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Background Purpose Methods Results Conclusion Cardiac computed tomography (CT) exams are some of the most complex CT exams due to the need to carefully time the scan when the heart chambers are near the peak contrast concentration. With current “bolus tracking” and “timing bolus” techniques, after contrast medium is injected, a target vessel or chamber is scanned periodically, and images are reconstructed to monitor the opacification. Both techniques have opportunities for improvement, such as reducing the contrast medium volume, the exam time, the number of manual steps, and improving the robustness of correctly timing the peak opacification.The objective of our study is to (1) develop a novel autonomous cardiac CT clinical workflow to track contrast bolus dynamics directly from pulsed x‐ray projections, (2) develop a new five‐dimensional virtual cardiac CT data generation tool with programmable cardiac profiles and bolus dynamics, and (3) demonstrate the feasibility of projection‐domain prospective bolus tracking using a neural network trained and tested with the virtual data to find the contrast peak.In our proposed workflow, pulsed mode projections (PMPs) are acquired with a wide‐open collimator under sparse view conditions (monitoring phase). Each time a new PMP is acquired, the neural network is used to estimate the contrast enhancement inside the target chambers. To train such a network, we introduce a new approach to generate clinically realistic virtual scan data based on a five‐dimensional cardiac model, by synthesizing user‐defined contrast bolus dynamics and patient electrocardiogram profiles. In this study, we investigated a scenario with one single PMP per rotation. To find the optimal PMP view angle, 20 angles were explored. For each angle, 300 virtual exams were generated from 115 human subject datasets and divided into training, validation, and testing groups. Twenty neural networks were trained and evaluated in total to find the optimal network. Finally, a simple bolus peak time estimation algorithm was developed and evaluated by comparing to the ground truth bolus peak time.To evaluate the accuracy of a bolus time‐intensity curve estimated by the network, the cosine similarity between the estimation and the ground truth was computed. The cosine similarity was larger than 0.97 for all projection angles. A view angle corresponding to the x‐ray tube at 30 degrees from vertical (left‐anterior of subject) showed the lowest errors. The amplitude of the estimated bolus curves (in Hounsfield Units) was not always correctly predicted, but the shape was accurately predicted. This resulted in an RMSE of 1.23 s for the left chambers and 0.78 s for the right chambers in the contrast peak time estimation.In this study, we proposed an innovative real‐time way to predict the contrast bolus peak in cardiac CT as well as an innovative approach to train a neural network using virtual but clinically realistic data. Our trained network successfully estimated the shape of the time‐intensity curve for the target chambers, which led to accurate bolus peak time estimation. This technique could be used for autonomous diagnostic cardiac CT to trigger a diagnostic scan for optimal contrast enhancement. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Single Photon Emission Computed Tomography (SPECT) sinogram restoration for low‐dose imaging is a critical challenge in medical imaging. Existing methods often overlook the characteristics of the sinograms, necessitating innovative approaches.In this study, we introduce the Sinogram‐characteristic‐informed network (SCI‐Net) to address the restoration of low‐dose SPECT sinograms. Our aim is to build and train a model based on the characteristics of sinograms, including continuity, periodicity, multi‐scale properties of lines in sinograms, and others, to enhance the model's understanding of the restoration process.SCI‐Net incorporates several novel mechanisms tailored to exploit the inherent characteristics of sinograms. We implement a channel attention module with a decay mechanism to leverage continuity across adjacent sinograms, while a position attention module captures global correlations within individual sinograms. Additionally, we propose a multi‐stage progressive integration mechanism to balance local detail and overall structure. Multiple regularization terms, customized to sinogram image characteristics, are embedded into the loss function for model training.The experimental evaluations are divided into two parts: simulation data evaluation and clinical evaluation. The simulation data evaluation is conducted on a dataset comprising ten organ types, generated by the SIMIND Monte Carlo program from extended cardiac‐torso (XCAT) anatomical phantoms. The dataset includes a total of SPECT sinograms with low‐dose as input data and normal‐dose as ground truth, consisting of 3881 sinograms in the training dataset and 849 sinograms in the testing set. When comparing the restoration of low‐dose sinograms to normal‐dose references, SCI‐Net effectively improves performance. Specifically, the peak signal‐to‐noise ratio (PSNR) and the structural similarity (SSIM) on sinograms increase from 15.72 to 34.66 (p<$\text{p}< $ 0.001) and 0.6297 to 0.9834 (p<$\text{p}<$ 0.001), respectively, and on reconstructed images, reconstructed by maximum likelihood‐expectation maximization (ML‐EM), the PSNR and the SSIM improve from 21.95 to 33.14 (p<$\text{p}<$ 0.001) and 0.9084 to 0.9866 (p<$\text{p}<$ 0.001), respectively. We compared SCI‐Net with existing methods, including baseline models, traditional reconstruction algorithms, end‐to‐end methods, sinogram restoration methods, and image post‐processing methods. The experimental results and visual examples demonstrate that SCI‐Net surpasses these existing methods in SPECT sinogram restoration. The clinical evaluation is conducted on clinical data of low‐dose SPECT sinograms for spleen, thyroid, skull, and bone. These SPECT projection data are obtained from Discovery NM/CT670 scans. We compare the reconstructed images from the SCI‐Net restored sinograms, the reconstructed images from the original low‐dose sinograms, and the reconstructed images using the built‐in algorithm of the Discovery NM/CT670. The results show that our method effectively reduces the coefficient of variation (COV) in the regions of interest (ROI) of the reconstructed images, thereby enhancing the quality of the reconstructed images through SPECT sinogram restoration.Our proposed SCI‐Net exhibits promising performance in the restoration of low‐dose SPECT projection data. In the SCI‐Net, we have implemented three mechanisms based on distinct forms, which are advantageous for the model to more effectively leverage the characteristics of sinograms and achieve commendable restoration outcomes. [ABSTRACT FROM AUTHOR]

