Conventional Computed Tomography Reconstruction Research Articles

XTransCT: ultra-fast volumetric CT reconstruction using two orthogonal x-ray projections for image-guided radiation therapy via a transformer network

Objective. The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy. Approach. Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which require extensive x-ray projections and lead to high radiation doses and equipment constraints. Main results. The proposed XTransCT algorithm demonstrated superior performance in terms of image quality, structural accuracy, and generalizability across different datasets, including a hospital set of 50 patients, the large-scale public LIDC-IDRI dataset, and the LNDb dataset for cross-validation. Notably, the algorithm achieved an approximately 300% improvement in reconstruction speed, with a rate of 44 ms per 3D image reconstruction compared to former 3D convolution-based methods. Significance. The XTransCT architecture has the potential to impact clinical practice by providing high-quality CT images faster and with substantially reduced radiation exposure for patients. The model's generalizability suggests it has the potential applicable in various healthcare settings.

CT Rendering and Radiomic Analysis in Post-Chemotherapy Retroperitoneal Lymph Node Dissection for Testicular Cancer to Anticipate Difficulties for Young Surgeons.

Post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) in non-seminomatous germ-cell tumor (NSTGCTs) is a complex procedure. We evaluated whether 3D computed tomography (CT) rendering and their radiomic analysis help predict resectability by junior surgeons. The ambispective analysis was performed between 2016-2021. A prospective group (A) of 30 patients undergoing CT was segmented using the 3D Slicer software while a retrospective group (B) of 30 patients was evaluated with conventional CT (without 3D reconstruction). CatFisher's exact test showed a p-value of 0.13 for group A and 1.0 for Group B. The difference between the proportion test showed a p-value of 0.009149 (IC 0.1-0.63). The proportion of the correct classification showed a p-value of 0.645 (IC 0.55-0.87) for A, and 0.275 (IC 0.11-0.43) for Group B. Furthermore, 13 shape features were extracted: elongation, flatness, volume, sphericity, and surface area, among others. Performing a logistic regression with the entire dataset, n = 60, the results were: Accuracy: 0.7 and Precision: 0.65. Using n = 30 randomly chosen, the best result obtained was Accuracy: 0.73 and Precision: 0.83, with a p-value: 0.025 for Fisher's exact test. In conclusion, the results showed a significant difference in the prediction of resectability with conventional CT versus 3D reconstruction by junior surgeons versus experienced surgeons. Radiomic features used to elaborate an artificial intelligence model improve the prediction of resectability. The proposed model could be of great support in a university hospital, allowing it to plan the surgery and to anticipate complications.

A total variation prior unrolling approach for computed tomography reconstruction.

With the rapid development of deep learning technology, deep neural networks can effectively enhance the performance of computed tomography (CT) reconstructions. One kind of commonly used method to construct CT reconstruction networks is to unroll the conventional iterative reconstruction (IR) methods to convolutional neural networks (CNNs). However, most unrolling methods primarily unroll the fidelity term of IR methods to CNNs, without unrolling the prior terms. The prior terms are always directly replaced by neural networks. In conventional IR methods, the prior terms play a vital role in improving the visual quality of reconstructed images. Unrolling the hand-crafted prior terms to CNNs may provide a more specialized unrolling approach to further improve the performance of CT reconstruction. In this work, a primal-dual network (PD-Net) was proposed by unrolling both the data fidelity term and the total variation (TV) prior term, which effectively preserves the image edges and textures in the reconstructed images. By further deriving the Chambolle-Pock (CP) algorithm instance for CT reconstruction, we discovered that the TV prior updates the reconstructed images with its divergences in each iteration of the solution process. Based on this discovery, CNNs were applied to yield the divergences of the feature maps for the reconstructed image generated in each iteration. Additionally, a loss function was applied to the predicted divergences of the reconstructed image to guarantee that the CNNs' results were the divergences of the corresponding feature maps in the iteration. In this manner, the proposed CNNs seem to play the same roles in the PD-Net as the TV prior in the IR methods. Thus, the TV prior in the CP algorithm instance can be directly unrolled to CNNs. The datasets from the Low-Dose CT Image and Projection Data and the Piglet dataset were employed to assess the effectiveness of our proposed PD-Net. Compared with conventional CT reconstruction methods, our proposed method effectively preserves the structural and textural information in reference to ground truth. The experimental results show that our proposed PD-Net framework is feasible for the implementation of CT reconstruction tasks. Owing to the promising results yielded by our proposed neural network, this study is intended to inspire further development of unrolling approaches by enabling the direct unrolling of hand-crafted prior terms to CNNs.

