Conventional Imaging Research Articles

Carbon fibre detection in polymer composites reinforced by chopped carbon fibres through digital image processing and machine learning

Mechanical and thermodynamical properties and thus machinability of carbon fibre reinforced polymer composites significantly depend on the fibre orientation relative to the load direction. However, the orientations of the fibre groups in polymer composites reinforced by chopped carbon fibres are stochastic; therefore, the properties and machinability of such composites are challenging to plan, predict and optimise. We developed four different and novel approaches for fibre detection in polymer composites reinforced by chopped carbon fibres: (i) detecting the fibres through naked eye supported manual drawing, (ii) digital image processing of optical images, (iii) machine learning-based fibre detection, and (iv) rectangle fitting on the outputs of the automated processes using the Chaudhuri and Samal method. The applicability of the novel approaches was tested through optically captured images of polymer composites reinforced by chopped carbon fibres. The developed methods are each capable of detecting fibre groups at the top and bottom of the composite plate with certain limitations. The rectangle fitting approaches performed the best from the point of view of correctly identifying of fibre groups, followed by the machine learning-based and the conventional digital image processed, respectively. As a result of this study, the machining process planning and condition monitoring of polymer composites reinforced by chopped carbon fibres is more deeply supported.

Research on deep learning restoration algorithm of X-ray backscatter imaging based on virtual training dataset

The X-ray lobster eye lens, an innovative technique for focusing high-energy radiation, enables wide-field X-ray imaging. However, its inherent cross point spread function introduces noise and degradation into the resultant images. Conventional image restoration methods are inadequate for suppressing such noise. This paper introduces a backscatter image restoration technique utilizing a virtual training dataset. By convolving the point spread function (PSF) with an object to simulate the image degradation process, the method generates a multitude of convolved images for deep learning training, eliminating the need for manual annotation. Given the high structural similarity between the synthetic convolved images and actual backscatter images, the trained model effectively restores real backscatter images. The restoration process yields a structural similarity index (SSIM) of 0.86 and a mean intersection over union (MIoU) of 0.83 when compared to the reference images. This approach mitigates the limitations of sparse real backscatter datasets, substantially reducing image acquisition time, decreasing radiation flux, and enhancing system safety.

Fast quantitative MRI: Spiral Acquisition Matching-Based Algorithm (SAMBA) for Robust T1 and T2 Mapping

Conventional diagnostic images from Magnetic Resonance Imaging (MRI) are typically qualitative and require subjective interpretation. Alternatively, quantitative MRI (qMRI) methods have become more prevalent in recent years with multiple clinical and preclinical imaging applications. Quantitative MRI studies on preclinical MRI scanners are being used to objectively assess tissues and pathologies in animal models and to evaluate new molecular MRI contrast agents. Low-field preclinical MRI scanners (≤3.0T) are particularly important in terms of evaluating these new MRI contrast agents at human MRI field strengths. Unfortunately, these low-field preclinical qMRI methods are challenged by long acquisition times, intrinsically low MRI signal levels, and susceptibility to motion artifacts. In this study, we present a new rapid qMRI method for a preclinical 3.0T MRI scanner that combines a Spiral Acquisition with a Matching-Based Algorithm (SAMBA) to rapidly and quantitatively evaluate MRI contrast agents. In this initial development, we compared SAMBA with gold-standard Spin Echo MRI methods using Least Squares Fitting (SELSF) in vitro phantoms and demonstrated shorter scan times without compromising measurement accuracy or repeatability. These initial results will pave the way for future in vivo qMRI studies using state-of-the-art chemical probes.

Calcium-Based Imaging of the Spine at Dual-Layer CT and Evaluation of Vertebral Fractures in Multiple Myeloma.

