New Frontiers in X-ray Technologies for Medical Research: Image Analysis and Disease Discovered

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 4967

Special Issue Editor


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Guest Editor
Inst Phys, University of Silesia in Katowice, 40-007 Katowice, Poland
Interests: X-ray; radiotherapy; medical physics; Monte Carlo simulation; radiation measurements

Special Issue Information

Dear Colleagues,

Today, medical imaging is an essential component of healthcare. Technologies that use X-rays are of particular importance due to their dynamic development. An example is computed tomography (CT). Technological advances in CT have made it possible to increase the scanning speed, to reduce the thickness of the imaged layers, to reduce the radiation dose absorbed in tissues and to improve image quality. Perfusion scanning has entered into medical imaging to enable detection and quantification of cerebral stroke. The combination of CT perfusion and CT angiography has revolutionized the world of stroke therapy. Another frontier is cardiac CT with coronary CT angiography (CTA). This method displays the anatomical detail of blood vessels more precisely than magnetic resonance imaging (MRI) or ultrasound (US). Today, it is well known that exposure to ionizing radiation from CT and other diagnostic methods increases the risk of malignant tumors. Therefore, one of the challenges of modern radiology is to minimize the dose with the possibly lossless quality of the obtained images. The future of computed tomography including radiation dose reduction is spectral (multi-energy) tomography. Spectral CT uses single acquisitions performed at multiple energies to extract more information about tissue differentiation based on the difference in absorption of photons of different energy in different tissues. Another promising achievement is the combination of CT and US or MRI or positron emission tomography (PET), where different scans obtained by the different methods based on various physical processes are jointly recorded. Such a fusion makes it possible to obtain complete diagnostic information. The new concept is now the so-called theranostics which is a combination of diagnostics and therapy, consisting of the creation of a single technology that both locates and treats cancers. Advances in medical imaging in recent decades was possible not only by the technological advances, but also by the digital revolution, including advances in software and hardware. It became possible to process huge amounts of data and create multi-plane and three-dimensional image reconstructions. An innovative solution is the use of advanced computational methods in analysis and interpretation of image, based on the Monte Carlo simulations or artificial intelligence algorithms.

Dr. Adam KonefaŁ
Guest Editor

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Keywords

  • X-rays
  • computed tomography
  • CT perfusion
  • coronary CT angiography
  • medical imaging
  • radiology
  • theranostics
  • deep learning
  • Monte Carlo modeling

Published Papers (3 papers)

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Research

15 pages, 9671 KiB  
Article
Development of a Method for Estimating the Angle of Lumbar Spine X-ray Images Using Deep Learning with Pseudo X-ray Images Generated from Computed Tomography
by Ryuma Moriya, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa and Hiroyuki Sugimori
Appl. Sci. 2024, 14(9), 3794; https://doi.org/10.3390/app14093794 - 29 Apr 2024
Viewed by 347
Abstract
Background and Objectives: In lumbar spine radiography, the oblique view is frequently utilized to assess the presence of spondylolysis and the morphology of facet joints. It is crucial to instantly determine whether the oblique angle is appropriate for the evaluation and the necessity [...] Read more.
Background and Objectives: In lumbar spine radiography, the oblique view is frequently utilized to assess the presence of spondylolysis and the morphology of facet joints. It is crucial to instantly determine whether the oblique angle is appropriate for the evaluation and the necessity of retakes after imaging. This study investigates the feasibility of using a convolutional neural network (CNN) to estimate the angle of lumbar oblique images. Since there are no existing lumbar oblique images with known angles, we aimed to generate synthetic lumbar X-ray images at arbitrary angles from computed tomography (CT) images and to estimate the angles of these images using a trained CNN. Methods: Synthetic lumbar spine X-ray images were created from CT images of 174 individuals by rotating the lumbar spine from 0° to 60° in 5° increments. A line connecting the center of the spinal canal and the spinous process was used as the baseline to define the shooting angle of the synthetic X-ray images based on how much they were tilted from the baseline. These images were divided into five subsets and trained using ResNet50, a CNN for image classification, implementing 5-fold cross-validation. The models were trained for angle estimation regression and image classification into 13 classes at 5° increments from 0° to 60°. For model evaluation, mean squared error (MSE), root mean squared error (RMSE), and the correlation coefficient (r) were calculated for regression analysis, and the area under the curve (AUC) was calculated for classification. Results: In the regression analysis for angles from 0° to 60°, the MSE was 14.833 degree2, the RMSE was 3.820 degrees, and r was 0.981. The average AUC for the 13-class classification was 0.953. Conclusion: The CNN developed in this study was able to estimate the angle of an lumbar oblique image with high accuracy, suggesting its usefulness. Full article
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18 pages, 4656 KiB  
Article
Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques
by Kousuke Usui, Takaaki Yoshimura, