration, supporting Nigeria’s diversification efforts toward sustainable resource development.

Native liver T1 mapping on magnetic resonance imaging for an evaluation of congestive liver injury in children with congenital heart disease.

Fontan-associated liver disease (FALD) may be caused by chronic liver congestion due to high central venous pressure (CVP). Recently, the usefulness of liver native T1 mapping in magnetic resonance imaging (MRI) in adulthood has been reported. To evaluate the usefulness of native liver T1 mapping in children with congenital heart disease (CHD), we investigated the utility of native liver T1 relaxation time (LT1) in pediatric Fontan patients in comparison to other CHDs. Correlations between LT1 and laboratory biomarkers or hemodynamic data were also assessed. A total of 155 patients with CHD (biventricular repair, n = 42; bidirectional Glenn circulation, n = 38; and Fontan circulation, n = 75) underwent blood tests, cardiac catheterization, and cardiac MRI within 48h. Both CVP and LT1 levels were higher in Fontan patients than in bidirectional Glenn and biventricular patients. There were significant correlation in the overall population and weak correlation in Fontan patients between CVP and LT1(correlation coefficient 0.644 [0.541-0.728] and 0.244 [0.0179-0.446], P < 0.001 and 0.035, respectively). Among the laboratory data, the multiple linear regression analysis revealed that the fibrosis-4 index and alanine aminotransferase were significantly correlated with LT1 in the overall population (P = 0.008,0.012), and the fibrosis-4 index was correlated with LT1 in Fontan patients (P = 0.019). LT1 might have some role to predict elevated CVP and liver injury in children with CHD.

Utilizing combined quantitative multiparametric MRI as potential biomarkers for improved early-stage parkinson's disease diagnosis.

Identifying Parkinson's disease (PD) during its initial phases presents considerable hurdles for clinicians. To examine the feasibility and efficacy of a machine learning model based on quantitative multiparametric magnetic resonance imaging (MRI) features in identifying early-stage PD. We recruited 33 participants, including 19 with early-stage PD, 14 with advanced-stage PD and 20 healthy control subjects. Each participant underwent both quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI). We utilized combined QSM and DKI features to establish a support vector machine (SVM) model to identify early-stage PD. When comparing early-stage PD with healthy controls, the SVM model exhibited moderate performance, achieving a training set accuracy of 0.78 and an area under the receiver operating characteristic curve (AUC) of 0.90, and the accuracy of 0.77 (AUC = 0.87) in the test set. When comparing advanced-stage PD with healthy controls, the SVM model exhibited equally high accuracy in both training (0.97, AUC = 0.97) and test (0.94, AUC = 0.94) sets. In discriminating between early-stage PD and advanced-stage PD, the SVM model achieved an accuracy of 0.80 (AUC = 0.81) in the training set and an accuracy of 0.71 (AUC = 0.72) in the test set. The mean kurtosis feature of DKI in the substantia nigra, played a significant role in classification. These findings suggest that early PD is associated with specific MRI features reflecting magnetic susceptibility and microstructural changes. The SVM model combining quantitative QSM and DKI features holds promise for improving early PD diagnosis.

Restoration of Posidonia oceanica Meadow Using Cuttings from an Area Impacted by Harbor Extension Project

In the Mediterranean Sea, restoration of marine habitats has mostly focused on the endemic seagrass Posidonia oceanica. Despite several transplanting experiments, large-scale projects are rare, and their success is poorly known. The present work describes a restoration project of a large, degraded area in northern Sardinia (Italy) using cuttings harvested from a donor meadow that was destined for destruction due to harbor expansion. The receiving site was selected through a multidisciplinary study including acoustic mapping, ROV surveys, sediment assessment, and analyses of satellite images across ten years to evaluate the site suitability. Plants were manually uprooted from the donor meadow and cuttings were selected and transplanted within 24 h by environmental engineering techniques. The cuttings were transplanted onto degradable mats of natural coconut nets coupled with a double-twist steel mesh and anchored to the bottom. Overall, 7000 patches, each containing 20 cuttings, were transplanted in three periods: June–July 2022, October–November 2022, and February–March 2023. One year after the restoration, all the patches were in situ, with an overall cutting survival of 59%. The results are comparable to those of previous small-scale projects using the same technique and also endorse its suitability for the restoration of large, degraded areas.

Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala

Abstract The primate amygdala serves to evaluate emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal, lateral, and central nuclei, respectively. Recent evidence has suggested the mesoscale (millimeter-scale) nature of intra-amygdala functional organization. However, the connectivity patterns by which these mesoscale regions interact with brainwide networks remain unclear. Using Infrared Neural Stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7T functional Magnetic Resonance Imaging (INS-fMRI), we have discovered that these mesoscale sites exert influence over a surprisingly extensive scope of the brain. Our findings strongly indicate that mesoscale sites within the amygdala modulate brainwide networks through a ‘one-to-many’ (integral) way. Meanwhile, these connections exhibit a point-to-point (focal) topography. Our work provides new insights into the functional architecture underlying emotional and social behavioral networks, thereby opening up possibilities for individualized modulation of psychological disorders.