A sequential regularization based image reconstruction method for limited-angle spectral CT

In spectral computed tomography (CT), the object is respectively scanned under different x-ray spectra. Multiple projection data can be collectively used for reconstructing basis images and virtual monochromatic images, which have been used in material decomposition, beam-hardening correction, bone removal, and so on. In practice, projection data may be obtained in a limited scanning angular range. Images reconstructed from limited-angle data by conventional spectral CT reconstruction methods will be deteriorated by limited-angle related artifacts and basis image decomposition errors. Motivated by observations of limited-angle spectral CT, we propose a sequential regularization-based limited-angle spectral CT reconstruction model and its numerical solver. Both simulated and real data experiments validate that our method is capable of suppressing artifacts, preserving edges and reducing decomposition errors.

The Role of 3D Reconstruction True-Volume Analysis in Osteochondral Lesions of the Talus: A Case Series

Evaluation and management of osteochondral lesions of the talus (OLTs) often warrant advanced imaging studies, especially in revision or cases with cystic defects. It is possible that orthopedic surgeons may overestimate the size and misinterpret the morphology of OLT from conventional computed tomography (CT), thereby influencing treatment strategies. The purpose of this study was to determine the utility of a novel means to estimate the true-volume of OLTs using 3D reconstructed images and volume analysis. With Institutional Review Board approval, an institutional radiology database was queried for patients with cystic OLTs that failed previous microfracture, having compatible CT scans and magnetic resonance imaging (MRI) between 2011 and 2016. Fourteen patients met inclusion criteria. Of these, 5 cases were randomly selected for 3D CT reconstruction modeling. Ten orthopedic surgeons independently estimated the volume of these 5 OLTs via standard CT. Then 3D reconstructions were made and morphometric true-volume (MTV) analysis measurements of each OLT were generated. The percent change in volumes from CT were compared to MTVs determined from 3D reconstructive analysis. On average, the volume calculated by conventional CT scanner grossly overestimated the actual size of the OLTs. The volume calculated on conventional CT scanner overestimated the size of OLTs compared to the 3D MTV reconstructed analysis by 285% to 864%. Our results showed that conventional measurements of OLTS with CT grossly overestimated the size of the lesion. The 3D MTV analysis of cystic osteochondral lesions may help clinicians with preoperative planning for graft selection and appropriate volume while avoiding unnecessary costs incurred with overestimation. Level IV, case series.

Single-Scan Dual-Energy CT Using Primary Modulation.

Compared with conventional computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but requires projection data acquired with two different effective x-ray spectra, limiting DECT applications to specialized scanners. We propose a hardware-based method, known as PM-DECT, which utilizes primary beam modulation to enable single-scan DECT on a conventional CT scanner. PM-DECT inserts an attenuation sheet with a spatially varying pattern-primary beam modulator-between the x-ray source and imaged object. During a CT scan, the modulator selectively hardens the x-ray beam, thereby increasing the average photon energy at specific detector pixel locations. Thus, PM-DECT simultaneously acquires high and low energy data at each projection angle. From the sparse projection data, high and low energy CT images are jointly reconstructed and simultaneously decomposed into basis materials via an iterative CT reconstruction algorithm with gradient weighting and an improved version of similarity based regularization. Studies on Catphan 600 and anthropomorphic head phantoms demonstrate that PM-DECT retains a high level of spatial resolution compared with conventional CT scans. Electron density values calculated from decomposed images indicate a limited error of 1.12% for PM-DECT. Comparison against a two-scan DECT technique shows that PM-DECT's image reconstruction from sparse data sets contributes only 0.66% error. By granting the opportunity for high-quality single-scan DECT on conventional CT scanners via limited hardware modification, PM-DECT has the potential to liberate DECT from specialized scanners, extending clinical availability, and implementation.