The projection data generated via the forward projection of a computed tomography (CT) image (FP-data) have useful potentials in cases where only image data are available. However, there is a question of whether the FP-data generated from an image severely corrupted by metal artifacts can be used for the metal artifact reduction (MAR). The aim of this study was to investigate the feasibility of a MAR technique using FP-data by comparing its performance with that of a conventional robust MAR using projection data normalization (NMARconv). The NMARconv was modified to make use of FP-data (FPNMAR). A graphics processing unit was used to reduce the time required to generate FP-data and subsequent processes. The performances of FPNMAR and NMARconv were quantitatively compared using a normalized artifact index (AIn) for two cases each of hip prosthesis and dental fillings. Several clinical CT images with metal artifacts were processed by FPNMAR. The AIn values of FPNMAR and NMARconv were not significantly different from each other, showing almost the same performance between these two techniques. For all the clinical cases tested, FPNMAR significantly reduced the metal artifacts; thereby, the images of the soft tissues and bones obscured by the artifacts were notably recovered. The computation time per image was ~ 56 ms. FPNMAR, which can be applied to CT images without accessing the projection data, exhibited almost the same performance as that of NMARconv, while consuming significantly shorter processing time. This capability testifies the potential of FPNMAR for wider use in clinical settings. [ABSTRACT FROM AUTHOR]

Breast cancer poses a significant health threat to women, necessitating advancements in diagnostic technologies. Breast dynamic optical imaging (DOI) technology, recognized for its non-invasive and radiation-free properties, is extensively utilized for the early screening and quantitative analysis of breast tumors. The integration of deep learning, a robust technology for automatic image feature extraction, with breast DOI has the potential to enhance tumor detection and diagnosis significantly. This paper introduces a deep learning-enhanced image optimization approach to overcome challenges such as poor image quality and distorted projection data commonly encountered in existing DOI methods. The approach utilizes convolutional neural networks (CNNs) to extract features from raw images and employs generative adversarial networks (GANs) to enhance these images, thereby improving their quality and contrast. Additionally, a novel correction algorithm is developed to address projection data distortion, enabling the reconstruction and correction of this data for more accurate and reliable imaging results. Experimental findings confirm that the proposed method markedly enhances both image quality and projection data accuracy in breast DOI, offering a reliable foundation for clinical diagnosis. This study not only provides a new perspective and methodology for the early screening and diagnosis of breast cancer but also holds substantial clinical importance and prospective applications., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Spectral photon-counting computed tomography (sCT) appears as a promising imaging technique for clinical applications thanks to its ability to offer low dose and possibility of quantitatively analyzing the composition of materials in a pixel. However, due to the dispatching of photons into different energy bins, the quality of sCT image at each energy bin is considerably degraded. We propose a reconstruction method for sCT images by combining multi-energy information. The method is based on clustering pixels containing similar material compositions, performing linear fitting within each class for all the energy images two-by-two, projecting the pixel values of the images at other energy bins to the pixel of the image at the current energy bin, and combining the original pixel value and projected pixel values. The results on both simulation and real data demonstrated the effectiveness of the proposed method, in terms of both image reconstruction quality and material decomposition accuracy.

For X-ray cone-beam computed tomography (CBCT), non-calibrated system geometry causes indistinct and blurring artifacts in reconstructed images, which impacts on the image quality badly. This paper presents a practical industrial CBCT geometric calibration method, which aims to determine the geometric parameter without extra phantom and benefit to efficiency for calibration. In our method, the geometric parameter is divided into system parameter and imaging parameter to be calibrated, respectively. First, the system parameter was determined precisely at the first beginning via average for multiple measurements. Next, the imaging parameter was determined via a designed algorithm in this paper based on the invariance of rotation axis. Furthermore, the proposed method only utilizes the projection data without reconstruction iteration to calibrate geometric parameter, which benefits to efficiency and practical industrial application. 3-D simulation data have been through test with and without noise, and the results show robust and accuracy on the proposed algorithm. In addition, it is also validated with various real data include printed circuit board and industrial components acquired at actual industrial CBCT. The results of reconstructed images demonstrate that the proposed method can achieve comparable image quality in the reconstruction as some phantom-based methods. Furthermore, the advantages and obstacle of the proposed method are analyzed and discussed in this paper.

This study aimed to analyze the temporal sensitivity profile (TSP) shapes and temporal resolutions of computed tomography (CT) and investigate the effect of a combination of a given rotation time and helical pitch on the TSP and image quality. The study was performed on 16- and 64-row CT scanners, and the TSP and temporal resolution were measured using the impulse method. A shorter gantry rotation time and larger helical pitch improved the temporal resolution of both CT scanners. Although the measured TSP shapes were dependent on the helical pitch, the maximum helical pitch did not improve the temporal resolution of either CT scanner. Different combinations of gantry rotation time and helical pitch yielded different TSP shapes such as the triangle, trapezoid, and rectangle, despite the use of equivalent scan speeds. We suggest that the different combinations of rotation time and helical pitch can greatly affect the TSP shapes. [ABSTRACT FROM AUTHOR]