To evaluate the prediction of vertebral fractures in plasma cell dyscrasias using dual-layer CT (DLCT) with quantitative assessment of conventional CT image data (CI), calcium suppressed image data (CaSupp), and calculation of virtual calcium-only (VCa) image data. Patients (n = 81) with the diagnosis of a plasma cell dyscrasia and whole-body DLCT at the time of diagnosis and follow-up were retrospectively enrolled. CI, CaSupp25, and CaSupp100 were quantitatively analyzed using regions of interest in the lumbar vertebral bodies and fractured vertebral bodies on baseline or follow-up imaging. VCa were calculated by subtraction (CaSupp100-CaSupp25), delineating bone only. Logistic regression analyses were performed to assess the possibility of imminent spine fractures. In 24 patients, new vertebral fractures were observed in the follow-up imaging. The possibility of new vertebral fractures was significant for baseline assessment of CT numbers in CI, CaSupp25, and VCa (p = 0.01, respectively), with a higher risk for new fractures in the case of lower CT numbers in CI (Odds ratio = [0.969; 0.994]) and VCa (Odds ratio = [0.978; 0.995]) and in the case of higher CT numbers in CaSupp 25 (Odds ratio 1.015 [1.006; 1.026]). Direct model comparisons implied that CT numbers in CaSupp 25 and VCa might show better fracture prediction than those in CI (R2 = 0.18 both vs. 0.15; AICc = 91.95, 91.79 vs. 93.62), suggesting cut-off values for CI at 103 HU (sensitivity: 54.2%; specificity: 82.5; AUC: 0.69), for VCa at 129 HU (sensitivity: 41.7%; specificity: 94.7; AUC: 0.72). Quantitative assessment with CaSupp and calculation of VCa is feasible to predict the vertebral fracture risk in MM patients. DLCT may prove useful in detecting imminent fractures.

Age-appropriate or delayed myelination? Scoring myelination in routine clinical MRI

BackgroundAssessment of myelination is a core issue in paediatric neuroimaging and can be challenging, particularly in settings without dedicated paediatric neuroradiologists. Deep learning models have recently been shown to be able to estimate myelination age in children with normal MRI, but currently lack validation for patients with myelination delay and implementation including pre-processing suitable for local imaging is not trivial. Standardized myelination scores, which have been successfully used as biomarkers for myelination in hypomyelinating diseases, rely on visual, semiquantitative scoring of myelination on routine clinical MRI and may offer an easy-to-use alternative for assessment of myelination. MethodsMyelination was scored in 13 anatomic sites (items) on conventional T2w and T1w images in controls (n = 253, 0–2 years). Items for the score were selected based on inter-rater variability, practicability of scoring, and importance for correctly identifying validation scans. ResultsThe resulting myelination score consisting of 7 T2- and 5 T1-items delineated myelination from term-equivalent to advanced, incomplete myelination which 50 % and 99 % of controls had reached by 19.1 and 32.7 months, respectively. It correctly identified 20/20 new control MRIs and 40/43 with myelination delay, missing one patient with borderline myelination delay at 8.6 months and 2 patients with incomplete T2-myelination of subcortical temporopolar white matter at 28 and 34 months. ConclusionsThe proposed myelination score provides an easy to use, standardized, and versatile tool to delineate myelination normally occurring during the first 1.5 years of life.

MM_Fast_RCNN_ResNet: Construction of Multimodal Faster RCNN Inception and ResNet V2 for Pedestrian Tracking and detection

Pedestrian identification and tracking is a crucial duty in smart building monitoring. The development of sensors has led to architects' focus on smart building design. The image distortions caused by numerous external environmental factors present a significant problem for pedestrian recognition in smart buildings. It is difficult for machine learning algorithms and other conventional filter-based image classification methods, such as histograms of oriented gradient filters, to function efficiently when dealing with many input photos of pedestrians. Deep learning algorithms are now performing substantially better when processing an enormous amount of image data. This article evaluates a novel multimodal classifier-based pedestrian identification method. The proposed method is Multimodal Faster RCNN Inception and ResNet V2 (MM Fast RCNN ResNet). The collected attributes address a tracking problem and establish the foundation for several object recognition tasks (novelty). Our method's neural network is regularized, and the feature representation is automatically adjusted to the detection assignment, resulting in high accuracy (superior to the proposed method). The proposed method is assessed using the PenFudan dataset and contemporary techniques regarding several factors. It is discovered that the recommended MM Fast RCNN ResNet obtains precision, recall, FPPI, FPPW, and average precision of 0.9057, 0.8629, 0.0898, and 0.0943.

