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This paper presents new findings concerning a hand-held stiffness probe for the medical diagnosis of abnormalities during palpation of soft-tissue. Palpation is recognized by the medical community as an essential and low-cost method to detect and diagnose disease in soft-tissue. However, differences are often subtle and clinicians need to train for many years before they can conduct a reliable diagnosis. The probe presented here fills this gap providing a means to easily obtain stiffness values of soft tissue during a palpation procedure. Our stiffness sensor is equipped with a multi degree of freedom (DoF) Aurora magnetic tracker, allowing us to track and record the 3D position of the probe whilst examining a tissue area, and generate a 3D stiffness map in real-time. The stiffness probe was integrated in a robotic arm and tested in an artificial environment representing a good model of soft tissue organs; the results show that the sensor can accurately measure and map the stiffness of a silicon phantom embedded with areas of varying stiffness.

Purpose Coming up with a quantitative diagnosis method for liver fibrosis using ultrasound would be highly significant. To permit tissue characterization using the characteristics of the echo signal such as power spectrum, texture parameters, local attenuation, and statistical characteristics, the relation between complicated scatterer structures and the echo signal must be understood. Methods In this study, we analyzed the property of the echo amplitude envelope using computer-simulated scatterer models. These models mimicked various liver conditions to evaluate our quantitative parametric imaging methods. Statistical echo characteristics changed with the density of the heterogeneous scatterer buried in a speckle. Results The new analysis method for a medium in which some tissues are embedded was proposed in consideration of analysis results from computer simulations. In the new method, it is possible to eliminate the influence of a cyst or veins and to detect the existence of fibers more clearly than in previous methods.

Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.

To examine changes in proximal patellar tendon structure during the adolescent growth spurt in athletes. Prospective observational study. 173 adolescent athletes aged 11–15 years from specialised sporting programmes were recruited. Data collection occurred biannually for 2.5 years. Chronological age and maturity offset were calculated. Patellar tendons were scanned using ultrasound tissue characterisation, the proximal attachment was classified as having a hypoechoic area absent/present, and structure was quantified into four distinct echotypes (I–IV). Generalised additive models and generalised additive mixed modelling assessed associations between changes in echotype proportions and chronological age or maturity offset. 147 of 173 participants had normal tendon structure (i.e., no hypoechoic area) at baseline and across the study. In these tendons, a stronger linear association with maturation offset, but not chronological age, was observed for all echotypes suggesting a subtle improvement in tendon structure (p ≤ 0.01). Nine tendons (n = 8 participants) were hypoechoic at baseline and had a significant association with maturation offset and aligned tendon structure (echotype I; p ≤ 0.01) but not for disorganised tendon structure (echotypes III, IV; p = 0.6). Another 17 tendons (n = 15 participants) developed a hypoechoic area across the study. Proximal patellar tendon structure undergoes echotype changes during adolescence, which was linearly associated with maturity offset and not chronological age. Areas of disorganised patellar tendon structure in adolescents at baseline appeared unchanged despite continued exposure to high loads. Skeletal maturation appears a critical period in the development of a normal and abnormal, proximal patellar tendon attachment having implications for patellar tendinopathy prevention.

Towards the characterization of viscoelasticity of the soft tissue, which is an important biomarker, this study aims to investigate the effectiveness of the Harmonic Shear Wave Elastography (HSWE) framework by analyzing the frequency-dependent phase velocity maps, using a 3D Finite-Element-based simulation framework. Here, we developed and verified a 3D finite-element framework to accurately model the tissue displacement under a multi-frequency HSWE setting. The HSWE results were compared using both simulation and phantom experiments against those from the Pulsed Shear Wave Elastography (PSWE) method which is widely used in shear wave elastography problems. Particularly, we analyzed the group and frequency-dependent phase velocities, focusing on the frequency range of 300 to 800 Hz. Additionally, we conducted parametric studies to examine the effects of inclusion size, stiffness, and viscosity. The HSWE framework provided accurate measurements of group and phase velocities, comparable to those obtained using the PSWE method. The median differences between HSWE and PSWE results were 5.21 % and 9.14 % for group and phase velocities, respectively, in simulations, and 13.98 % and 22.32 % for group and phase velocities, respectively, in phantom experiments. Parametric studies showed that the HSWE framework is effective in accurately characterizing the location, size, stiffness and viscoelastic properties of tissue inclusions, with notable improvements over PSWE, particularly for smaller inclusions at lower frequencies. Future work will focus on optimizing the HSWE framework for clinical use and developing inverse models to estimate the underlying viscoelastic shear moduli of the tissue to enhance its diagnostic capabilities.

