Advanced Reconstruction Algorithms Research Articles

Non-Invasive Bio-Impedance Imaging and Sensing for Medical Diagnostics and Industrial Applications

Bio-impedance-based imaging technique stands at the intersection of medical/industrial imaging techniques and is also known as electrical impedance tomography (EIT), imparting a non-invasive, value-powerful approach for imaging biological tissues and procedures. We begin by elucidating the essential standards underlying EIT, focusing on the size of impedance variations inside an object (plastic pipe) to reconstruct its inner conductivity distribution. Subsequently, we delve into the numerous programs of EIT across numerous fields and discuss recent experimental strategies and advancements aimed at mitigating these boundaries, such as novel electrode configurations, advanced reconstruction algorithms, and incorporation of complementary imaging modalities. Finally, we underscore the importance of experimental research in advancing the capabilities and packages of EIT as a valuable device in industrial monitoring, specifically in bedside tracking, process pipelines, tank monitoring and also used for other medical or industrial detection, even as additionally exploring its rising roles in commercial and environmental applications.

Bioimaging of sense organs and the central nervous system in extant fishes and reptiles in situ: A review.

Bioimaging is changing the field of sensory biology, especially for taxa that are lesser-known, rare, and logistically difficult to source. When integrated with traditional neurobiological approaches, developing an archival, digital repository of morphological images can offer the opportunity to improve our understanding of whole neural systems without the issues of surgical intervention and negate the risk of damage and artefactual interpretation. This review focuses on current approaches to bioimaging the peripheral (sense organs) and central (brain) nervous systems in extant fishes (cartilaginous and bony) and non-avian reptiles in situ. Magnetic resonance imaging (MRI), micro-computed tomography (μCT), both super-resolution track density imaging and diffusion tensor-based imaging, and a range of other new technological advances are presented, together with novel approaches in optimizing both contrast and resolution, for developing detailed neuroanatomical atlases and enhancing comparative analyses of museum specimens. For MRI, tissue preparation, including choice of fixative, impacts tissue MR responses, where both resolving power and signal-to-noise ratio improve as field strength increases. Time in fixative, concentration of contrast agent, and duration of immersion in the contrast agent can also significantly affect relaxation times, and thus image quality. For μCT, the use of contrast-enhancing stains (iodine-, non-iodine-, or nanoparticle-based) is critical, where the type of fixative used, and the concentration of stain and duration of staining time often require species-specific optimization. Advanced reconstruction algorithms to reduce noise and artifacts and post-processing techniques, such as deconvolution and filtering, are now being used to improve image quality and resolution.

Optimizing Neuroimaging Techniques for Enhanced Visualization of Intracranial Aneurysms

Intracranial aneurysms, abnormal dilations of blood vessels in the brain, pose significant health risks, especially when ruptured, leading to conditions such as subarachnoid hemorrhage and stroke. The primary objective of this study is to evaluate the diagnostic accuracy of different computed tomography angiography (CTA) protocols for detecting intracranial aneurysms and to identify methods for optimizing scanning parameters to reduce radiation exposure while ensuring high image quality. Results from the analysis of various CTA protocols revealed that lowering tube voltages (80-120 kV) and milliampere seconds (mAs) effectively reduced radiation exposure without compromising the quality of images. Additionally, the use of advanced reconstruction algorithms, including deep learning techniques, significantly improved the visualization of small vascular structures, thereby enhancing diagnostic accuracy. These findings provide valuable insights for enhancing clinical practices in the fields of neurology and radiology.

3D modeling of the urinary bladder using electrical impedance tomography: advanced reconstruction algorithms and medical applications

Purpose: The research presented in this paper was conducted to obtain a detailed 3D model of the urinary bladder using electrical impedance tomography, a noninvasive tomographic technique. Methods: Electrical impedance tomography (EIT) is an imaging technique that measures electrical impedance inside the human body. Many methods, including those based on physical models and machine learning, are used to reconstruct the considered 3D object using EIT. The work focuses on the Gauss-Newton algorithm in its generalized form. Results: Three-dimensional reconstructions of the urinary bladder were obtained. The models are built with high accuracy and can be processed by subsequent algorithms. Discussion: The constructed models can serve as the basis for correct diagnosis in medicine and as research material for subsequent work, for example, on the possibilities of 3D printing. Possible methods of obtaining even higher-quality reconstruction also remain to be considered.

