Radon Transform Research Articles

Rapid Micro-Motion Feature Extraction of Multiple Space Targets Based on Improved IRT

Micro-motion feature extraction is of great significance for target recognition. However, traditional methods mostly focus on single target and struggle to correctly separate the severely overlapping micro-motion curves of multiple targets. In this paper, a rapid micro-motion feature extraction algorithm of multiple space targets based on inverse radon transform (IRT) with a modified model is proposed. First, the high-resolution range profile (HRRP) generated from echo is subject to binarization to improve the unstable estimation caused by noise. Then, the micro-motion period in a complicated multi-target scenario is obtained by a period estimation method based on the autocorrelation coefficients of binarized HRRP. To further improve the extraction accuracy, the IRT model of the micro-range curve is modified from the sine function to second-order sine function. By searching for the remaining unknown parameters in the model in conjunction with the period, the precise micro-range curves are quickly separated. Each time the curves of a target are extracted, they are removed, and the next extraction is carried out until all the targets have been searched. Finally, simulation and experimental results indicate that the proposed algorithm can not only correctly separate the micro-motion feature curves of multiple space targets under low signal-to-noise ratio (SNR) conditions but also significantly outperforms the original IRT in terms of extraction speed.

Effect of adaptive statistical iterative reconstruction-V algorithm and deep learning image reconstruction algorithm on image quality and emphysema quantification in COPD patients under ultra-low-dose conditions.

To explore the effect of different reconstruction algorithms (ASIR-V and DLIR) on image quality and emphysema quantification in chronic obstructive pulmonary disease (COPD) patients under ultra-low-dose scanning conditions. This prospective study with patient consent included 62 COPD patients. Patients were examined by pulmonary function test (PFT), standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with filtered-back-projection (FBP), while ULDCT images were reconstructed using FBP, 30%ASIR-V, 60%ASIR-V, 90%ASIR-V, low-strength (DLIR-L), medium-strength (DLIR-M) and high-strength DLIR (DLIR-H) to form 8 image sets. Images were analyzed using a commercial computer aided diagnosis (CAD) software. Parameters such as image noise, lung volume (LV), emphysema index (EI), mean lung density (MLD), 15th percentile of lung density (PD15) were measured. Two radiologists evaluated tracheal and pulmonary artery image quality using a 5-point scale. Measurements were compared and the correlation between EI and PFT indices was analyzed. ULDCT used 0.46 ± 0.22mSv in radiation dose, 93.8% lower than SDCT (P < 0.001). There was no difference in LV and MLD among image groups (P > 0.05). ULDCT-ASIR-V90% and ULDCT-DLIR-M had similar image noise and EI and PD15 values to SDCT-FBP, and ULDCT-DLIR-M and ULDCT-DLIR-H had similar subjective scores to SDCT-FBP (all P > 0.05). ULDCT-DLIR-M provided the best correlation between EI and the FEV1/FVC and FEV1% indices in PFT, and the lowest deviations with SDCT-FBP in both EI and PD15. DLIR-M provides the best image quality and emphysema quantification for COPD patients in ULDCT. Ultra-low-dose CT scanning combined with DLIR-M reconstruction is comparable to standard dose images for quantitative analysis of emphysema and image quality.

A low-dose CT reconstruction method using sub-pixel anisotropic diffusion.

We present a new low-dose CT reconstruction method using sub-pixel and anisotropic diffusion. The sub-pixel intensity values and their second-order differences were obtained using linear interpolation techniques, and the new gradient information was then embedded into an anisotropic diffusion process, which was introduced into a penalty-weighted least squares model to reduce the noise in low-dose CT projection data. The high-quality CT image was finally reconstructed using the classical filtered back-projection (FBP) algorithm from the estimated data. In the Shepp-Logan phantom experiments, the structural similarity (SSIM) index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 28.13%, 5.49%, and 0.91%, the feature similarity (FSIM) index was increased by 21.08%, 1.78%, and 1.36%, and the root mean square error (RMSE) was reduced by 69.59%, 18.96%, and 3.90%, respectively. In the digital XCAT phantom experiments, the SSIM index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 14.24%, 1.43% and 7.89%, the FSIM index was increased by 9.61%, 1.78% and 5.66%, and the RMSE was reduced by 26.88%, 9.41% and 18.39%, respectively. In clinical experiments, the SSIM index of the image reconstructed using the proposed algorithm was increased by 19.24%, 15.63% and 3.68%, the FSIM index was increased by 4.30%, 2.92% and 0.43%, and the RMSE was reduced by 44.60%, 36.84% and 15.22% in comparison with FBP, PWLS-Gibbs and PWLS-TV algorithms, respectively. The proposed method can effectively reduce the noises and artifacts while maintaining the structural details in low-dose CT images.

Design and Implementation of Sports Network Teaching Platform Based on Discrete Similarity Method

Sport is the most important topic in education, which individually shows motor skills in health activities. Athletic training should focus graduates on sports, low-involvement task activities, and physical fitness or exercise. Teaching concepts and methodologies are innovative, coaching methods and procedures, evaluation of coaching sessions in sports-all this is accompanied by development in the sports hall and successful improvement of sports performance. Here, he provides extraordinary assistance to students by predicting early school leaving and improving the potential application of wireless platforms in sports programs and changing the nature of sports, including visualization and repetition, and incorporating it into sports training. A sports network learning platform based on the discrete similarity approach is proposed for design and implementation in this paper. First, data is collected from a real-time data set. After that, the data submitted for preprocessing removes noise and imperfect records, which can be removed using a global constant or a most likely learning method using an Adaptive Savitsky-Golay Filtering (ASGF) method. The preprocessing method is fed into the feature extraction utilizing Multi‐Hypothesis Fuzzy‐Matching Radon Transform (MHFMRT). MHFMRT extract statistical features such us kurtosis, variance, energy, mean and standard deviation. Then, the Similarity-Based Convolutional Neural Network (SBCNN) method is optimized by the Chaotic Satin Bower Bird Optimization (CSBBO) algorithm, which effectively classifies the visualization and a satisfactory training platform of the sports network. The proposed system is executed in the MATrix LABoratory (MATLAB) platform and the performance of the proposed methods attains 28.5%, 27.9% and 29.5% higher accuracy, 28%, 21% and 26.5% higher precision, 21.2%, 28.75% and 21.3% higher recall compared with the existing methods like Recurrent Neural model to analyze the effect of Physical Training and Treatment in Relation to Sports Injuries (PTT-RSI-RNN), recognizing Sports Activities from Video Frames using Deformable Convolution and Adaptive Multiscale Features (SAVF-DCN) and Sports Match Prediction model for training and Exercise using attention-based LSTM network (SMPE-LSTM) respectively

Impact of Deep Learning-Based Image Reconstruction on Tumor Visibility and Diagnostic Confidence in Computed Tomography.

Deep learning image reconstruction (DLIR) has shown potential to enhance computed tomography (CT) image quality, but its impact on tumor visibility and adoption among radiologists with varying experience levels remains unclear. This study compared the performance of two deep learning-based image reconstruction methods, DLIR and Pixelshine, an adaptive statistical iterative reconstruction-volume (ASIR-V) method, and filtered back projection (FBP) across 33 contrast-enhanced CT staging examinations, evaluated by 20-24 radiologists. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for tumor and surrounding organ tissues across DLIR (Low, Medium, High), Pixelshine (Soft, Ultrasoft), ASIR-V (30-100%), and FBP. In two blinded surveys, radiologists ranked eight reconstructions and assessed four using a 5-point Likert scale in arterial and portal venous phases. DLIR consistently outperformed other methods in SNR, CNR, image quality, image interpretation, structural differentiability and diagnostic certainty. Pixelshine performed comparably only to ASIR-V 50%. No significant differences were observed between junior and senior radiologists. In conclusion, DLIR-based techniques have the potential to establish a new benchmark in clinical CT imaging, offering superior image quality for tumor staging, enhanced diagnostic capabilities, and seamless integration into existing workflows without requiring an extensive learning curve.

Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose

Background Computed tomography angiography (CTA) provides significant information on image quality in vascular imaging, thus offering high-resolution images despite having the disadvantages of increased radiation doses and contrast agent-related side effects. The deep-learning image reconstruction strategies were used to quantitatively evaluate the enhanced contrast-to-noise ratio (CNR) and the dose reduction effect of subtracted images. Objective This study aimed to elucidate a comprehensive understanding of the quantitative image quality features of the conventional filtered back projection (FBP) and the advanced intelligent clear-IQ engine (AiCE), a deep learning reconstruction technique. The comparison was made in subtracted images with variable concentrations of contrast agents at variable tube currents and voltages, enhancing our knowledge of these two techniques. Methods Data were obtained using a state-of-the-art 320-detector CT scanner. Image reconstruction was performed using FBP and AiCE with various intensities. The image quality evaluation was based on eight iodine concentrations in the phantom setup. The efficiency of AiCE relative to FBP was assessed by computing parameters including the root mean square error (RMSE), dose-dependent CNR, and potential dose reduction. Results The results showed that elevated concentrations of iodine and increased tube currents improved AiCE performance regarding CNR enhancement compared to FBP. AiCE also demonstrated a potential dose reduction ranging from 13.7 to 81.9% compared to FBP, suggesting a significant reduction in radiation exposure while maintaining image quality. Conclusions The employment of deep learning image reconstruction with AiCE presented a significant improvement in CNR and potential dose reduction in CT angiography. This study highlights the potential of AiCE to improve vascular image quality and decrease radiation exposure risk, thereby improving diagnostic precision and patient care in vascular imaging practices.

Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.

We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT). CT images of a 16 cm dosimetry phantom, a head phantom, and the brains of 11 patients were reconstructed using filtered backprojection (FBP) and various levels of DLR and HIR. The slice thickness was 5, 2.5, 1.25, and 0.625 mm. Phantom imaging was also conducted at various tube currents. The noise reduction ratio was calculated using FBP as the reference. For patient imaging, overall image quality was visually compared between DLR and HIR images that exhibited similar noise reduction ratios. The noise reduction ratio increased with increasing levels of DLR and HIR in phantom and patient imaging. For DLR, noise reduction was more pronounced with decreasing slice thickness, while such thickness dependence was less evident for HIR. Although the noise reduction effects of DLR were similar between the head phantom and patients, they differed for the dosimetry phantom. Variations between imaging objects were small for HIR. The noise reduction ratio was low at low tube currents for the dosimetry phantom using DLR; otherwise, the influence of the tube current was small. In terms of visual image quality, DLR outperformed HIR in 1.25 mm thick images but not in thicker images. The degree of noise reduction using DLR depends on the slice thickness, tube current, and imaging object in addition to the level of DLR, which should be considered in the clinical use of DLR. DLR may be particularly beneficial for thin-slice imaging.

Fabrication of an In-house Phantom for Evaluation of Metal Artifact Reduction in Computed Tomography

Purpose: This study aims to fabricate an in-house phantom for evaluating effectiveness of a metal artifact reduction (MAR) algorithm in computed tomography (CT) images. Methods: The in-house phantom was made from polyester-resin (PESR) with methyl ethyl ketone peroxide (MEKP) as a catalyst. The dimension of the phantom was 160 mm in diameter and 50 mm in length with seven insert holes ranging from 10 to 20 mm. The titanium representing a metal implant and calcium carbonate representing the bone were inserted into the holes of the phantom. The phantom was scanned using a GE Revolution Apex CT scanner, slice thickness of 1.25 mm, and tube current of 200 and 370 mA. Images were reconstructed using filtered back projection (FBP) and MAR algorithm for reducing metal artifact. Artifact evaluation was characterized by changing in CT number and image noise. Results: Titanium material increases metal artifact in the in-house phantom. Metal artifact from titanium decreases CT number around 5% and increases noise level around 60%. MAR implementation can reduce metal artifacts, which is indicated by an increase in the CT number around 0.7% and a decrease in noise level around 30%. Conclusion: The in-house phantom for evaluating MAR algorithm was successfully developed. The presence of titanium increases metal artifacts, which is characterized by a decrease in the CT number and an increase in noise level in the area around the titanium. It is found that the MAR succeeded in reducing metal artifacts.