The added value of a new high-performance ring-gantry CBCT imaging system for prostate cancer patients

Background and purposeA novel Cone-Beam Computed Tomography (CBCT) named HyperSight provides superior CBCT image quality compared to conventional ring gantry CBCT imaging, and it is suitable for dose calculations for prostate cancer, but it comes with considerable additional costs. The aim of this study was to determine the added value of HyperSight CBCT imaging compared to conventional CBCT imaging in terms of organ visibility in the male pelvic region. Materials and methodsTwenty prostate cancer patients were included in this prospective clinical study. For each patient three CBCT pairs, consisting of HyperSight and conventional CBCT scans acquired on consecutive days, were included. CBCT scans were evaluated by four observers in terms of visibility of the prostate, bladder, rectum and seminal vesicles. Visibility was scored on a 1-to-5 scale and by annotating axial slices where the organs were hard to delineate. Lastly, observers indicated whether the CBCT scans were of sufficient quality for an online adaptive radiation therapy workflow. ResultsAll four organs were better visible on HyperSight CBCT scans compared to conventional CBCT scans. The mean visibility scores increased from 3.1 to 4.5 on a 1––5 scale of and the mean number of annotated slices reduced from 4.5 to 1.1. 99% Of the HyperSight CBCT scans were considered suitable for an online adaptive workflow vs 25–83% for the conventional CBCT scans. ConclusionHyperSight CBCT scans yielded a visibility of prostate, bladder, rectum and seminal vesicles comparable to planning CT scans and, can replace a repeat planning CT scan in case of anatomical changes requiring a new treatment plan.

Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: breast tissue composition changes and collagen discriminative potential.

The purpose of this clinical study is to test a broad spectral range (635-1060 nm) time-domain diffuse optical spectroscopy in monitoring the response of breast cancer patients to neoadjuvant chemotherapy (NAC). The broadband operation allows us to fully analyze tissue composition in terms of hemoglobin, water, lipids and collagen concentration, which has never been systematically studied until now during the course of therapy. Patients are subjected to multiple breast optical imaging sessions, each one performed at different stages of NAC, both on tumor-bearing and contralateral healthy breasts. We correlate the optical results with conventional imaging techniques and pathological response. Preliminary outcomes on 10 patients' data show an average significant reduction in the concentrations of oxy-hemoglobin (-53%, p = 0.0020), collagen (-36%, p = 0.0039) and water (-15%, p = 0.0195), and increase in lipids (+39%, p = 0.0137) from baseline to the end of therapy in the tumor-bearing breast of patients who responded to therapy at least partially. With respect to scattering, the scattering amplitude, a, increases slightly (+15%, p = 0.0039) by the end of the therapy compared to the baseline, while the scattering slope, b, shows no significant change (+4%, p = 0.9219). Some change in the constituents' concentrations was also noticed in the contralateral healthy breast, even though it was significant only for oxy-hemoglobin concentration. We observed that collagen seems to be the only component distinguishing between complete and partial responders by the end of 2-3 weeks from the baseline. In the complete responder group, collagen significantly decreased after 2-3 weeks with respect to baseline (p = 0.0423). While the partial responder group also showed a decrease, it did not reach statistical significance (p = 0.1012). This suggests that collagen could serve as a potential biomarker to measure NAC effectiveness early during treatment. Even though obtained on a small group of patients, these initial results are consistent with those of standard medical modalities and highlight the sensitivity of the technique to changes that occur in breast composition during NAC.

Puerarin Affects 1HMR Spectroscopy Quantified Hepatic Fat Signal Fraction in Intrauterine Growth Restricted Rats.