Preoperative planning of left-sided valve surgery with 3D computed tomography reconstruction models: sternotomy or a minimally invasive approach?

With the emergence of a new concept aimed at individualization of patient care, the focus will shift from whether a minimally invasive procedure is better than conventional treatment, to the question of which patients will benefit most from which technique? The superiority of minimally invasive valve surgery (MIVS) has not yet been proved. We believe that through better patient selection advantages of this technique can become more pronounced. In our current study, we evaluate the feasibility of 3D computed tomography (CT) imaging reconstruction in the preoperative planning of patients referred for MIVS. We retrospectively analysed all consecutive patients who were referred for minimally invasive mitral valve surgery (MIMVS) and minimally invasive aortic valve replacement (MIAVR) to a single surgeon in a tertiary referral centre for MIVS between March 2014 and 2015. Prospective preoperative planning was done for all patients and was based on evaluations by a multidisciplinary heart-team, an echocardiography, conventional CT images and 3D CT reconstruction models. A total of 39 patients were included in our study; 16 for mitral valve surgery (MVS) and 23 patients for aortic valve replacement (AVR). Eleven patients (69%) within the MVS group underwent MIMVS. Five patients (31%) underwent conventional MVS. Findings leading to exclusion for MIMVS were a tortuous or slender femoro-iliac tract, calcification of the aortic bifurcation, aortic elongation and pericardial calcifications. Furthermore, 2 patients had a change of operative strategy based on preoperative planning. Seventeen (74%) patients in the AVR group underwent MIAVR. Six patients (26%) underwent conventional AVR. Indications for conventional AVR instead of MIAVR were an elongated ascending aorta, ascending aortic calcification and ascending aortic dilatation. One patient (6%) in the MIAVR group was converted to a sternotomy due to excessive intraoperative bleeding. Two mortalities were reported during conventional MVS. There were no mortalities reported in the MIMVS, MIAVR or conventional AVR group. Preoperative planning of minimally invasive left-sided valve surgery with 3D CT reconstruction models is a useful and feasible method to determine operative strategy and exclude patients ineligible for a minimally invasive approach, thus potentially preventing complications.

Identifying Small Coronary Calcification in Non-Contrast 0.5-mm Slice Reconstruction to Diagnose Coronary Artery Disease in Patients with a Conventional Zero Coronary Artery Calcium Score.

Aims: In a new-generation computed tomography (CT) scanner, coronary artery calcium (CAC) scores were measured using 3.0-mm slice reconstruction images originally acquired with 0.5 mm thickness scans in a single beat. This study investigated the usefulness of thin-slice (0.5 mm) reconstruction for identifying small calcifications in coronary arteries and evaluated the association with coronary plaques and stenosis compared to conventional 3.0-mm reconstruction images.Methods: We evaluated 132 patients with zero CAC scores in conventional 3.0-mm Agatston method using a 320-slice CT. Then, 0.5-mm slice reconstruction was performed to identify small calcifications. The presence of stenosis and coronary plaques was assessed using coronary CT angiography.Results: In total, 22 small calcifications were identified in 18 patients. There were 28 (21%) patients with any (≥ 25%) stenosis (34 lesions). Forty-seven coronary plaques were found in 33 patients (25%), including 7 calcified plaques in 7 patients (5%), 34 noncalcified plaques in 27 patients (20%), and 6 partially calcified plaques in 5 patients (4%). Patients with small calcifications had a significantly higher prevalence of noncalcified or partially calcified plaques (83% vs 14%; p < 0.001) and obstructive stenosis (33% vs 5.2%; p < 0.001) compared to those without small calcifications. The addition of small calcifications to the coronary risk factors when diagnosing stenosis significantly improved the diagnostic value.Conclusion: Small calcifications detected by thin-slice 0.5-mm reconstruction are useful for distinguishing coronary atherosclerotic lesions in patients with zero CAC scores from conventional CT reconstruction.

