Tomographic Reconstruction Research Articles

Conceptual research on meeting tomographic reconstruction and measurement accuracy requirements: Key factors in the development of a radiated power diagnostics for DEMO

Conceptual research on meeting tomographic reconstruction and measurement accuracy requirements: Key factors in the development of a radiated power diagnostics for DEMO

Phase space reconstruction technique for beam optimization in the Low Energy High Intensity Proton Accelerator (LEHIPA)

The Low Energy High Intensity Proton Accelerator (LEHIPA) has been commissioned to the design energy of 20 MeV at BARC, India. The low energy beam transport (LEBT) channel of LEHIPA consists of two solenoids and drift spaces for matching the 50 keV proton beam from the ECR ion source (ECR-IS) to the RFQ. The ion beam extracted from the ECR-IS also contains molecular species like H2 + and H3 +. Proton fraction in the beam is found to degrade slowly with time due to surface contamination of the plasma chamber and this reduction in proton beam current has implications for longitudinal and transverse beam dynamics. Hence, it becomes important to characterise the beam at different proton fraction levels to understand the end-to-end beam dynamics of LEHIPA. Computed Tomography (CT) technique has been used for the beam phase-space reconstruction in LEHIPA, using a Python program, incorporating the feature of filtering secondary species from the beam profiles measured using slit scanners in the LEBT. Simultaneous Algebraic Reconstruction Technique (SART) is used in the reconstruction since it is identified to be a better technique for a limited number of projections. The reconstruction program is benchmarked with the TraceWin beam dynamics code and implemented on the measured beam profiles to recreate the phase space distribution at the beginning of LEBT. Further, the tomographic reconstruction method is compared with the solenoid scan method and the rms emittance values are found to be in good agreement. The measured tomographic phase space distribution has then been used as TraceWin input for LEHIPA end-end beam dynamics simulations and the LEHIPA beam line parameters are re-optimized for minimum beam emittance growth. This paper presents the simulations of CT technique, benchmarking simulations with TraceWin, phase space reconstruction with measured beam profiles and beam dynamics studies of LEHIPA using the reconstructed beam distributions.

Stochastic Gauss-Newton method for estimating absorption and scattering in optical tomography with the Monte Carlo method for light transport.

Image reconstruction in optical tomography in the so-called transport regime, where the diffusion approximation is not valid, requires modeling of light transport using the radiative transfer equation. In this work, we approach this problem by utilizing the Monte Carlo method for light transport. In this work, we propose a methodology for absolute imaging of absorption and scattering in this regime utilizing a Monte Carlo method for light transport. The image reconstruction problem is formulated as a minimization problem that is solved using a stochastic Gauss-Newton method. In the construction of the Jacobian matrix for scattering, a perturbation approximation for Monte Carlo is utilized. The approach is evaluated with numerical simulations using an adaptive approach where the number of photon packets is adjusted during the iterations, and with different fixed numbers of photon packets. The simulations show that the Monte Carlo method for light transport can be utilized in the absolute imaging problem of optical tomography and that the absorption and scattering parameters can be estimated simultaneously with good accuracy.

Correction: Randomized recursive techniques for image reconstruction in diffuse optical tomography

Correction: Randomized recursive techniques for image reconstruction in diffuse optical tomography

Ultrasound-guided quadratus lumborum block in sheep: A cadaveric study.