Simulation on Image Reconstruction of Concrete with Rebars by Filtered Back Projection Algorithm using PHITS and MATLAB Program

This paper describes simulation on image reconstruction of concrete with rebars by filtered back projection algorithm using PHITS and MATLAB program. In PHITS simulation, a source of Cs-137 and NaI (Tl) detector were used. Concrete block with one rebar was carried out with PHITS simulation. The sample of another concrete block with two rebars with different diameters was also conducted. Linear attenuation coefficient values of the concrete were calculated. These phantoms were rotated with 2.8125 degree intervals over 0 to 180° in PHITS simulation. The objects were scanned with the parallel beam method and the projection data were obtained. The data was built into the image using filtered back projection (FBP) algorithm. The ‘spline’ interpolation and ‘Hamming’ filter functions were chosen for the clearest image in image reconstruction. The results indicated that the reconstructed images of the concrete containing one rebar and two rebars can be observed clearly in MATLAB.

An experimental and computational evaluation of a novel Timepix3 detector for Compton imaging in nuclear medicine

In this paper, we investigate the use of a MiniPIX (Timepix3) hybrid pixel detector as a single layer Compton camera for low-energy (< 200 keV) photons for Nuclear Medicine imaging applications. Literature to date has focused on using Compton camera imaging for high energy photons emitted from non-clinically relevant radionuclides. This paper aims to investigate Compton imaging for nuclear medicine specific radionuclides such as Tc99m (140 keV). For this, a Timepix3 chip with a 1 mm CdTe sensor has been fully characterised in terms of efficiency, energy and timing resolution using a range of point sources, and a detailed model of the detector developed using the EGSnrc Monte Carlo toolkit. Knowledge of the specific interaction points within the detector in the simulation allowed a prediction to be made of the performance of the detector as a Compton camera. Comparison with experimental measurements showed good agreement with simulations, providing validation of the methods employed for image reconstruction. Simple Back Projection (SBP), Filtered Back Projection (FBP) and List Mode-Maximum Likelihood Expectation Maximisation (LM-MLEM) techniques were used for image reconstruction. For all experimental acquisitions, measured Full Width Half Maximum (FWHM) values were within 6% of the modelled values, with Doppler broadening identified as the main limiting factor of the spatial resolution of the system. LM-MLEM consistently returned the highest spatial resolution images, with a 7.3 ± 0.5 mm FWHM achieved for 99mTc point sources, consistent with current clinical gamma cameras. Our results demonstrate the potential of the Timepix3 as a single layer CdTe Compton camera for clinical applications using low energy gamma emitters. The developed MC model will provide a means of investigating larger area, single layer CdTe Compton cameras for nuclear medicine imaging.

Improvement of image quality of dentomaxillofacial region in ultra-high resolution computed tomography: A phantom study.

The purpose of this study was to compare the image quality of ultra-high-resolution computed tomography (U-HRCT) with that of conventional multi-detector row CT (convCT) and demonstrate its usefulness in the dentomaxillofacial region. Phantoms were helically scanned with U-HRCT and convCT scanners using clinical protocols. In U-HRCT, phantoms were scanned in super-high-resolution (SHR) mode, and hybrid iterative reconstruction (HIR) and filtered-back projection (FBP) techniques were performed using a bone kernel (FC81). The FBP technique was performed using the same kernel as in convCT (reference). Two observers independently evaluated the 54 resulting images using a 5-point scale (5: excellent diagnostic image quality; 4: above average; 3: average; 2: subdiagnostic; and 1: unacceptable). The system performance function (SPF) was calculated for a comprehensive evaluation of the image quality using the task transfer function and noise power spectrum. Statistical analysis using the Kruskal-Wallis test was performed to compare the image quality among the three protocols. The observers assigned higher scores to images acquired with the SHRHIR and SHRFBP protocols than to those acquired with the reference (p < 0.0001 and p < 0.0001, respectively). The relative SPF value at 1.0 cycles/mm in SHRHIR and SHRFBP compared to the reference protocol were 151.5 and 45.6%, respectively. Through phantom experiments, this study demonstrated that U-HRCT can provide superior-quality images compared to conventional CT in the dentomaxillofacial region. The development of a better image reconstruction method is required to improve image quality and optimise the radiation dose.