Pyrochlore-Supergroup Minerals and Their Relation to Columbite-Group Minerals in Peralkaline to Subaluminous A-Type Rare-Metal Granites with Special Emphasis on the Madeira Pluton, Amazonas, Brazil

Niobium (Nb) and tantalum (Ta) are quoted as “strategic” or “critical” elements for contemporaneous society. The main sources of Nb and Ta are minerals of the pyrochlore supergroup (PSGM) and the columbite group (CGM) mined from different magmatic lithologies. Textures and chemical compositions of PSGM and CGM often provide key information about the origin of NbTa mineralization. Therefore, we decided to carry out a detailed study of the relations between the PSGM and CGM and their post-magmatic transformations, and the Madeira peralkaline pluton (Brazil) is an ideal object for such a study. Textures of the PSGM and CGM were studied using BSE imaging and SEM mapping, and their chemical compositions were determined using 325 electron microprobe analyses. Pyrochlore from the Madeira granite can be chemically characterized as Na, Ca-poor, U- and Pb-dominant, and Sn- and Zn-enriched; REE are enriched only during alteration. Two stages of alteration are present: (i) introduction of Fe + Mn, with the majority of them consumed by columbitization; (ii) introduction of Si and Fe, and in lesser amounts also Pb and U: Si, Pb, and U incorporated into pyrochlore, iron forming Fe-oxide halos around pyrochlore. During both stages, F and Na decreased. In the case of a (nearly) complete pyrochlore columbitization, U and Th were exsolved to form inclusions of a thorite/coffinite-like phase. In contrast to altered pyrochlores from other localities, pyrochlore from Madeira shows a relatively high occupancy of the A-site. Although Madeira melt was Na-, F-rich, contemporaneous crystallization of cryolite consumed both elements and pyrochlore was, from the beginning, relatively Na-, F-poor.

Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography.

Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal-to-noise ratio (SNR) can be very low. The purpose of this study is to develop a deep learning approach for denoising shear wavefields in ultrasound shear wave elastography. This may lead to improved reconstruction of shear wave phase velocity imagemaps. The study addresses noise by transforming particle velocity data into a time-frequency representation. A neural network with encoder and decoder convolutional blocks effectively decomposes the input and extracts the signal of interest, improving the SNR in high-noise scenarios. The network is trained on simulated phantoms with elasticity values ranging from 3 to 60kPa. A total of 185570 samples with 80%-20 split were used for training and validation. The approach is tested on experimental phantom and ex-vivo goat liver tissue data. Performance was compared with the traditional filtering methods such as bandpass, median, and wavelet filtering. Kruskal-Wallis one-way analysis of variance was performed to check statistical significance. Multiple comparisons were performed using the Mann-Whitney U test and Holm-Bonferroni adjustment of . The results are evaluated using SNR and the percentage of pixels that can be reconstructed in the phase velocity maps. The SNR levels in experimental data improved from -2 to 9.9dB levels to 15.6 to 30.3 dB levels. Kruskal-Wallis one-way analysis showed statistical significance ( ). Multiple comparisons with p-value corrections also showed statistically significant improvement when compared to the bandpass and wavelet filtering scheme ( ). Smoother phase velocity maps were reconstructed after denoising. The coefficient of variation is less than in CIRS phantom and less than in ex-vivo goat livertissue. The proposed approach demonstrates improvement in shear wave phase velocity image map reconstruction and holds promise that deep learning methods can be effectively utilized to extract true shear wave signal from measured noisydata.

Pore-space partitioning in geological porous media using the curvature of the distance map

Media classification and the construction of pore network models from binary images of porous media hinges on accurately characterizing the pore space. We present a new method for (i) locating critical points, that is, pore body and throat centers, and (ii) partitioning of the pore space using information on the curvature of the distance map (DM) of the binary image. Specifically, we use the local maxima and minima of the determinant map of the Hessian matrix of the DM to locate the center of pore bodies and throats. The locating step provides structural information on the pore system, such as pore body and throat size distributions and the mean coordination number. The partitioning step is based on the eigenvalues of the Hessian, rather than the DM, to characterize the pore space using either watershed or medial-axis transforms. This strategy eliminates the common problem of saddle-induced over-partitioning shared by all traditional marker-based watershed methods and represents an alternative method to determine the skeleton of the pore space without the need for morphological reconstruction.