EIT probe based intraoperative tissue inspection for minimally invasive surgery

Abstract Electrical impedance tomography (EIT) has become an integral component in the repertoire of medical imaging techniques, particularly due to its non-invasive nature and real-time imaging capabilities. Despite its potential, the application of EIT in minimally invasive surgery (MIS) has been hindered by a lack of specialized electrode probes. Existing designs often compromise between invasiveness and spatial sensitivity: probes small enough for MIS often fail to provide detailed imaging, while those offering greater sensitivity are impractically large for use through a surgical trocar. Addressing this challenge, our study presents a breakthrough in EIT probe design. The open electrode probe we have developed features a line of 16 electrodes, thoughtfully arrayed to balance the spatial demands of MIS with the need for precise imaging. Employing an advanced EIT reconstruction algorithm, our probe not only captures images that reflect the electrical characteristics of the tissues but also ensures the homogeneity of the test material is accurately represented. The versatility of our probe is demonstrated by its capacity to generate high-resolution images of subsurface anatomical structures, a feature particularly valuable during MIS where direct visual access is limited. Surgeons can rely on intraoperative EIT imaging to inform their navigation of complex anatomical landscapes, enhancing both the safety and efficacy of their procedures. Through rigorous experimental validation using ex vivo tissue phantoms, we have established the probe’s proficiency. The experiments confirmed the system’s high sensitivity and precision, particularly in the critical tasks of subsurface tissue detection and surgical margin delineation. These capabilities manifest the potential of our probe to revolutionize the field of surgical imaging, providing a previously unattainable level of detail and assurance in MIS procedures.

Spatial and temporal motion characterization for x-ray CT.

Motion induced image artifacts have been the focus of many investigations for x-ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post-processing algorithms to minimize motion artifacts. Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image-space spatial- and temporal-consistency metric (CM) to detect and characterize the local motion. In a non-rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region-of-interests for consistency-metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion. Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in-plane motion, cross-plane motion, cone-beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images. In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view-by-view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact.

Stability of radiomic features from positron emission tomography images: a phantom study comparing advanced reconstruction algorithms and ordered subset expectation maximization.

In this study, we compared the repeatability and reproducibility of radiomic features obtained from positron emission tomography (PET) images according to the reconstruction algorithm used-advanced reconstruction algorithms, such as HYPER iterative (IT), HYPER deep learning reconstruction (DLR), and HYPER deep progressive reconstruction (DPR), or traditional Ordered Subset Expectation Maximization (OSEM)-to understand the potential variations and implications of using advanced reconstruction techniques in PET-based radiomics. We used a heterogeneous phantom with acrylic spherical beads (4- or 8-mm diameter) filled with 18F. PET images were acquired and reconstructed using OSEM, IT, DLR, and DPR. Original and wavelet radiomic features were calculated using SlicerRadiomics. Radiomic feature repeatability was assessed using the Coefficient of Variance (COV) and intraclass correlation coefficient (ICC), and inter-acquisition time reproducibility was assessed using the concordance correlation coefficient (CCC). For the 4- and 8-mm diameter beads phantom, the proportion of radiomic features with a COV < 10% was equivocal or higher for the advanced reconstruction algorithm than for OSEM. ICC indicated that advanced methods generally outperformed OSEM in repeatability, except for the original features of the 8-mm beads phantom. In the inter-acquisition time reproducibility analysis, the combinations of 3 and 5min exhibited the highest reproducibility in both phantoms, with IT and DPR showing the highest proportion of radiomic features with CCC > 0.8. Advanced reconstruction methods provided enhanced stability of radiomic features compared with OSEM, suggesting their potential for optimal image reconstruction in PET-based radiomics, offering potential benefits in clinical diagnostics and prognostics.

Image quality characterization of an ultra-high-speed kilovoltage cone-beam computed tomography imaging system on an O-ring linear accelerator.