This study aimed to investigate the impact of puerarin early intervention on growth parameters and Hepatic Fat Signal Fraction (HFF) quantification in Intrauterine Growth Restricted(IUGR)rats through Proton Magnetic resonance spectroscopy (1H-MRS). Pregnant rats were divided into three groups: control, IUGR with puerarin treatment, and IUGR without treatment. The treatment and nontreatment groups were received a low-protein diet during pregnancy, while the control group received a normal diet. After birth, pups in the treatment group received a unilateral intraperitoneal injection of 50 mg/kg/d puerarin. Male rats were evaluated at 3,8 and 12 weeks, including measurements of weight, body length and waist circumference and body mass index (BMI). Conventional magnetic resonance imaging and 1HMRS were conducted using a 3.0 T whole-body MR scanner. Newborn pups in the treatment and non-treatment groups showed significantly lower body weight, BMI, and body length at 3 weeks compared to the control group. However, there were no significant differences in HFF and waist circumference between the three groups at 3 weeks. At 8 and 12 weeks post-delivery, significant differences in body weight, BMI, waist circumference were observed in newborn pups of IUGR non-treatment rats compared to the control group. In contrast, there were no significant differences in body weight, BMI, waist circumference between the treatment group and the control group at 8 and 12 weeks. Moreover, the treatment group exhibited notably higher HFF compared to the control group at both time points. At 12 weeks post-birth, a significant difference in HFF was observed between the IUGR non-treatment and treatment groups, although no significant difference was found at 8 weeks. Early intervention with puerarin following birth has a significant impact on liver fat content and may potentially reduce adult obesity among IUGR rats.

Ultrashort Echo Time and Fast Field Echo Imaging for Spine Bone Imaging with Application in Spondylolysis Evaluation

Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like images. Lumbar spines of asymptomatic and low back pain (LBP) subjects were imaged at 3-Tesla with multi-echo ultrashort echo time (UTE) and field echo (FE) sequences followed by simple post-processing of averaging and inverting to depict spinal bones with a CT-like appearance. The contrast-to-noise ratio (CNR) for bone was determined to compare UTE vs. FE and single-echo vs. multi-echo data. Visually, both sequences depicted cortical bone with good contrast; UTE-processed sequences provided a flatter contrast for soft tissues that made them easy to distinguish from bone, while FE-processed images had better resolution and bone–muscle contrast, which are important for fracture detection. Additionally, multi-echo images provided significantly (p = 0.03) greater CNR compared with single-echo images. Using these techniques, progressive spondylolysis was detected in an LBP subject. This study demonstrates the feasibility of using spine bone MRI to yield CT-like contrast. Through the employment of multi-echo UTE and FE sequences combined with simple processing, we observe sufficient enhancements in image quality and contrast to detect pars fractures.

Feasibility of dose calculation for treatment plans using electron density maps from a novel dual-layer detector spectral CT simulator

BackgroundConventional single-energy CT can only provide a raw estimation of electron density (ED) for dose calculation by developing a calibration curve that simply maps the HU values to ED values through their correlations. Spectral CT, also known as dual-energy CT (DECT) or multi-energy CT, can generate a series of quantitative maps, such as ED maps. Using spectral CT for radiotherapy simulations can directly acquire ED information without developing specific calibration curves. The purpose of this study is to assess the feasibility of utilizing electron density (ED) maps generated by a novel dual-layer detector spectral CT simulator for dose calculation in radiotherapy treatment plans.Methods30 patients from head&neck, chest, and pelvic treatment sites were selected retrospectively, and all of them underwent spectral CT simulation. Treatment plans based on conventional CT images were transplanted to ED maps with the same structure set, including planning target volume (PTV) and organs at risk (OARs), and the dose distributions were then recalculated. The differences in dose and volume histogram (DVH) parameters of the PTV and OARs between the two types of plans were analyzed and compared. Besides, gamma analysis between these plans was performed by using MEPHYSTO Navigator software.ResultsIn terms of PTV, the homogeneity index (HI), gradient index (GI), D2%, D98%, and Dmean showed no significant difference between conventional plans and ED plans. For OARs, statistically significant differences were observed in parotids D50%, brainstem in head&neck plans, spinal cord in chest plans and rectum D50% in pelvic plans, whereas the variance remained minor. For the rest, the DVH parameters exhibited no significant difference between conventional plans and ED plans. All of the mean gamma passing rates (GPRs) of gamma analysis were higher than 90%.ConclusionCompared to conventional treatment plans relying on CT images, plans utilizing ED maps demonstrated similar dosimetric quality. However, the latter approach enables direct utilization in dose calculation without the requirements of establishing and selecting a specific Hounsfield unit (HU) to ED calibration curve, providing an advantage in clinical applications.

