Quantitative Texture Analysis Research Articles

Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

BackgroundPulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.MethodsWe retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.ResultsThe optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.ConclusionsThis study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.Clinical trial numberNot applicable.

Impact of quantitative CT texture analysis on the outcome of CT-guided bone biopsy

Texture analysis can provide new imaging-based biomarkers. Texture analysis derived from computed tomography (CT) might be able to better characterize patients undergoing CT-guided percutaneous bone biopsy. The present study evaluated this and correlated texture features with bioptic outcome in patients undergoing CT-guided bone biopsy. Overall, 123 patients (89 female patients, 72.4 %) were included into the present study. All patients underwent CT-guided percutaneous bone biopsy with an 11 Gauge coaxial needle. Clinical parameters and quantitative imaging features were investigated. Random forest classifier was used to predict a positive biopsy result. Overall, 69 patients had osteolytic metastasis (56.1 %) and 54 had osteoblastic metastasis (43.9 %). The overall positive biopsy rate was 72 %. The developed radiomics model demonstrated a prediction accuracy of a positive biopsy result with an AUC of 0.75 [95 %CI 0.65 – 0.85]. In a subgroup of breast cancer patients, the model achieved an AUC of 0.85 [95 %CI 0.73 – 0.96]. In the subgroup of non-breast cancer patients, the signature achieved an AUC of 0.80 [95 %CI 0.60 – 0.99]. Quantitative CT imaging findings comprised of conventional and texture features can aid to predict the bioptic result of CT-guided bone biopsies. The developed radiomics signature aids in clinical decision-making, and could identify patients at risk for a negative biopsy.

A Step Forward in Quantitative Automated Mineralogy in 2D and 3D

Automated mineralogy is a software addition usually seen on scanning electron microscopes designed to provide rapid insight into sample chemistry and texture in routine petrology workflows. The specific purpose of automated mineralogy is to provide mineral classifications to uniquely identified phases typically using energy dispersive spectroscopy, thus removing laborious and time‐consuming human input for routine tasks. These mineral classifications can then be applied to image data to quantify which mineral is associated with any particular texture. Automated mineralogy systems were primarily designed to generate quantitative textural analysis of particle samples to the mining industry and have remained a critical technique in this setting for the last several decades. Automated mineralogy has become more widely used in academia, and this has changed the focus of the technique, applying it to a broader range of workflows and applications. Here we show petrology examples focussing where combined geochemical and textural analysis are widely used. Critically, the use of quantitative geochemical data means that mineral classifications are based on their quantitatively measured chemistry. By making both the chemical and textural analysis quantitative, automated mineralogy can become highly flexible and provide a unique system for petrologists in both industry and academia.

Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging.

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage.

Differential diagnosis of cemento-osseous dysplasia and periapical cyst using texture analysis of CBCT

BackgroundRadiolucencies found at the root apex in patients with cemento-osseous dysplasia (COD) may be mistaken for periapical cysts (PC) of endodontic origin. The purpose of this study was to examine the utility of quantitative texture analysis using cone-beam computed tomography (CBCT) to differentiate between COD and PC.MethodsPatients who underwent CBCT at Wonkwang University Daejeon Dental Hospital between January 2019 and December 2022 and were diagnosed with COD and PC by clinical, radiologic, and, if necessary, histopathologic examination were included. Twenty-five patients each were retrospectively enrolled in the COD and PC group. All lesions observed on axial CBCT images were manually segmented using the open-access software MaZda version 4.6 to establish the regions of interest, which were then subjected to texture analysis. Among the 279 texture features obtained, 10 texture features with the highest Fisher coefficients were selected. Statistical analysis was performed using the Mann-Whitney U-test, Welch’s t-test, or Student’s t-test. Texture features that showed significant differences were subjected to receiver operating characteristics (ROC) curve analysis to evaluate the differential diagnostic ability of COD and PC.ResultsThe COD group consisted of 22 men and 3 women, while the PC group consisted of 14 men and 11 women, showing a significant difference between the two groups in terms of sex (p=0.003). The 10 selected texture features belonged to the gray level co-occurrence matrix and included the sum of average, sum of entropy, entropy, and difference of entropy. All 10 selected texture features showed statistically significant differences (p<0.05) when comparing patients with COD (n=25) versus those with PC (n=25), osteolytic-stage COD (n=11) versus PC (n=25), and osteolytic-stage COD (n=11) versus cementoblastic-stage COD (n=14). ROC curve analysis to determine the ability to differentiate between COD and PC showed a high area under the curve ranging from 0.96 to 0.98.ConclusionTexture analysis of CBCT images has shown good diagnostic value in the differential diagnosis of COD and PC, which can help prevent unnecessary endodontic treatment, invasive biopsy, or surgical intervention associated with increased risk of infection.