The quality of on-board imaging systems, including cone-beam computed tomography (CBCT), plays a vital role in image-guided radiation therapy (IGRT) and adaptive radiotherapy. Recently, there has been an upgrade of the CBCT systems fused in the O-ring linear accelerators called HyperSight, featuring a high imaging performance. As the characterization of a new imaging system is essential, we evaluated the image quality of the HyperSight system by comparing it with Halcyon 3.0 CBCT and providing benchmark data for routine imaging quality assurance. The HyperSight features ultra-fast scan time, a larger kilovoltage (kV) detector, a more substantial kV tube, and an advanced reconstruction algorithm. Imaging protocols in the two modes of operation, treatment mode with IGRT and the CBCT for planning(CBCTp) mode were evaluated and compared with Halcyon 3.0 CBCT. Image quality metrics, including spatial resolution, contrast resolution, uniformity, noise, computed tomography (CT) number linearity, and calibration error, were assessed using a Catphan and an electron density phantom and analyzed with TotalQA software. HyperSight demonstrated substantial improvements in contrast-to-noise ratio and noise in both IGRT and CBCTp modes compared to Halcyon 3.0 CBCT. CT number calibration error of HyperSight CBCTp mode (1.06%) closely matches that of a full CT scanner (0.72%), making it suitable for adaptive planning. In addition, the advanced hardware of HyperSight, such as ultra-fast scan time (5.9s) or 2.5 times larger heat unit capacity, enhanced the clinical efficiency in our experience. HyperSight represented a significant advancement in CBCT imaging. With its image quality, CT number accuracy, and ultra-fast scans, HyperSight has a potential to transform patient care and treatment outcomes. The enhanced scan speed and image quality of HyperSight are expected to significantly improve the quality and efficiency of treatment, particularly benefiting patients.

A physics based machine learning model to characterize room temperature semiconductor detectors in 3D

Room temperature semiconductor radiation detectors (RTSD) for X-ray and γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma$$\\end{document}-ray detection are vital tools for medical imaging, astrophysics and other applications. CdZnTe (CZT) has been the main RTSD for more than three decades with desired detection properties. In a typical pixelated configuration, CZT have electrodes on opposite ends. For advanced event reconstruction algorithms at sub-pixel level, detailed characterization of the RTSD is required in three dimensional (3D) space. However, 3D characterization of the material defects and charge transport properties in the sub-pixel regime is a labor intensive process with skilled manpower and novel experimental setups. Presently, state-of-art characterization is done over the bulk of the RTSD considering homogenous properties. In this paper, we propose a novel physics based machine learning (PBML) model to characterize the RTSD over a discretized sub-pixelated 3D volume which is assumed. Our novel approach is the first to characterize a full 3D charge transport model of the RTSD. In this work, we first discretize the RTSD between a pixelated electrodes spatially into 3 dimensions—x, y, and z. The resulting discretizations are termed as voxels in 3D space. In each voxel, the different physics based charge transport properties such as drift, trapping, detrapping and recombination of charges are modeled as trainable model weights. The drift of the charges considers second order non-linear motion which is observed in practice with the RTSDs. Based on the electron–hole pair injections as input to the PBML model, and signals at the electrodes, free and trapped charges (electrons and holes) as outputs of the model, the PBML model determines the trainable weights by backpropagating the loss function. The trained weights of the model represents one-to-one relation to that of the actual physical charge transport properties in a voxelized detector.

ACR benchmark testing of a novel high-speed ring-gantry linac kV-CBCT system.

A new generation cone-beam computed tomography (CBCT) system with new hardware design and advanced image reconstruction algorithms is available for radiation treatment simulation or adaptive radiotherapy (HyperSight CBCT imaging solution, Varian Medical Systems-a Siemens Healthineers company). This study assesses the CBCT image quality metrics using the criteria routinely used for diagnostic CT scanner accreditation as a first step towards the future use of HyperSight CBCT images for treatment planning and target/organ delineations. Image performance was evaluated using American College of Radiology (ACR) Program accreditation phantom tests for diagnostic computed tomography systems (CTs) and compared HyperSight images with a standard treatment planning diagnostic CT scanner (Siemens SOMATOM Edge) and with existing CBCT systems (Varian TrueBeam version 2.7 and Varian Halcyon version 2.0). Image quality performance for all Varian HyperSight CBCT vendor-provided imaging protocols were assessed using ACR head and body ring CT phantoms, then compared to existing imaging modalities. Image quality analysis metrics included contrast-to-noise (CNR), spatial resolution, Hounsfield number (HU) accuracy, image scaling, and uniformity. All image quality assessments were made following the recommendations and passing criteria provided by the ACR. The Varian HyperSight CBCT imaging system demonstrated excellent image quality, with the majority of vendor-provided imaging protocols capable of passing all ACR CT accreditation standards. Nearly all (8/11) vendor-provided protocols passed ACR criteria using the ACR head phantom, with the Abdomen Large, Pelvis Large, and H&N vendor-provided protocols produced HU uniformity values slightly exceeding passing criteria but remained within the allowable minor deviation levels (5-7 HU maximum differences). Compared to other existing CT and CBCT imaging modalities, both HyperSight Head and Pelvis imaging protocols matched the performance of the SOMATOM CT scanner, and both the HyperSight and SOMATOM CT substantially surpassed the performance of the Halcyon 2.0 and TrueBeam version 2.7 systems. Varian HyperSight CBCT imaging system could pass almost all tests for all vendor-provided protocols using ACR accreditation criteria, with image quality similar to those produced by diagnostic CT scanners and significantly better than existing linac-based CBCT imaging systems.