Axial spondyloarthritis—current aspects

Axial spondyloarthritis (axSpA) is a frequent inflammatory rheumatic disease mainly affecting the axial skeleton causing inflammatory back pain. If chronic inflammation persists new bone formation may occur possibly leading to irreversible spinal stiffness. The disease has a strong genetic background with HLA-B27 as the major factor. For diagnostic purposes, imaging is of critical importance—especially conventional radiography and magnetic resonance imaging (MRI). While the former has advantages in the detection of bony changes such as the syndesmophytes, MRI is used to detect axial inflammation but also erosions in the sacroiliac joint. Treatment follows the treat-to-target strategy starting with non-steroidal anti-inflammatory drugs (NSAIDs) in the first line, and later, if high disease activity persists, therapy with biologic disease modifying anti-rheumatic drugs (bDMARDs) is according to international recommendations indicated. For the treatment of axSpA, important targets such as tumor necrosis factor alpha (TNFα) and interleukin (IL)-17 have been identified, and several of their inhibitors (i) including some biosimilars for the former have been approved. Recently, also inhibition of Janus kinases was shown to be efficacious. There is evidence that long term inhibition of inflammation with TNFi can reduce bone formation.

An efficient ultrasonic wavenumber-domain plane wave imaging method towards the inspection of curved structures

Ultrasonic phased array testing is commonly employed for inspecting curved structures. Conventional plane wave imaging techniques, based on delay-and-sum in the time-domain, offer high image quality and inspection accuracy but suffer from low frame rates due to their high computational complexity. In this work, an efficient wavenumber-domain imaging method that combines non-stationary wavefield extrapolation and f-k migration is proposed for curved structure inspection. Special emission focal laws are designed to generate a sequence of steered plane waves through the curved interface. The raw data is then extrapolated to the top boundary of the region of interest, followed by f-k migration to reconstruct images with high time efficiency. Simulation and experimental evaluations demonstrate a time reduction by a factor of up to 32.24 compared to conventional time-domain plane wave image reconstruction with equivalent image quality, highlighting its potential for monitoring flaws in real-time.

Photoacoustic imaging for three-dimensional bioprinted constructs

Bioimaging is used to inspect the successful growth and functional differentiation of cells in printed biomaterials, which are ultimately finalized into functional artificial tissues capable of replacing native tissues. While optical bioimaging techniques are commonly utilized, the current trend in three-dimensional (3D) bioprinting towards replicating complex 3D microarchitectures poses a challenge for conventional optical imaging techniques in providing clear cross-sectional images due to the opaque nature of tissue. Consequently, these limitations necessitate lengthy and destructive preparation processes, which are associated with sacrificing cell viability and damaging the bioprinted material. Photoacoustic imaging (PAI) is a versatile imaging technique that extends the advantages of the optical bioimaging technique to undiscovered depths enabled by its acoustic hybridity, making itself a promising tool for non-destructive imaging of 3D bioprinted constructs. In this review, we introduce the flexible spectral contrasts provided by PAI, which are potentially applicable to 3D-bioprinted constructs, and summarize bioprinting studies that functionally implement PAI for in vitro and in vivo assessments. Finally, we provide an outlook on practical considerations for the more complete integration of these two fields, anticipating more fruitful discoveries as bioprinting advances towards more complex hierarchies.