Prediction of neonatal respiratory distress syndrome by Quantitative ultrasound texture analysis of fetal lung Observational cross section study

Prediction of neonatal respiratory distress syndrome by Quantitative ultrasound texture analysis of fetal lung Observational cross section study

Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters.

To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities' persistence were investigated. Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12. The cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12. CALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities' persistence at T12.

Quality Assessment of Selective Laser Melting Components Using Quantitative Ultrasound Measurements and Image Texture Analysis

Quality Assessment of Selective Laser Melting Components Using Quantitative Ultrasound Measurements and Image Texture Analysis

Quantitative textural analysis reveals magma chamber residence time and magma eruption rate of porphyritic basaltic andesite in the Xiong'er Group

Quantitative textural analysis reveals magma chamber residence time and magma eruption rate of porphyritic basaltic andesite in the Xiong'er Group

Preparation of uniform lignin/titanium dioxide nanoparticles by confined assembly: A multifunctional nanofiller for a waterborne polyurethane wood coating

We report a facile synthesis for lignin/titanium dioxide (TiO2) nanoparticles (LT NPs) at room temperature by confining assembly of lignin macromolecules. The LT NPs had a uniform nanosize distribution (average diameter ∼ 68 nm) and were directly employed as multifunctional nanofillers to reinforce a waterborne polyurethane wood coating (WBC). X-ray diffraction, Fourier-transform infrared spectroscopy, and X-ray photoelectron spectroscopy revealed the mechanism by which formed TiO2 confined lignin assembly. The LT NPs considerably increased the tensile strength of a WBC film from 16.3 MPa to 28.1 MPa. The WBC–LT NPs exhibited excellent ultraviolet (UV) A and UVB blocking performances of 87 % and 98 %, respectively, while maintaining 94 % transmittance in the visible region. Incorporating LT NPs into the WBC enhanced the coating performance (the hardness, adhesion, and abrasion resistance) on wood substrates. A quantitative color and texture analysis revealed that the LT NPs increased the decorativeness of actual wooden products. After nearly 1800 h of UV irradiation, wood coated with the WBC–LT NPs exhibited good color stability, where the original color remained unchanged or even became brighter. In this study, value-added valorization of lignin is enabled by using organic–inorganic nanofillers and insights are gained into developing multifunctional WBCs.

Clast shape-fabric analysis: A comprehensive and efficient methodology to measure particle-orientation data in solid and loose volcaniclastic deposits

Fabric analysis is essential for understanding the evolution of volcaniclastic deposits. Here we present a comprehensive and efficient methodology, called “Clast shape-fabric analysis,” which is part of the Quantitative Textural Analysis (QTA). This methodology combines high-resolution image analysis techniques with geospatial data processing tools.The fabric of a deposit refers to the three-dimensional orientation of the particles with respect to space, where the degree of iso-orientation of the major axes of the particles is taken into account. The process begins with the collection of oriented samples in the field. Then, in the laboratory, the samples are processed to obtain high-resolution images. The final stage involves the analysis of these images using the FabricS program, which combines image processing techniques and circular statistics.An application of the method was made at the Joya Honda Maar in Mexico, where shape-fabric analysis was used to identify the emission centers of pyroclastic materials.In summary, the “Clast shape-fabric analysis” is a reliable, low-cost and high-potential methodology that can be applied in several geoscientific disciplines and other areas of scientific research.•New Methodology for shape-fabric analysis is presented.•The methodology involves field work, laboratory work and image analysis.•Identification of particle orientations in volcaniclastic deposits.