To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4). We implemented an FCNN for MyoMapNet to estimate T1 values from a reduced number of T1-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T1, or a combination of both. We also explored the effects of number of T1-weighted images (four and five) for native T1. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T1 data from 61 patients by discarding the additional T1-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T1 mapping data in 27 subjects with inline T1 map reconstruction by MyoMapNet. The resulting T1 values were compared to MOLLI. MyoMapNet trained using a combination of native and post-contrast T1-weighted images had excellent native and post-contrast T1 accuracy compared to MOLLI. The FCNN model using four T1-weighted images yields similar performance compared to five T1-weighted images, suggesting that four T1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T1 maps on the scanner. Native and post-contrast myocardium T1 by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T1 was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively. A FCNN, trained using MOLLI data, can estimate T1 values from only four T1-weighted images. MyoMapNet enables myocardial T1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.

Accurate and detailed tissue characterization is a central goal in medical diagnostics, often requiring the combination of multiple imaging modalities. This study presents a multimodal imaging system that integrates mid-infrared (MIR) scanning with fluorescence imaging to enhance the chemical specificity and spatial resolution in biological samples. A motorized mirror allows rapid switching between MIR and fluorescence modes, enabling efficient, co-registered data acquisition. The MIR modality captures label-free chemical maps based on molecular vibrations, while the fluorescence channel records endogenous autofluorescence for additional biochemical contrast. Applied to mouse brain tissue, the system enabled the clear differentiation of gray matter and white matter, supported by the clustering analysis of spectral features. The addition of autofluorescence imaging further improved anatomical segmentation and revealed fine structural details. In mouse skin, the approach allowed the precise mapping of the layered tissue architecture. These results demonstrate that combining MIR scanning and fluorescence imaging provides complementary, label-free insights into tissue morphology and chemistry. The findings support the utility of this approach as a powerful tool for biomedical research and diagnostic applications, offering a more comprehensive understanding of tissue composition without relying on staining or external markers.

Non-invasive quantification of the extracellular volume (ECV) is a method for the evaluation of focal and diffuse myocardial fibrosis, potentially obviating the need for invasive endomyocardial biopsy. While ECV quantification with cardiac magnetic resonance imaging (ECVMRI) is already an established method, ECV quantification with CT (ECVCT) is an attractive alternative to ECVMRI, similarly using the properties of extracellular contrast media for ECV calculation. In contrast to ECVMRI, ECVCT provides a more widely available, cheaper and faster tool for ECV quantification and allows for ECV calculation also in patients with contraindications for MRI. Many studies have already shown a high correlation between ECVCT and ECVMRI and accumulating evidence suggests a prognostic value of ECVCT quantification in various cardiovascular diseases. Adding a late enhancement scan (for dual energy acquisitions) or a non-enhanced and late enhancement scan (for single-energy acquisitions) to a conventional coronary CT angiography scan improves risk stratification, requiring only minor adaptations of the contrast media and data acquisition protocols and adding only little radiation dose to the entire scan.Critical relevance statementThis article summarizes the technical principles of myocardial extracellular volume (ECV) quantification with CT, reviews the literature comparing ECVCT with ECVMRI and histopathology, and reviews the prognostic value of myocardial ECV quantification for various cardiovascular disease.Key points• Non-invasive quantification of myocardial fibrosis can be performed with CT.• Myocardial ECV quantification with CT is an alternative in patients non-eligible for MRI.• Myocardial ECV quantification with CT strongly correlates with ECV quantification using MRI.• Myocardial ECV quantification provides incremental prognostic information for various pathologies affecting the heart (e.g., cardiac amyloidosis).

Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert’s opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated.

Medical techniques for scanning the brain enable imaging of its structure or function of the brain. There is growing interest in using quantum light for tissue scanning. A precise investigation of this quantum mechanical phenomenon can lead to new medical diagnostics and brain imaging techniques. In this study, we employed quantum photon pair’s, created by spontaneous parametric down-conversion. The technique involved preparing pairs of photons in maximally-entangled Bell states in the polarization degree of freedom. One part of the entangled photons was focused on rat brain tissue. The state of the photons passing through the brain was measured. We compared the preservation of the photon pairs’ quantum correlation in polarization via Bell parameter measurement of the transmitted entangled photons. Our results show a distinct difference in the entanglement preservation among different regions of the rat brain. The cortex tissue meaningfully preserved the photons’ correlation to a high degree despite the scattering effect , while the inner part, like the amygdala, degraded the entanglement and the Bell parameter declined to 1.31. The gradual decrease of the Bell parameter, indicating the decoherence of entangled photons in the tissue, can serve as a proper criterion to describe the characteristics of biological media. This study can be a major step toward a modern imaging method and brain mapping in the future.