Research on CT System Image based on Back Projection Reconstruction Model

CT technology has been widely used in clinical medicine, industrial engineering and other fields in contemporary society. Since the traditional back projection reconstruction algorithm will introduce star artifacts, we decide to use a parallel beam filtered back projection reconstruction model based on Radon changes and R-L filters. Since the rotation center of the system does not coincide with the geometric center, the data will be missing when using Radon transformation, so we carry out "edging" processing on the square tray. After processing the data to remove the gain (where the gain coefficient is 2.0033) and removing the "edge" at the end of the filtering, we reconstructed the detected object and obtained the absorption rates of the ten points required by the problem as follows :0.0126, 2.2902, 5.9159, 0.0163, 0.0823, 3.1336, 6.0333, 0.0000, 7.7184, 0.0861.

Adaptive Weighted Bregman Huber Total Variation Method for Terahertz Computed Tomography

ABSTRACTTo improve the image quality of terahertz (THz) continuous wave (CW) computed tomography (CT) under sparse‐view projections, a sparse‐view adaptive‐weighted Bregman Huber total variation (AW‐BHTV) method is proposed and experimentally evaluated at 0.11 THz. The Huber function is served as the fidelity term and TV function is worked for the regularization term, optimized by the adaptive weights, incorporating with iterative algorithms containing the modified simultaneous algebraic reconstruction technique (MSART) and the iterative filtered back‐projection (FBP). Sparse‐view reconstruction experiments are carefully implemented via polystyrene (PS) foam samples: Sample 1 and Sample 2, respectively. Under 20 projection angles by MSART based AW‐BHTV, the values of rooted mean‐square‐error (RMSE), peak signal‐to‐noise ratio (PSNR), structure similarity (SSIM) and feature similarity (FSIM) are 0.0061, 22.1427, 0.8738 and 0.9902 for Sample 1 together with 0.0051, 22.9101, 0.8376 and 0.9885 for Sample 2 separately. All of the results above suggest that the proposed AW‐BHTV method can effectively protect image details and strengthen image quality in sparse‐view THz CW CT.

Analytic sparse-view CBCT reconstruction using a novel sinogram restoration method based on higher-order Fourier harmonic peaks

This study presents an analytic sparse-view cone-beam computed tomography (CBCT) reconstruction method using a novel sinogram restoration technique based on higher-order Fourier harmonic peaks to obtain CBCT images of reasonable quality with a reduced radiation dose. The proposed method involves four main steps: (1) acquiring sparse-view (P144) sinogram from a CBCT system, (2) upsampling (P720) the original sparse-view sinogram with equally spaced zero padding, (3) separating higher-order Fourier harmonic peaks of the upsampled sinogram using a band-pass filter, and (4) restoring the sinogram of the higher-order harmonic peaks by taking its magnitude, followed by computationally cost-efficient filtered-backprojection (FBP)-based CBCT reconstruction and denoising processes. The notation P144 indicates that the number of projections is 144. To verify the efficacy of the proposed method, an experiment was performed on a chest phantom using a prototype CBCT system comprising an X-ray tube (90 kV p and 40 mA), a large-area flat-panel detector (388 μm pixel size), and a rotational gantry. The quality of the restored sparse-view CBCT images was quantitatively evaluated in terms of the image intensity profile, universal quality index (UQI), and peak signal-to-noise ratio (PSNR). The results indicate that the proposed sinogram restoration method effectively recovered zero-padded areas in the original upsampled sinogram with image intensities nearly identical to those of the reference dense-view (P720) sinogram, which demonstrates the efficacy of the proposed sinogram restoration method. The quality of the sparse-view CBCT image restored with higher-order harmonic peaks was close to that of the reference dense-view CBCT image, maintaining the image quality. The UQI and PSNR values of the restored sparse-view CBCT image were 0.93 and 34.84, respectively, which are approximately 1.3 and 1.2 times greater than those of the original sparse-view CBCT image, respectively, improving the image quality. Consequently, the proposed method has the potential to resolve the issues caused by sparse-view sampling in FBP-based CBCT.

Eliminating metal artifacts in dental computed tomography using an elaborate sinogram normalization interpolation method with CNR-based metal segmentation

Metal artifact reduction (MAR) in dental cone-beam computed tomography (CBCT) is critical for improving its clinical usefulness and remains a challenging problem. This study presents an elaborate sinogram normalization interpolation (SNI) method with contrast-to-noise ratio (CNR)-based metal segmentation to eliminate metal artifacts in dental CBCT. The proposed MAR method involves three main steps: (1) CNR-based segmentation of a metal trace in the sinogram domain; (2) generation of a residual artifact-reduced prior sinogram; and (3) sinogram completion using the prior sinogram, followed by filtered-backprojection (FBP)-based CBCT reconstruction and metal insertion processes. To verify the efficacy of the proposed method, simulations and experiments were performed using numerical and physical dental phantoms with several metal inserts. The quality of the resulting CBCT images was quantitatively evaluated using the structural similarity (SSIM) metric and compared to those obtained with other interpolation-based MAR methods. A tabletop setup for the micro-CT system was used in the experiment, which comprised an X-ray tube operated under tube conditions of 70 kV p and 5 mA and a flat-panel detector with a pixel size of 198 μm. Our results indicate that the CNR-based metal segmentation method precisely identified traces of metallic objects on the sinogram, and thereby, the proposed SNI-based MAR method considerably reduced metal artifacts in dental CBCT images without introducing any contrast anomalies. The SSIM values of the CBCT images obtained with the proposed MAR method were 0.99 and 0.95 for the simulation and experiment, respectively, which are approximately 11% and 9% greater than those with a simple threshold-based linear interpolation method, respectively, improving the image quality. The proposed MAR method can be applied to reduce metal artifacts in real-world dental CBCT systems.

Regularized maximum likelihood estimation for the random coefficients model

. The random coefficients model Y i = β 0 i + β 1 i X 1 i + β 2 i X 2 i + … + β d i X d i , with 𝐗 i , Y i , 𝜷 i i.i.d, and 𝜷 i independent of 𝐗 i is often used to capture unobserved heterogeneity in a population. We propose a quasi-maximum likelihood method to estimate the joint density distribution of the random coefficient model. This method implicitly involves the inversion of the Radon transformation in order to reconstruct the joint distribution, and hence is an inverse problem. To add stability to the solution, we apply Tikhonov-type regularization methods. Nonparametric estimation for the joint density of β i = ( β 0 i , … , β d i ) based on kernel methods or Fourier inversion have been proposed in recent years. Most of these methods assume a heavy tailed design density f 𝐗 . We analyze the convergence of the quasi maximum likelihood method without assuming heavy tails for f 𝐗 and illustrate performance by applying the method on simulated and real data.

A Non-Stochastic Special Model of Risk Based on Radon Transform.

The concept of risk is fundamental in various scientific fields, including physics, biology and engineering, and is crucial for the study of complex systems, especially financial markets. In our research, we introduce a novel risk model that has a natural transactional-financial interpretation. In our approach, the risk of holding a financial instrument is related to the measure of the possibility of its loss. In this context, a financial instrument is riskier the more opportunities there are to dispose of it, i.e., to sell it. We present a model of risk understood in this way, introducing, in particular, the concept of financial time and a financial frame of reference, which allows for associating risk with the subjective perception of the observer. The presented approach does not rely on statistical assumptions and is based on the transactional interpretation of models. To measure risk, we propose using the Radon transform. The financial concept of risk is closely related to the concepts of uncertainty, entropy, information, and error in physics. Therefore, the well-established algorithmic aspects of the computed tomography method can be effectively applied to the broader field of uncertainty analysis, which is one of the foundational elements of experimental physics.

Evaluating the performance of automatic wave speed estimators of natural shear waves to assess myocardial stiffness