Music curriculum integration and reconstruction model based on advanced music waveform iterative reconstruction algorithm

To improve the effect of intelligent teaching in music classrooms, this paper combines the advanced music waveform iterative reconstruction algorithm to analyze the integration and reconstruction of the music curriculum. Aiming at the problem that the projection matrix occupies a large space and takes a long time to calculate in iterative reconstruction, a fast and real-time incremental method for generating a music wave matrix is proposed. The improved method avoids the judgment and comparison calculations performed by the incremental method when calculating the length and number of each voxel that the ray passes through. The research results show that the music curriculum integration and reconstruction model based on the advanced music waveform iterative reconstruction algorithm can effectively improve the teaching effect of modern music classrooms.

Research on the Integration Strategy of Technology and Teaching in the Construction of Accounting Smart Classroom in Colleges and Universities

Abstract In the “Internet+” era, the smart classroom has emerged as a significant focal point in the research on educational informatization. This advancement represents a synergistic melding of novel technologies and academic practices. Accordingly, this study proposes a smart classroom teaching model tailored for accounting courses in higher education institutions. Within this framework, we have developed a specialized model to evaluate teaching efficacy in such environments. This evaluation model incorporates an enhanced version of the basic YOLOv5s model, which has been modified in three key areas to facilitate the detection of students’ facial expressions during lectures. Furthermore, to accommodate low-resolution images, the model integrates an advanced DRN-F face super-resolution reconstruction algorithm, thereby augmenting the accuracy of face recognition. This paper introduces a composite model for assessing the effectiveness of smart classroom teaching by amalgamating the two refined models. Comparative analysis with traditional manual recognition methods reveals that our model aligns more closely with the actual attentive responses of students when their faces are unobscured. A practical study on the teaching application further substantiates the efficacy of the smart classroom approach. Employing an independent sample t-test, the findings demonstrate a statistically significant difference in the post-test scores between the experimental and control groups, with a Sig. Value of 0.001 (p&lt;0.05). This indicates that the learning achievements of students under the smart classroom model are significantly enhanced compared to those in conventional teaching settings.

Synthetization of high-dose images using low-dose CT scans.

Radiation dose reduction has been the focus of many research activities in x-ray CT. Various approaches were taken to minimize the dose to patients, ranging from the optimization of clinical protocols, refinement of the scanner hardware design, and development of advanced reconstruction algorithms. Although significant progress has been made, more advancements in this area are needed to minimize the radiation risks to patients. Reconstruction algorithm-based dose reduction approaches focus mainly on the suppression of noise in the reconstructed images while preserving detailed anatomical structures. Such an approach effectively produces synthesized high-dose images (SHD) from the data acquired with low-dose scans. A representative example is the model-based iterative reconstruction (MBIR). Despite its widespread deployment, its full adoption in a clinical environment is often limited by an undesirable image texture. Recent studies have shown that deep learning image reconstruction (DLIR) can overcome this shortcoming. However, the limited availability of high-quality clinical images for training and validation is often the bottleneck for its development. In this paper, we propose a novel approach to generate SHD with existing low-dose clinical datasets that overcomes both the noise texture issue and the data availability issue. Our approach is based on the observation that noise in the image can be effectively reduced by performing image processing orthogonal to the imaging plane. This process essentially creates an equivalent thick-slice image (TSI), and the characteristics of TSI depend on the nature of the image processing. An advantage of this approach is its potential to reduce impact on the noise texture. The resulting image, however, is likely corrupted by the anatomical structural degradation due to partial volume effects. Careful examination has shown that the differential signal between the original and the processed image contains sufficient information to identify regions where anatomical structures are modified. The differential signal, unfortunately, contains significant noise and has to be removed. The noise removal can be accomplished by performing iterative noise reduction to preserve structural information. The processed differential signal is subsequently subtracted from TSI to arrive at SHD. The algorithm was evaluated extensively with phantom and clinical datasets. For better visual inspection, difference images between the original and SHD were generated and carefully examined. Negligible residual structure could be observed. In addition to the qualitative inspection, quantitative analyses were performed on clinical images in terms of the CT number consistency and the noise reduction characteristics. Results indicate that no CT number bias is introduced by the proposed algorithm. In addition, noise reduction capability is consistent across different patient anatomical regions. Further, simulated water phantom scans were utilized in the generation of the noise power spectrum (NPS) to demonstrate the preservation of the noise-texture. We present a method to generate SHD datasets from regularly acquired low-dose CT scans. Images produced with the proposed approach exhibit excellent noise-reduction with the desired noise-texture. Extensive clinical and phantom studies have demonstrated the efficacy and robustness of our approach. Potential limitations of the current implementation are discussed and further research topics are outlined.