Speckle tracking phase-contrast computed tomography at an inverse Compton X-ray source

Speckle-based X-ray imaging (SBI) is a phase-contrast method developed at and for highly coherent X-ray sources, such as synchrotrons, to increase the contrast of weakly absorbing objects. Consequently, it complements the conventional attenuation-based X-ray imaging. Meanwhile, attempts to establish SBI at less coherent laboratory sources have been performed, ranging from liquid metal-jet X-ray sources to microfocus X-ray tubes. However, their lack of coherence results in interference fringes not being resolved. Therefore, algorithms were developed which neglect the interference effects. Here, we demonstrate phase-contrast computed tomography employing SBI in a laboratory-setting with an inverse Compton X-ray source. In this context, we investigate and compare also the performance of the at synchrotron conventionally used phase-retrieval algorithms for SBI, unified modulated pattern analysis (UMPA) with a phase-retrieval method developed for low coherence systems (LCS). We successfully retrieve a full computed tomography in a phantom as well as in biological specimens, such as larvae of the greater wax moth (Galleria mellonella), a model system for studies of pathogens and infections. In this context, we additionally demonstrate quantitative phase-contrast computed tomography using SBI at a low coherent set-up.

Sex and mental health are related to subcortical brain microstructure

Some mental health problems such as depression and anxiety are more common in females, while others such as autism and attention deficit/hyperactivity (AD/H) are more common in males. However, the neurobiological origins of these sex differences are poorly understood. Animal studies have shown substantial sex differences in neuronal and glial cell structure, while human brain imaging studies have shown only small differences, which largely reflect overall body and brain size. Advanced diffusion MRI techniques can be used to examine intracellular, extracellular, and free water signal contributions and provide unique insights into microscopic cellular structure. However, the extent to which sex differences exist in these metrics of subcortical gray matter structures implicated in psychiatric disorders is not known. Here, we show large sex-related differences in microstructure in subcortical regions, including the hippocampus, thalamus, and nucleus accumbens in a large sample of young adults. Unlike conventional T1-weighted structural imaging, large sex differences remained after adjustment for age and brain volume. Further, diffusion metrics in the thalamus and amygdala were associated with depression, anxiety, AD/H, and antisocial personality problems. Diffusion MRI may provide mechanistic insights into the origin of sex differences in behavior and mental health over the life course and help to bridge the gap between findings from experimental, epidemiological, and clinical mental health research.

Conventional magnetic resonance imaging key features for distinguishing pathologically confirmed corticobasal degeneration from its mimics: a retrospective analysis of the J-VAC study.

Due to the indistinguishable clinical features of corticobasal syndrome (CBS), the antemortem differentiation between corticobasal degeneration (CBD) and its mimics remains challenging. However, the utility of conventional magnetic resonance imaging (MRI) for the diagnosis of CBD has not been sufficiently evaluated. This study aimed to investigate the diagnostic performance of conventional MRI findings in differentiating pathologically confirmed CBD from its mimics. Semiquantitative visual rating scales were employed to assess the degree and distribution of atrophy and asymmetry on conventional T1-weighted and T2-weighted images. Additionally, subcortical white matter hyperintensity (SWMH) on fluid-attenuated inversion recovery images were visually evaluated. In addition to 19 patients with CBD, 16 with CBD mimics (progressive supranuclear palsy (PSP): 9, Alzheimer's disease (AD): 4, dementia with Lewy bodies (DLB): 1, frontotemporal lobar degeneration with TAR DNA-binding protein of 43kDa(FTLD-TDP): 1, and globular glial tauopathy (GGT): 1) were investigated. Compared with the CBD group, the PSP-CBS subgroup showed severe midbrain atrophy without SWMH. The non-PSP-CBS subgroup, comprising patients with AD, DLB, FTLD-TDP, and GGT, showed severe temporal atrophy with widespread asymmetry, especially in the temporal lobes. In addition to over half of the patients with CBD, two with FTLD-TDP and GGT showed SWMH, respectively. This study elucidates the distinct structural changes between the CBD and its mimics based on visual rating scales. The evaluation of atrophic distribution and SWMH may serve as imaging biomarkers of conventional MRI for detecting background pathologies.

