Radon Transform Research Articles

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.

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.

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!

Super-resolution deep learning image reconstruction: image quality and myocardial homogeneity in coronary computed tomography angiography

BackgroundThe recently introduced super-resolution (SR) deep learning image reconstruction (DLR) is potentially effective in reducing noise level and enhancing the spatial resolution. We aimed to investigate whether SR-DLR has advantages in the overall image quality and intensity homogeneity on coronary computed tomography (CT) angiography with four different approaches: filtered-back projection (FBP), hybrid iterative reconstruction (IR), DLR, and SR-DLR.MethodsSixty-three patients (mean age, 61 ± 11 years; range, 18–81 years; 40 men) who had undergone coronary CT angiography between June and October 2022 were retrospectively included. Image noise, signal to noise ratio, and contrast to noise ratio were quantified in both proximal and distal segments of the major coronary arteries. The left ventricle myocardium contrast homogeneity was analyzed. Two independent reviewers scored overall image quality, image noise, image sharpness, and myocardial homogeneity.ResultsImage noise in Hounsfield units (HU) was significantly lower (P < 0.001) for the SR-DLR (11.2 ± 2.0 HU) compared to those associated with other image reconstruction methods including FBP (30.5 ± 10.5 HU), hybrid IR (20.0 ± 5.4 HU), and DLR (14.2 ± 2.5 HU) in both proximal and distal segments. SR-DLR significantly improved signal to noise ratio and contrast to noise ratio in both the proximal and distal segments of the major coronary arteries. No significant difference was observed in the myocardial CT attenuation with SR-DLR among different segments of the left ventricle myocardium (P = 0.345). Conversely, FBP and hybrid IR resulted in inhomogeneous myocardial CT attenuation (P < 0.001). Two reviewers graded subjective image quality with SR-DLR higher than other image reconstruction techniques (P < 0.001).ConclusionsSR-DLR improved image quality, demonstrated clearer delineation of distal segments of coronary arteries, and was seemingly accurate for quantifying CT attenuation in the myocardium.

Radiation dose reduction and image quality in pediatric paranasal sinus CT: with automatic tube current modulation and iterative reconstruction technique.

Our objective is to evaluate radiation dose and image quality in pediatric paranasal sinus computed tomography (CT) with automatic tube current modulation (ATCM) and sinogram-affirmed iterative reconstruction algorithm (SAFIRE). CT scans from 80 patients were divided into two groups: Group A [80 kVp, pitch 1.5, 40 mAs, the filtered back projection (FBP) algorithm] and Group B (70 kVp, pitch 3, ATCM with reference at 40 mAs, SAFIRE strengths 1-5). We have evaluated image quality and radiation dose. Group B demonstrated significantly lower volume computed tomography dose index, dose-length product, and effective dose than Group A (0.13±0.03 vs. 1.57±0.01mGy, 2.27±0.82 vs. 19.88±2.01mGy·cm, and 0.0081±0.0017 vs. 0.079±0.013mSv, respectively; P<.001). Increasing SAFIRE strengths correlated with noise reduction and SNR enhancement. Group B's noise and SNRsoft at SAFIRE strength 5 were comparable with Group A. Images reconstructed with SAFIRE strength 5 in Group B exhibit comparable image quality with FBP in Group A.

Development of a proton CT imaging system using scintillator-based range detection.

The accuracy of proton therapy and preclinical proton irradiation experiments is susceptible to proton range uncertainties, which partly stem from the inaccurate conversion between CT numbers and relative stopping power (RSP). Proton computed tomography (PCT) can reduce these uncertainties by directly acquiring RSP maps. This study aims to develop a novel PCT imaging system based on scintillator-based proton range detection for accurate RSP reconstruction. The proposed PCT system consists of a pencil-beam brass collimator with a 1mm aperture, an object stage capable of translation and 360° rotation, a plastic scintillator for dose-to-light conversion, and a complementary metal oxide semiconductor (CMOS) camera for light distribution acquisition. A calibration procedure based on Monte Carlo (MC) simulation was implemented to convert the obtained light ranges into water equivalent ranges. The water equivalent path lengths (WEPLs) of the imaged object were determined by calculating the differences in proton ranges obtained with and without the object in the beam path. To validate the WEPL calculation, measurements of WEPLs for eight tissue-equivalent inserts were conducted. PCT imaging was performed on a custom-designed phantom and a mouse, utilizing both 60 and 360 projections. The filtered back projection (FBP) algorithm was employed to reconstruct the RSP from WEPLs. Image quality was assessed based on the reconstructed RSP maps and compared to reference and simulation-based reconstructions. The differences between the calibrated and reference ranges of 110-150 MeV proton beams were within 0.18mm. The WEPLs of eight tissue-equivalent inserts were measured with accuracies better than 1%. Phantom experiments exhibited good agreement with reference and simulation-based reconstructions, demonstrating average RSP errors of 1.26%, 1.38%, and 0.38% for images reconstructed with 60 projections, 60 projections after penalized weighted least-squares algorithm denoising, and 360 projections, respectively. Mouse experiments provided clear observations of mouse contours and major tissue types. MC simulation estimated an imaging dose of 3.44 cGy for decent RSP reconstruction. The proposed PCT imaging system enables RSP map acquisition with high accuracy and has the potential to improve dose calculation accuracy in proton therapy and preclinical proton irradiation experiments.

Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography

This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstructed by deep learning reconstruction (DLR) and filtered back projection (FBP). This retrospective study included 54 patients (mean age: 67.7 ± 13.1) who underwent contrast-enhanced CT from May 2023 to August 2023. Among eligible patients, 30 and 24 were positive and negative for pancreatic cystic lesions, respectively. DLR and FBP were used to reconstruct portal venous phase images. Objective image quality analyses calculated quantitative image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) using regions of interest on the abdominal aorta, pancreatic lesion, and pancreatic parenchyma. Three blinded radiologists performed subjective image quality assessment and lesion detection tests. Lesion depiction, normal structure illustration, subjective image noise, and overall image quality were utilized as subjective image quality indicators. DLR significantly reduced quantitative image noise compared with FBP (p < 0.001). SNR and CNR were significantly improved in DLR compared with FBP (p < 0.001). Three radiologists rated significantly higher scores for DLR in all subjective image quality indicators (p ≤ 0.029). Performance of DLR and FBP were comparable in lesion detection, with no statistically significant differences in the area under the receiver operating characteristic curve, sensitivity, specificity and accuracy. DLR reduced image noise and improved image quality with a clearer depiction of pancreatic structures. These improvements may have a positive effect on evaluating pancreatic cystic lesions, which can contribute to appropriate management of these lesions.

Extracting Multimodal Surface-Wave Dispersion Curves from Ambient Seismic Noise Using High-Resolution Linear Radon Transform

Abstract In the past two decades or so, ambient noise tomography (ANT) has emerged as an established method for imaging subsurface seismic velocity structures. One of the key steps in ANT is to extract surface-wave dispersion curves. The predominant approach for subsurface shear-wave velocity structure inversion involves utilizing fundamental-mode surface waves in ANT. Nevertheless, a notable challenge encountered is the issue of nonuniqueness when employing the dispersion information of fundamental-mode surface waves to invert for shear-wave velocity models. The inclusion of higher-mode dispersion curves in the inversion offers several benefits, including the reduction of nonuniqueness, enhancement of inversion stability, and decreased dependence on the initial model. In this study, we illustrate the applicability of the high-resolution linear radon transform method (HRLRT) for extracting multimodal surface-wave dispersion energy from ambient seismic noise data. We apply HRLRT to both the synthetic noise data and real data recorded by USArray. Our results of applications show that the HRLRT method can extract multimodal surface-wave dispersion information. Compared with established methods such as the frequency–Bessel transform and multicomponent frequency–Bessel transform, the HRLRT exhibits an advantage in suppressing “crossed” artifacts and the second-/third-type artifacts caused by sparse spatial sampling, and the resulting dispersion energy from HRLRT has narrower peaks, meaning high resolution of dispersion curves based on the HRLRT method.

Rare-earth dopant distribution measurement of optical fibers based on filtered back projection

We present a nondestructive measurement based on filtered back projection (FBP) for the distribution of rare-earth dopant in optical fibers. Based on the approach, a measurement system is established, where the single-mode pumped laser illuminates the fiber under test from the side, generating multi-wavelength fluorescence through photoluminescence (PL). The distribution of rare-earth doped particles is proportional to the distribution of fluorescence intensity. The projection images are collected by the CCD camera at 90° relative to the excitation arm. According to the computed tomography (CT), the rotation of the optical fiber enables the experimental measurements of intensity information extracted from multi-angle projections. The rare-earth dopant distribution of the fiber is reconstructed by the improved filtered back projection algorithm, which improves the quality of the reconstructed result by incorporating total variation (TV) algorithm. To evaluate the proposed approach, rare-earth dopant distribution is compared with results measured by EPMA, exhibiting a correlation coefficient above 0.99, and the relationship between the dopant distribution and the refractive index is analyzed. Measurements on doped particle distributions at various wavelengths are conducted, and the fluorescence spectrum are constructed to further analyze the fluorescence characteristics of rare earth doped fiber. Measurement of RE dopant distribution will be useful for quality evaluation and optimization of partial active optical fibers.

Performance of Iterative Reconstruction in Image Space Algorithm in Combination with Automatic Tube Current Modulation Compared to Filtered Back Projection in Brain CT Scan.

High-quality images with minimum radiation dose are considered a challenge in Computed Tomography (CT) scans. The current study aimed to assess the efficacy of the Iterative Reconstruction in Image Space (IRIS) algorithm combined with Automatic Tube Current Modulation (ATCM) compared to Filtered Back Projection (FBP) in brain CT scans. In this cross-sectional study, 200 patients underwent to brain CT scan, and images were then reconstructed using both FBP and IRIS. The CT Number (CTN), noise, and Signal-to-Noise Ratio (SNR) were computed for different tissues from CT images. The performance of two algorithms under different exposure conditions was evaluated using a water phantom. Two experienced radiologists assessed the image quality. Volume CT Dose Index (CTDIvol) and Dose Length Product (DLP) were recorded for each scan. FBP reconstruction exhibited higher noise and lower SNR compared to IRIS, both with and without ATCM. Noise levels significantly increased for FBP combined with ATCM. Subjective analysis showed higher performance for IRIS without ATCM compared to other approaches. The mean CTDIvol with and without ATCM was 20.04±3.33 and 36.37±4.65 mGy, respectively. In the phantom study, the noise with IRIS remained lower than that with FBP even with a 42% dose reduction. IRIS algorithm can preserve the image quality when radiation dose is significantly reduced by ATCM in brain CT scan. Implementation of IRIS combined with ATCM is recommended for brain CT examinations.