Evaluation of Electron Tomography Capabilities for Shale Imaging.

Despite the advantageous resolution of electron tomography (ET), reconstruction of three-dimensional (3D) images from multiple two-dimensional (2D) projections presents several challenges, including small signal-to-noise ratios, and a limited projection range. This study evaluates the capabilities of ET for thin sections of shale, a complex nanoporous medium. A numerical phantom with 1.24 nm pixel size is constructed based on the tomographic reconstruction of a Barnett shale. A dataset of 2D projection images is numerically generated from the 3D phantom and studied over a range of conditions. First, common reconstruction techniques are used to reconstruct the shale structure. The reconstruction uncertainty is quantified by comparing overall values of storage and transport metrics, as well as the misclassification of pore voxels compared to the phantom. We then select the most robust reconstruction technique and we vary the acquisition conditions to quantify the effect of artifacts. We find a strong agreement for large pores over the different acquisition workflows, while a wider variability exists for nanometer-scale features. The limited projection range and reconstruction are identified as the main experimental bottlenecks, thereby suggesting that sample thinning, advanced holders, and advanced reconstruction algorithms offer opportunities for improvement.

Dedicated Cone-Beam Breast CT: Reproducibility of Volumetric Glandular Fraction with Advanced Image Reconstruction Methods.

Dedicated cone-beam breast computed tomography (CBBCT) is an emerging modality and provides fully three-dimensional (3D) images of the uncompressed breast at an isotropic voxel resolution. In an effort to translate this modality to breast cancer screening, advanced image reconstruction methods are being pursued. Since radiographic breast density is an established risk factor for breast cancer and CBBCT provides volumetric data, this study investigates the reproducibility of the volumetric glandular fraction (VGF), defined as the proportion of fibroglandular tissue volume relative to the total breast volume excluding the skin. Four image reconstruction methods were investigated: the analytical Feldkamp-Davis-Kress (FDK), a compressed sensing-based fast, regularized, iterative statistical technique (FRIST), a fully supervised deep learning approach using a multi-scale residual dense network (MS-RDN), and a self-supervised approach based on Noise-to-Noise (N2N) learning. Projection datasets from 106 women who participated in a prior clinical trial were reconstructed using each of these algorithms at a fixed isotropic voxel size of (0.273 mm3). Each reconstructed breast volume was segmented into skin, adipose, and fibroglandular tissues, and the VGF was computed. The VGF did not differ among the four reconstruction methods (p = 0.167), and none of the three advanced image reconstruction algorithms differed from the standard FDK reconstruction (p > 0.862). Advanced reconstruction algorithms developed for low-dose CBBCT reproduce the VGF to provide quantitative breast density, which can be used for risk estimation.

Impact on Reconstruction SPECT Image Distortion by Iteration and Subset Number

Iterative reconstruction on Single Photon Emission Computed Tomography (SPECT) imaging has recently been commercially available, resulting in reconstructed images with lower noise levels and better spatial resolution. The reconstruction variables employed and chosen under these suggestions include post-filtering, as well as several iterations and subset numbers. However, most of the number of iterations and subsets will give distortion to the segmentation image on a volume to be segmented. For this purpose, this paper will compare the effect of distortion on the dice similarity coefficient of volume segmentation for the iterative reconstruction setting by default as practice. The best outcome with the smallest root-mean-square deviation, highest percentage contrast values, and highest dice similarity coefficient) for all iterations and subsets is in iteration 6 subset 16. Here, the root-mean-square deviation is 8.06. Moreover, the four most enormous spheres can be calculated from the six spheres filled. The best contrast percentage is in subset 32, but the background variability for that subset is exceptionally high, which is 64.5. With post-filtering implementation, the scatter-corrected slices show the most enhanced image resolution and contrast when using advanced Astonish reconstruction algorithms. The user must decide if noise reduction via post-filtering or increased image resolution is more suitable for a certain imaging process.