Northwest Africa 13669, a reequilibrated nakhlite from a previously unsampled portion of the nakhlite igneous complex

AbstractNorthwest Africa (NWA) 13669 is a recently found nakhlite and here, we use 2‐D and 3‐D mineralogy and texture, quantitative textural analysis of pyroxene, bulk rock and mineral major and trace element compositions, and melt inclusion analyses to assess its formation and emplacement. Using these combined results, we determine that NWA 13669 is derived from a depleted mantle source common to nakhlites and modeled parental melt compositions from olivine‐ and pyroxene‐hosted melt inclusions record similar major element trends to other nakhlites, including an alkali enrichment. Homogeneous pyroxene and olivine and diffusive reequilibration of trace elements in melt inclusions provide evidence for magma storage in a crystal mush within the nakhlite plumbing system and suggest NWA 13669 has undergone extensive reequilibration. Quantitative textural analysis of NWA 13669, including crystal size distribution (CSD) profiles, CSD slope and intercepts, and residence times, are similar to the Yamato nakhlite group, and indicate that NWA 13669 likely experienced similar emplacement conditions. However, differences in bulk rock and mineral compositions suggest NWA 13669 represents a new flow or sill from a previously unsampled portion of the nakhlite igneous complex, further increasing the diversity of the nakhlite suite.

Quantitative Analysis of Texture and Prediction of the R-value in Commercial AA6061 Sheets by EBSD

Quantitative Analysis of Texture and Prediction of the R-value in Commercial AA6061 Sheets by EBSD

CPO and quantitative textural analyses within sheath folds

We investigate the intra- and inter-crystalline deformation processes involved in sheath fold development combining complementary fabric analysis techniques and 3D modelling by neutron tomography. The investigated sheath fold is a multi-layered sub-metre scale single-eye structure, developed in metapsammites from the Ben Hope Nappe, overlying the Moine Thrust Zone of NW Scotland. Crystallographic Preferred Orientations (CPOs) of quartz and biotite were acquired through a Neutron Diffractometer and an SEM-EBSD system to compare the full-fabric of the main phases and the active slip systems for an “in situ” structural control. Combined with orientation maps and grain size maps, results show that, despite the different structural positions of the investigated microdomains (upper vs lower fold limbs, inner vs outer sheath closures, distance from hinge of the sheath fold), quartz and biotite deformed uniformly, suggesting a constant differential stress and orientation of the kinematic vorticity axis. Previously recognized detachment horizons within the sampled sheath fold do not affect the fabric patterns recorded by quartz and biotite. This may be interpreted in two different ways: i) detachments formed during earlier active folding and prior to passive amplification of folds associated with more uniform flow to create the sheath fold geometries; ii) the quartz c-axis patterns are coeval with a late deformation phase (loading of the orogenic wedge) that pervasively obliterated the previous fabric and therefore did not preserve the active folding component. Several pieces of evidence reported here, such as top-to-SE normal-shear sense which is opposite to the regional kinematics, are more supportive of the second hypothesis. The analysis of mineral textures provides an improved dataset for the whole sheath fold and increases our understanding of recrystallization mechanisms active in shear zones.

In-situ study of texture-breakage coupling in a copper ore using X-ray micro-CT

As the mining industry continues to seek greater energy efficiency, it needs more effective characterization of ore material as it progresses from mine to mill. The energy cost of ore mineral liberation, for example, is critically dependent on management of rock breakage from blasting to comminution. The study reported here presents a quantitative method for 3D characterization of ore texture and breakage based on X-ray micro-CT and in-situ micromechanical test. A porphyry copper ore is studied from its initial mechanical failure to the onset of fragmentation. This work integrates a quantitative ore textural analysis with 3D mapping of both the dominant minerals and micro-porosity in the ore sample. It enables us to show, as the fractured ore is still under loading, that internal breakage of feldspar grains dominates grain boundary fracturing. We discuss how the microporous regions, mostly found in hydrothermally altered plagioclase, may act as a weakening factor leading to the dominant breakage of plagioclase. We measure quantitatively and discuss how size reduction, ore texture and breakage characteristics control the degree of liberation of Cu minerals. Combining characterisation of ore texture with breakage in 3D could provide much needed insights for achieving more energy-efficient comminution and optimal liberation of critical minerals.

Noninvasive Evaluation of Breast Tumor Response to Combined Ultrasound-Stimulated Microbubbles and Hyperthermia Therapy Using Quantitative Ultrasound-Based Texture Analysis Method.

Quantitative ultrasound (QUS) is a noninvasive imaging technique that can be used for assessing response to anticancer treatment. In the present study, tumor cell death response to the ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) treatment was monitored in vivo using QUS. Human breast cancer cell lines (MDA-MB-231) were grown in mice and were treated with HT (10, 30, 50, and 60 minutes) alone, or in combination with USMB. Treatment effects were examined using QUS with a center frequency of 25 MHz (bandwidth range: 16 to 32 MHz). Backscattered radiofrequency (RF) data were acquired from tumors subjected to treatment. Ultrasound parameters such as average acoustic concentration (AAC) and average scatterer diameter (ASD), were estimated 24 hours prior and posttreatment. Additionally, texture features: contrast (CON), correlation (COR), energy (ENE), and homogeneity (HOM) were extracted from QUS parametric maps. All estimated parameters were compared with histopathological findings. The findings of our study demonstrated a significant increase in QUS parameters in both treatment conditions: HT alone (starting from 30 minutes of heat exposure) and combined treatment of HT plus USMB finally reaching a maximum at 50 minutes of heat exposure. Increase in AAC for 50 minutes HT alone and USMB +50 minutes was found to be 5.19 ± 0.417% and 5.91 ± 1.11%, respectively, compared to the control group with AAC value of 1.00 ± 0.44%. Furthermore, between the treatment groups, ΔASD-ENE values for USMB +30 minutes HT significantly reduced, depicting 0.00062 ± 0.00096% compared to 30 minutes HT only group, showing 0.0058 ± 0.0013%. Further, results obtained from the histological analysis indicated greater cell death and reduced nucleus size in both HT alone and HT combined with USMB. The texture-based QUS parameters indicated a correlation with microstructural changes obtained from histological data. This work demonstrated the use of QUS to detect HT treatment effects in breast cancer tumors in vivo.

Sintering in seconds, elucidated by millisecond in situ diffraction

Materials, when sintered at high temperatures, undergo structural changes on multiple, hierarchical length scales but getting realtime information on these changes is difficult. To address this challenge, we developed a custom-built sample environment that allows us to investigate the structural evolution of materials during sintering using high-energy two-dimensional synchrotron X-ray diffraction (2D-XRD). Changes in the structure of SrFe12O19 ceramic magnet at multiple length scales were tracked in situ and modelled with millisecond time-resolution. In addition, we also demonstrated the ability to perform quantitative texture analysis from individual 2D-XRD images with a time resolution of 4 ms each. Owing to the high brightness X-ray source and advanced X-ray detectors, the evolution of crystallographic texture could be followed during sintering. This in situ approach can aid understanding of the synthesis–structure–property relationships in sintered materials, enabling the development of improved functional materials.

Computed Tomography-Based Quantitative Texture Analysis and Gut Microbial Community Signatures Predict Survival in Non-Small Cell Lung Cancer.

This study aims to combine computed tomography (CT)-based texture analysis (QTA) and a microbiome-based biomarker signature to predict the overall survival (OS) of immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients by analyzing their CT scans (n = 129) and fecal microbiome (n = 58). One hundred and five continuous CT parameters were obtained, where principal component analysis (PCA) identified seven major components that explained 80% of the data variation. Shotgun metagenomics (MG) and ITS analysis were performed to reveal the abundance of bacterial and fungal species. The relative abundance of Bacteroides dorei and Parabacteroides distasonis was associated with long OS (>6 mo), whereas the bacteria Clostridium perfringens and Enterococcus faecium and the fungal taxa Cortinarius davemallochii, Helotiales, Chaetosphaeriales, and Tremellomycetes were associated with short OS (≤6 mo). Hymenoscyphus immutabilis and Clavulinopsis fusiformis were more abundant in patients with high (≥50%) PD-L1-expressing tumors, whereas Thelephoraceae and Lachnospiraceae bacterium were enriched in patients with ICI-related toxicities. An artificial intelligence (AI) approach based on extreme gradient boosting evaluated the associations between the outcomes and various clinicopathological parameters. AI identified MG signatures for patients with a favorable ICI response and high PD-L1 expression, with 84% and 79% accuracy, respectively. The combination of QTA parameters and MG had a positive predictive value of 90% for both therapeutic response and OS. According to our hypothesis, the QTA parameters and gut microbiome signatures can predict OS, the response to therapy, the PD-L1 expression, and toxicity in NSCLC patients treated with ICI, and a machine learning approach can combine these variables to create a reliable predictive model, as we suggest in this research.

Relation between quantitative descriptive analysis and textural analysis of boiled plantain.

Inadequate consideration of textural quality in conventional breeding pipelines of plantains (from breeders to end-users) results in limited impact. Knowledge of the textural quality characteristics of boiled plantain, as preferred by end-users, could help improve the adoption of new clones when these traits are selected for breeding. The present study aimed to evaluate the relationship between instrumental and sensory texture attributes of boiled plantain genotypes. Consumer testing (Just About Right and Check All That Apply tests), sensory quantitative descriptive analysis (QDA), instrumental texture profile analysis (TPA) and penetrometry were conducted with nine accessions: three landraces and six plantain-like bred hybrids. Landraces were considered just-about-right by more than 45% of people for all the sensory attributes (humidity, sweetness, color and firmness), described by characteristics such as smooth on sight, attractive, mealy, firm, plantain taste and yellow. Color and firmness were the most highly scored attributes by panelists for the landraces. Penetrometry discriminated among genotypes better than TPA. Hardness, gumminess, resilience and chewiness were the most discriminatory attributes for TPA, whereas hardness and area under the curve were the most discriminatory attributes for penetrometry. No correlation was found between penetrometry and sensory texture of boiled plantain. For TPA, negative correlations were found between sensory humidity and hardness, as well as between sensory firmness and resilience, whereas a positive correlation was found between resilience and sensory humidity. Combining QDA and texture measurements can make the selection of plantain hybrids more effective and improve the adoption of new varieties. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Quantitative proteomic analysis of super soft kernel texture in soft white spring wheat.

Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel development. The cultivar 'Alpowa', a soft white spring wheat, was crossed to a closely related super soft spring wheat line 'BC2SS163' to produce F6 recombinant inbred lines (RILs). Four normal soft RILs and four super soft RILs along with the parents were selected for proteomic analysis. Alpowa and the normal soft RILs showed hardness indices of 20 to 30, whereas BC2SS163 and the super soft RILs showed hardness indices of -2 to -6. Kernels were collected from normal soft and super soft genotypes at 7 days post anthesis (dpa), 14 dpa, 28 dpa, and maturity and were subject to quantitative proteomic analysis. Throughout kernel development, 175 differentially abundant proteins (DAPs) were identified. Most DAPs were observed at 7 dpa, 14 dpa, and 28 dpa. Of the 175 DAPs, 32 had higher abundance in normal soft wheat, whereas 143 DAPs had higher abundance in super soft wheat. A total of 18 DAPs were associated with carbohydrate metabolism and five DAPs were associated with lipids. The gene TraesCS4B02G091100.1 on chromosome arm 4BS, which encodes for sucrose-phosphate synthase, was identified as a candidate gene for super soft kernel texture in BC2SS163. This study enhanced our understanding of the mechanism underlying super soft kernel texture in soft white spring wheat.