High-Dynamic-Range CT Reconstruction Based on Varying Tube-Voltage Imaging.

For complicated structural components characterized by wide X-ray attenuation ranges, the conventional computed tomography (CT) imaging using a single tube-voltage at each rotation angle cannot obtain all structural information. This limitation results in a shortage of CT information, because the effective thickness of the components along the direction of X-ray penetration exceeds the limitation of the dynamic range of the X-ray imaging system. To address this problem, high-dynamic-range CT (HDR-CT) reconstruction is proposed. For this new method, the tube’s voltage is adjusted several times to match the corresponding effective thickness about the local information from an object. Then, HDR fusion and HDR-CT are applied to obtain the full reconstruction information. An accompanying experiment demonstrates that this new technology can extend the dynamic range of X-ray imaging systems and provide the complete internal structures of complicated structural components.

CT imaging method with varying energy based on logarithm demodulation

For complicated structural components with wider X-ray attenuation ranges, the projection information is short of single-energy computed tomography (CT) imaging because the attenuation thickness of the components along the orientation of X-ray penetration exceeds the limit of the dynamic range of the CT imaging system. Then under this circumstance, in the conventional imaging mode of fixed ray energy the phenomenon of overexposure and underexposure easily occurs. The whole structure information cannot be obtained, and the projection information is seriously lacking, hence it will affect the quality of CT reconstruction. This paper proposes a CT imaging method with varying energy based on logarithm demodulation.#br#For the incomplete projection due to the limited dynamic range, we collect images at various rotation angles, as in standard CT; also, at each angle we collect a series of images at different energies and fuse them into a single high dynamic range (HDR) image. Based on this HDR image, the conventional CT reconstruction methods can get the full construction information. However, the CT quality is poor, because of noise amplification in the fusion process of X-ray image sequences which are gathered during varying the X-ray energy. To solve this problem, this paper proposes to use logarithm transformation to reduce the dynamic range for the varying energy image sequences. This can change the weight coefficients' access in the projection sequences to suppress the noise. Also in the transformation sequences, we can also use the fusion method, which is based on image gray consistency, to compute the weighting coefficient, to obtain the HDR projection, because the logarithm transformation does not change the image structure, and the overlap area between image sequences is definite. Finally we use the conventional CT reconstruction algorithm to make the CT imaging with the complicated structural components.#br#An accompanying experiment with a complicated instrument demonstrates that the new method can extend the dynamic range of X-ray imaging system and provide a complete representation of the internal structure of the complicated structural components as compared with the conventional CT imaging method. So, it will be of important significance and may be applied to the potential system, so as to improve the detecting sensitivity of the system, and make the high-quality reconstruction about complex products.

Geometry-based CT scanner for measuring logs in sawmills

Significant economic advantage can be achieved by assessing logs at the inlet of a sawmill so that they can be optimally processed to produce the highest possible value products from the available raw material. Computed Tomography (CT) scanning is of particular interest because it can identify the locations and sizes of internal quality-controlling features such as knots, heartwood/sapwood extent, rot, cracks and holes. Conventional CT scanning is based on medical-style equipment, which needs to have very high spatial and density resolution. This need sets very high requirements on the measurement resolution and accuracy, and substantially increases equipment complexity and cost. These features make such equipment unsuited for typical sawmill applications. However, for log scanning applications, high spatial and density resolution and measurement accuracy are not needed because most targeted internal features are fairly large and have specific geometrical shapes. Based on this thought, this paper proposes a novel geometry-based CT reconstruction approach and corresponding density reconstruction algorithms. A prototype CT scanner was built and the CT reconstructions were determined by processing the measured data using the proposed density reconstruction approach. The results obtained compare well with the results computed from conventional CT reconstruction. The good comparison gives confidence in the usefulness and applicability of the proposed CT approach for industrial use in sawmills for log scanning.