“Seeing” Individual Atoms in 3D: Atomic Electron Tomography

Have you ever seen atoms in 3D? Atoms are the basic building blocks of materials, and scientists have been actively seeking ways to “see” the 3-dimensional positions of atoms, i.e., where the atoms are located within materials. With the development of powerful modern transmission electron microscopy technology and advanced reconstruction algorithms, it became possible to really observe the individual atoms in 3D. In this paper, we will review the recent progress of the technique “atomic electron tomography”, which allowed the 3D atomic structural determination of complex nanomaterials.

Neutron imaging software tools at the Oak Ridge National Laboratory

Over the past decade, Oak Ridge National Laboratory developed neutron capabilities at both the High Flux Isotope Reactor and the Spallation Neutron Source. A broad scientific portfolio has flourished at both facilities such that a new facility named VENUS is currently under construction at the Spallation Neutron Source. With a large range of applications, comes a broad range of user expertise regarding data processing, analysis, and visualization. Our team has developed and implemented a suite of tools to respond to all user needs. These tools span from data reduction, normalization, general tools such as organization of data folders, pre-processing of data, etc., to strain mapping, neutron grating interferometry, and advanced reconstruction algorithms for computed tomography. This manuscript provides an overview of some of the aforementioned tools.

Comparative study of advanced image reconstruction algorithms for complex arbitrary components

Accurate ultrasonic imaging and inspection of specimens with non-planar complex surfaces are often challenging from both transducer coupling and imaging perspectives. Several inspection strategies have been developed to inspect such complex interfaced components such as flat arrays with flexible wedges or water-filled chambers with a flexible membrane. In these methods, firstly surface profiles are estimated and then Fermat’s principle or other algorithms are applied to calculate delay laws. The main drawback of these methods is the longer computation time and therefore real time inspection is limited, also a coupling problem due to the complex geometries. Alternative approaches have been developed to compute the ultrasonic beam paths such as the Fast-Marching Method (FMM), Dijkstra’s algorithms, numerical analysis, bisection and root-finding algorithms for the inspection of complex surface components. These algorithms can be applied in two and three-dimensional domains resulting in high computation time. This work presents an experimental comparison of an iterative beamforming method for arbitrary shaped surface profile imaging against both Full Matrix Capture-Total Focusing Method (FMC-TFM) and CIVA simulation on an aluminium calibration block with Side drilled holes (SDHs) at various depths in the range of 15-80 mm. The performance of the iterative beamforming method is evaluated in terms of sensitivity, Signal to Noise Ratio (SNR) and Array Performance Indicators (API). The method provides a 2 dB improved SNR when compared to FMC-TFM.

Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner.

Portable magnetic resonance imaging (pMRI) has potential to rapidly acquire images at the patients' bedside to improve access in locations lacking MRI devices. The scanner under consideration has a magnetic field strength of 0.064T, thus image-processing algorithms to improve image quality are required. Our study evaluated pMRI images produced using a deep learning (DL)-based advanced reconstruction scheme to improve image quality by reducing image blurring and noise to determine if diagnostic performance was similar to images acquired at 1.5T. Six radiologists viewed 90 brain MRI cases (30 acute ischemic stroke (AIS), 30 hemorrhage, 30 no lesion) with , , and fluid attenuated inversion recovery sequences, once using standard of care (SOC) images (1.5T) and once using pMRI DL-based advanced reconstruction images. Observers provided a diagnosis and decision confidence. Time to review each image was recorded. Receiver operating characteristic area under the curve revealed overall no significant difference () between pMRI and SOC images. Examining each abnormality, for acute ischemic stroke, there was a significant difference () with SOC better than pMRI; but for hemorrhage, there was no significant difference (). There was no significant difference in viewing time for pMRI versus SOC () or abnormality (). The deep learning (DL)-based reconstruction scheme to improve pMRI was successful for hemorrhage, but for acute ischemic stroke the scheme could still be improved. For neurocritical care especially in remote and/or resource poor locations, pMRI has significant clinical utility, although radiologists should be aware of limitations of low-field MRI devices in overall quality and take that into account when diagnosing. As an initial triage to aid in the decision of whether to transport or keep patients on site, pMRI images likely provide enough information.