Split-Aperture 2-in-1 Computational Cameras

While conventional cameras offer versatility for applications ranging from amateur photography to autonomous driving, computational cameras allow for domain-specific adaption. Cameras with co-designed optics and image processing algorithms enable high-dynamic-range image recovery, depth estimation, and hyperspectral imaging through optically encoding scene information that is otherwise undetected by conventional cameras. However, this optical encoding creates a challenging inverse reconstruction problem for conventional image recovery, and often lowers the overall photographic quality. Thus computational cameras with domain-specific optics have only been adopted in a few specialized applications where the captured information cannot be acquired in other ways. In this work, we investigate a method that combines two optical systems into one to tackle this challenge. We split the aperture of a conventional camera into two halves: one which applies an application-specific modulation to the incident light via a diffractive optical element to produce a coded image capture, and one which applies no modulation to produce a conventional image capture. Co-designing the phase modulation of the split aperture with a dual-pixel sensor allows us to simultaneously capture these coded and uncoded images without increasing physical or computational footprint. With an uncoded conventional image alongside the optically coded image in hand, we investigate image reconstruction methods that are conditioned on the conventional image, making it possible to eliminate artifacts and compute costs that existing methods struggle with. We assess the proposed method with 2-in-1 cameras for optical high-dynamic-range reconstruction, monocular depth estimation, and hyperspectral imaging, comparing favorably to all tested methods in all applications.

Machine learning-assisted wood materials: Applications and future prospects

Wood and wood-based materials, surpassing their conventional image as mere stems and branches of trees, have found extensive utilization in diverse industrial sectors due to their low carbon footprint. Nonetheless, maximizing wood utilization and advancing multifunctional wood materials face challenges due to resource-intensive conventional approaches. Integrating machine learning (ML) in wood mechanics has emerged as a promising avenue for deeper exploration of this remarkable material. By leveraging advanced computational techniques, researchers can delve into wood's intricate properties and behavior, unraveling the complex interactions between its chemical constituents, microstructures, and mechanical characteristics. Combined with imaging and sensor technologies, ML contributes to efficient, fast, and real-time health detection of wood materials. This review aims to illuminate the transformative impact of ML in unlocking the hidden potential of wood, fostering innovative applications, and facilitating sustainable engineering solutions. The basic workflow of ML and its typical applications in property prediction, defect detection, and optimized design of wood materials are discussed, thereby highlighting the challenges and the need for future research.

Improvement of Breast Cancer Detection Using Dual-Layer Spectral CT.

This study aimed to investigate the diagnostic performance of breast mass detection on monoenergetic image data at 40 keV (MonoE40) and on iodine maps (IM) compared with conventional image data (CI). In this prospective single-center case-control study, 50 breast cancer patients were examined using contrast-enhanced dual-layer spectral CT. For qualitative and quantitative comparison of MonoE40 and IM with CI image data, four blinded, independent readers assessed 300 randomized single slices (two slices for each imaging type per case) with or without cancerous lesions for the presence of a breast mass. Detection sensitivity and specificity were calculated and readers rated their subjective diagnostic certainty. For statistical analysis of sensitivity and specificity, a paired t-test and ANOVA were used (significance level p = 0.05). A total of 50 female patients (median age 51 years, range 28-83 years) participated. IM had the highest overall scores in sensitivity and specificity for breast cancer detection, with 0.97 ± 0.06 and 0.95 ± 0.07, respectively, compared with 0.90 ± 0.04 and 0.92 ± 0.06 in CI. MonoE40 yielded a sensitivity of 0.96 ± 0.02 and specificity of 0.94 ± 0.08. All differences in sensitivity and specificity between MonoE or IM and CI were statistically significant (p < 0.001). The superiority of IM sensitivity and specificity was most pronounced in patients with dense breasts. Spectral CT improved the detection of breast cancer with higher sensitivity and specificity compared to conventional image data in our study.