This prospective anatomical study aimed to establish an ultrasound-guided technique to the quadratus lumborum (QL) plane in sheep cadavers. Thirteen cadavers, weighing less than 117 kg, were included. In phase 1, one cadaver underwent dissection and two cadavers underwent 3D computed tomographic reconstruction for anatomical evaluation of the thoracolumbar region. In phase 2, two cadavers were used to compare two ultrasound techniques to the QL plane: lateral to the QL muscle with a transversal approach (LQL) and transmuscular between QL and psoas muscles with a longitudinal approach (TQL). For LQL, the reference was the first lumbar transverse process, whereas for TQL, it was the intertransverse region between the first and second lumbar vertebrae. The needle was advanced in-plane towards the specific target for each technique and a total of four injections were performed using 0.4 ml kg-1 of a dye-lidocaine solution. In phase 3, 10 cadavers received bilateral LQL injections (n = 20). All cadavers were then dissected to evaluate spread of dye. In phase 2, following LQL injections, no dye was observed in undesired locations; however, the dye was noted in the retroperitoneal space (1/2) after TQL injections. In phase 3, the 13th thoracic, first, second, third lumbar nerves, and sympathetic trunk segments were stained in 80%, 95%, 100%, 45% and 35% of the injections, respectively. In conclusion, the LQL technique was feasible, allowing staining of the spinal nerves innervating the cranial abdomen in sheep cadavers. Further studies in live animals are warranted.

Experimental Tip Shape Imaging Routes to Assist Data Reconstruction in Atom Probe Tomography

Experimental Tip Shape Imaging Routes to Assist Data Reconstruction in Atom Probe Tomography

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms

BackgroundComputed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT.MethodsNine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed.ResultsDLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058).ConclusionDLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used.Relevance statementDeep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction.Key PointsLow-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms.Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction.Dose has little impact on lesion detectability against anatomical background structures.Graphical

Multi-field Split Curvature Wavefront Sensing and its Application in the Large Field Survey Telescope

The image quality of a large field survey telescope with a fast focal ratio of the primary mirror is high sensitivity to the optical elements’ misalignments and the primary mirror’s deformations. To maintain good optical performance, the perturbations need to be aligned, and the tomographic reconstruction of these perturbations can be derived from wavefront sensing with multi-field points for alignment. This work undertakes a comprehensive examination related to the implementation and optimization of a multi-field split curvature wavefront sensing system, including defocus distance, signal pre-processing, deblending of overlapped doughnuts, field-dependent correction, and distorted coordinate correction. We also conduct experiments to demonstrate the application and performance of a multi-field split curvature wavefront sensing system in Mephisto. In the context of the decentering of the secondary mirror, the coefficient of determination (R) attains a high value of 0.993, indicating a robust linearity between the coma coefficients and the decentering of the secondary mirror. A comparative analysis of the simulated and experimental results shows that the difference between them is less than 0.1 λ.

A Semi-smooth Newton Method for Diffuse Optical Tomography with L1-Norm and Total Variation Regularization

This study introduces a novel multi-measurement Diffuse Optical Tomography reconstruction method that employs both L1-norm and Total Variation regularization, solved by the semi-smooth Newton method. By integrating L1-norm regularization to address sparsity and TV regularization to preserve edges and structural details, our approach effectively combats the ill-posed nature of DOT reconstructions. The results demonstrate that our approach reduces the required iterations while simultaneously maintaining or enhancing reconstruction accuracy and robustness to noise.

Iterative Tomographic Image Reconstruction Algorithm Based on Extended Power Divergence by Dynamic Parameter Tuning.

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques.

Applicability and potential of monitored reconstruction in computed tomography.

Monitored tomographic reconstruction (MTR) is a potentially powerful tool for dose and time reduction in computed tomography scanning. We are the first to study the issue of practical implementation of MTR protocols in current-generation real-life instruments. We propose an empirical quantitative model for calculating acquisition and reconstruction times. It is demonstrated that projection acquisition order has a significant impact on the time and dose of tomographic experiments. The new alternative acquisition most suitable for MTR protocols is proposed. To estimate the restrictions and scope of applicability for MTR four typical commercial setups are studied within a proposed model. We construct an experimental stand for achieving a real-time reconstruction, together with validation of the proposed acquisition time model. We demonstrate that real-time reconstruction may be implemented without slowing down an acquisition process. An optimization of reconstruction from partial data is proposed, which allowed the production of 385 and 440 reconstructions for standard and proposed acquisition orders correspondingly during a single acquisition of 512 projections. The results of the study demonstrate that with proposed optimizations MTR can be effectively utilized for practical applications using the current generation of existing setups in industrial and nano tomography fields.

Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction

BackgroundDeep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions. MethodsWe conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta. ResultsThere were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants’ body mass index and waist circumference. ConclusionsThe optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.

Noninvasive Quantification of Glucose Metabolism in Mice Myocardium Using the Spline Reconstruction Technique.

The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for various iteration numbers, using small-animal dynamic PET data obtained from a Mediso nanoScan® PET/CT scanner. For this purpose, Patlak graphical kinetic analysis was employed to noninvasively quantify the myocardial metabolic rate of glucose (MRGlu) in seven male C57BL/6 mice (n=7). All analytic reconstructions were performed via software for tomographic image reconstruction. The analysis of all PET-reconstructed images was conducted with PMOD software (version 3.506, PMOD Technologies LLC, Fällanden, Switzerland) using the inferior vena cava as the image-derived input function. Statistical significance was determined by employing the one-way analysis of variance test. The results revealed that the differences between the values of MRGlu obtained via SRT versus FBP, and the variants of he Tera-Tomo 3D algorithm were not statistically significant (p > 0.05). Overall, the SRT appears to perform similarly to the other algorithms investigated, providing a valid alternative analytic method for preclinical dynamic PET studies.

Head of Zebu cattle (Bos Taurus indicus): sectional anatomy and 3D computed tomography

The research was designed to use computed tomography (CT) with 3D-CT reconstruction imaging techniques and the various anatomical sections—plana transversalia, frontalis, and dorsalia—to describe the anatomical architecture of the Zebu cattle head. Our study used nine mature heads. The CT bone window created detailed images of cranial bones, mandibles, teeth, and hyoid bones. All of the head cavities were evaluated, including the cranial, orbital, oral, auricular, and nasal cavities with their paranasal and conchal sinuses. The septum nasi, attached to the vomer and maxillary bones, did not reach the nasal cavity floor caudally at the level of the second premolar teeth, resulting in a single median channel from the choanae to the nasopharynx. The positions, boundaries, and connections of the paranasal sinuses were clearly identified. There were four nasal conchal sinuses (that were named the dorsal, middle, ethmoidal, and ventral) and five paranasal sinuses that were described as the following: sinus frontalis, maxillaris, palatinorum, and lacrimalis, as defined in the different anatomical sections and computed tomographic images. The complicated sinus frontalis caused the pneumatization of all bones that surrounded the cranial cavity, with the exception of the ethmoidal and body of basisphenoid bones. The sinus maxillaris was connected to the sinus lacrimalis and palatinorum through the maxillolacrimal and palatomaxillary openings, and to the middle nasal meatus through the nasomaxillary opening. Our findings provide a detailed anatomical knowledge for disease diagnosis to internal medicine veterinarians and surgeons by offering a comprehensive atlas of the Zebu cattle anatomy.

Ultrastructure and Spectral Characteristics of the Compound Eye of Asiophrida xanthospilota (Baly, 1881) (Coleoptera, Chrysomelidae).

In this study, the morphology and ultrastructure of the compound eye of Asi. xanthospilota were examined by using scanning electron microscopy (SEM), transmission electron microscopy (TEM), micro-computed tomography (μCT), and 3D reconstruction. Spectral sensitivity was investigated by electroretinogram (ERG) tests and phototropism experiments. The compound eye of Asi. xanthospilota is of the apposition type, consisting of 611.00 ± 17.53 ommatidia in males and 634.8 0 ± 24.73 ommatidia in females. Each ommatidium is composed of a subplano-convex cornea, an acone consisting of four cone cells, eight retinular cells along with the rhabdom, two primary pigment cells, and about 23 secondary pigment cells. The open type of rhabdom in Asi. xanthospilota consists of six peripheral rhabdomeres contributed by the six peripheral retinular cells (R1~R6) and two distally attached rhabdomeric segments generated solely by R7, while R8 do not contribute to the rhabdom. The orientation of microvilli indicates that Asi. xanthospilota is unlikely to be a polarization-sensitive species. ERG testing showed that both males and females reacted to stimuli from red, yellow, green, blue, and ultraviolet light. Both males and females exhibited strong responses to blue and green light but weak responses to red light. The phototropism experiments showed that both males and females exhibited positive phototaxis to all five lights, with blue light significantly stronger than the others.