Abstract Introduction Cardiac Shear Wave Elastography is an emerging non-invasive technique using ultrafast echocardiographic imaging. Natural shear waves (SW) occur after mitral valve closure (MVC) and aortic valve closure (AVC), and their propagation velocities are directly related to myocardial stiffness. However, the current assessment of SW velocities lacks a gold standard method, and relies on manual measurements with their inherent variability. Purpose To explore new automated methods for shear wave velocity estimates, aiming to achieve greater accuracy, less variability, and high feasibility. Methods To visualize shear waves, anatomical M-modes were drawn (4±1 cm) along ventricular septum in parasternal long axis views (PLAX), acquired with high-frame-rate echocardiography (1055±159 Hz). M-modes were colour coded for tissue acceleration. SWs appear as tilted green bands after MVC &amp; AVC, the slope of which represents the SW propagation velocity. One hundred-twenty different slopes from healthy volunteers (n=67) and patients (n=53) were measured manually and with 9 different automatic methods. All automatic methods were initiated by indicating the approximate position of the shear wave in the M-mode with a mouse click of the user. Measurements were repeated three times to assess measurement reproducibility. (figure 1, A). In order to determine the influence of display settings, three different acceleration colour scales were tested (0.5, 1, 2 m/s²). (Figure 1, B). Failure of correctly identifying shear waves was counted to assess feasibility. Due to the lack of a gold standard, manual measurements of an experienced reader served as reference. Results Three of nine automatic methods: Radon Transformation (RT), Regression Segmentation Maximum value (RM), Regression Segmentation Weighted Maximum (RMW), revealed the least variability with repeated measurements (0.0±0.0 m/s for all) and colour scales (0.0±0.0, 0.1±0.2 and 0.1±0.2 m/s, resp.). Those three methods showed moderate to good agreement (r=0.66, ICC 0.77 [CI 0.64- 0.85]; r= 0.67, ICC 0.75 [CI 0.50 -0.86]; and r= 0.77, ICC 0.85 [CI 0.73-0.90], resp.) with manual measurements (r=0.96, ICC 0.98 [CI 0.97- 0.99] (figure 2). Conclusion RT, RM, and RMW showed a good reproducibility while RMW showed the best agreement with manual measurements. Our data suggest, that the RMW method could offer an objective alternative for SW velocity estimations. Further research is needed to explore their applicability in clinical scenarios.

Quantitative structural organization of the sclera in chicks after deprivation myopia measured with second harmonic generation microscopy.

Visual deprivation causes enhanced eye growth and the development of myopia, which is associated with a change in the arrangement of collagen fibers within the sclera. A second harmonic generation (SHG) microscope has been used to image the collagen fibers of unstained scleral punches from the posterior part of chicken eyes. We aimed to analyze the fibrous scleral tissue and quantify the changes in collagen organization in relation to the extent of induced deprivation myopia. The scleral architecture was assessed with the Radon transform (RT) through the parameter called structural dispersion (SD) that provides an objective tool to quantify the level of organization of the collagen network. We found that final refraction and axial length changes were linearly correlated. However, no significant differences in scleral thickness were found for different amounts of induced myopia. In contrast, a significant correlation between SD and refraction was demonstrated, ranging from a non-organized (in the control sclerae) to a quasi-aligned distribution (with a dominant direction of the fibers, in the sclera of myopic chicks). These findings demonstrate a remodeling process of the scleral collagen associated with myopia progression that can be measured accurately combining SHG imaging microscopy and RT algorithms.

Comparison of preclinical PET scanners performance with varying axial fields-of-view

In preclinical Positron Emission Tomography (PET) imaging, understanding a PET system's performance is crucial for comparing its results with other systems’. In multicentre studies, the performance of the participating systems is essential when planning the experiments [1,2]. In this study, a comprehensive performance evaluation of three PET systems was carried out. The Mediso PET systems were installed in 2023, 2022, and 2014. The newest system is a three-ring system, with 150 mm axial field-of-view (FOV), while the others are two-ring systems. The reconstruction software has undergone multiple updates over time for each system. Other hardware specifications and the crystal matrix are consistent across the three systems. The NEMA NU-4 2008 assessments [3], and linearity measurements were performed. The resolution was similar across the three systems, with filtered-back-projection (FBP) and with iterative reconstruction at the centre of the FOV. The absolute sensitivity within the 250-750 keV range was highest in the newest 3-ring system, while it was similar with the other 2-ring scanners. The mouse 250-750 keV NEC peak was considerably higher with the newest system. Image quality results are presented in Figure 1. The linearity of all systems was within acceptable limits in the 0.5-120 MBq range. All results of the newest system are preliminary, before publication. The greatest differences arose from the extended FOV of the newest system. The impact of software advancements and system ageing is evident in our results, particularly in the image quality phantom. Although the systems are similar, their performances vary significantly. When designing a study involving these systems, factors such as ageing, software version, and the length of the FOV must be considered. Please click on the 'PDF' for the full abstract!