Information extraction and CT reconstruction of liver images based on diffraction enhanced imaging

X-ray phase-contrast imaging (PCI) is a new emerging imaging technique that generates a high spatial resolution and high contrast of biological soft tissues compared to conventional radiography. Herein a biomedical application of diffraction enhanced imaging (DEI) is presented. As one of the PCI methods, DEI derives contrast from many different kinds of sample information, such as the sample’s X-ray absorption, refraction gradient and ultra-small-angle X-ray scattering (USAXS) properties, and the sample information is expressed by three parametric images. Combined with computed tomography (CT), DEI-CT can produce 3D volumetric images of the sample and can be used for investigating micro-structures of biomedical samples. Our DEI experiments for liver samples were implemented at the topography station of Beijing Synchrotron Radiation Facility (BSRF). The results show that by using our provided information extraction method and DEI-CT reconstruction approach, the obtained parametric images clearly display the inner structures of liver tissues and the morphology of blood vessels. Furthermore, the reconstructed 3D view of the liver blood vessels exhibits the micro blood vessels whose minimum diameter is on the order of about tens of microns, much better than its conventional CT reconstruction at a millimeter resolution. In conclusion, both the information extraction method and DEI-CT have the potential for use in biomedical micro-structures analysis.

SU-FF-T-222: The Analysis of Confounding Factors in Volume Reconstruction of 3DCRT with Spiral Mode CT Simulation

Purpose: This study is to analyze the difference in reconstructing volume by using phantom model between various settings of axial‐mode and spiral‐mode computed tomography(CT) simulator for three dimensional conformal radiotherapy.Method and Materials: Three phantom balls with different diameters (5.1, 9.9, 12.2 cm) were scanned by a single‐row detector CT simulator. The volumes of all phantom balls were reconstructed in the same system. The exactly calculated phantom ball volumes were the baselines as compared to the volumes by the conventional axial CT reconstruction. The reconstructed volumes from the axial‐scanning‐mode were compared with the corresponding settings of the spiral‐scanning‐mode in CT simulation, with four different Hounsfield thresholds, three different pitches (1, 1.5 and 2), and four different slice intervals (1, 2, 3 and 5 mm). Results: The larger slice width and HU threshold were associated with larger difference between the exactly calculated volumes and reconstructed volumes in axial CT mode. The volume losses were more than 5% for small phantom ball in all axial CT settings. The lowest HU threshold and slice width of less than 5 mm were needed to maintain the volume loss of less than 5% in medium and large balls. As compared to axial scanning, spiral scanning offered the volume reconstruction loss of less than 5% in almost all settings. The exceptions existed in the small phantom ball with the slice interval of 5 mm, spiral pitch of 1.5 and 2 at 50 HU, and slice interval of 5 mm, spiral pitch of 2 at 0 HU. Conclusion: Slice interval, spiral scanning pitch and HU threshold were the factors with the impact on the accurate estimation of volume reconstruction by spiral CT simulator. Spiral CT mode was feasible in most scanning settings with the acceptable volume reconstruction accuracy threshold of more than 95%.

Recent investigations on milk and butter

Significant economic advantage can be achieved by assessing logs at the inlet of a sawmill so that they can be optimally processed to produce the highest possible value products from the available raw material. Computed Tomography (CT) scanning is of particular interest because it can identify the locations and sizes of internal quality-controlling features such as knots, heartwood/sapwood extent, rot, cracks and holes. Conventional CT scanning is based on medical-style equipment, which needs to have very high spatial and density resolution. This need sets very high requirements on the measurement resolution and accuracy, and substantially increases equipment complexity and cost. These features make such equipment unsuited for typical sawmill applications. However, for log scanning applications, high spatial and density resolution and measurement accuracy are not needed because most targeted internal features are fairly large and have specific geometrical shapes. Based on this thought, this paper proposes a novel geometry-based CT reconstruction approach and corresponding density reconstruction algorithms. A prototype CT scanner was built and the CT reconstructions were determined by processing the measured data using the proposed density reconstruction approach. The results obtained compare well with the results computed from conventional CT reconstruction. The good comparison gives confidence in the usefulness and applicability of the proposed CT approach for industrial use in sawmills for log scanning.