One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging

Objective. Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise. Approach. To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials. Main results. Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN. Significance. These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

A generalized deep neural network approach for improving resolution of fluorescence microscopy images

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264[Formula: see text]nm to 138[Formula: see text]nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

Microfiber Rotational Dynamics In Turbulence

We measure the effect of turbulence on spinning and tumbling rates of microfibers in wall-bounded turbulence. The measurements are performed in a turbulent water channel at Reτ 720. Fibres are 1.2 mm long and 10 m in diameter (aspect ratio 120). Their length ranges from 4 to 12 Kolmogorov length scales. They are neutrally buoyant, inertial-less, and rigid in these flow conditions. Six high-speed cameras image the fibres at the channel centre and in a near-wall region. We employ and further refine a technique of tomographic fibre reconstruction and tracking. Their curved shape is used to define a fibre-fixed reference frame and measure its time-resolved orientation. Thus measurements of tumbling and spinning rates are enabled. We provide a discussion about the uncertainty on the rotation rates based on their shape and angular displacement between time-steps. Based on converged statistics, we observed that the mean and mean square spinning are higher than tumbling rates at both channel centre and near-wall region. In addition, our study reveals that slender, curved fibres behave akin to straight rods, yet their curved shape provides a unique advantage: this curvature allows us to measure all rotational components.

Combined Rotating Ultra-High-Resolution Spectral Domain OCT and Scheimpflug Imaging for In Vivo Corneal Optical Biopsy.

This article introduces the Pentacam® Cornea OCT (optical coherence tomography). This advanced corneal imaging system combines rotating ultra-high-resolution spectral domain OCT with sub- 2-micron axial resolution and Scheimpflug photography. The purpose of this study is to present the first experience with the instrument and its potential for corneal diagnostics, including optical biopsy. In this prospective study, the Pentacam® Cornea OCT was used to image the corneas of seven patients. The novel wide-angle pericentric scan system enables optimal OCT imaging performance for the corneal layer structure over the entire width of the cornea, including the limbal regions. A detailed analysis of the resulting images assessed the synergism between the OCT and Scheimpflug photography. The Pentacam® Cornea OCT demonstrated significantly improved image resolution and ability to individualize corneal layers with high quality. There is a synergism between the OCT high-definition signal to individualize details on the cornea and Scheimpflug photography to detect and quantify corneal scattering. The noncontact exam was proven safe, user-friendly, and effective for enabling optical biopsy. Pentacam® Cornea OCT is an advancement in corneal imaging technology. The ultra-high-resolution spectral domain OCT and Scheimpflug photography provide unprecedented detail and resolution, enabling optical biopsy and improving the understanding of corneal pathology. Further studies are necessary to compare and analyze the tomographic reconstructions of the cornea with the different wavelengths, which may provide helpful information for diagnosing and managing corneal diseases.

The GoldX Fiducial Eraser.

Gold nanoparticles with sizes in the range of 5-15 nm are a standard method of providing fiducial markers to assist with alignment during reconstruction in cryogenic electron tomography. However, due to their high electron density and resulting contrast when compared to standard cellular or biological samples, they introduce artifacts such as streaking in the reconstructed tomograms. Here, we demonstrate a tool that automatically detects these nanoparticles and suppresses them by replacing them with a local background as a post-processing step, providing a cleaner tomogram without removing any sample relevant information or introducing new artifacts or edge effects from uniform density replacements.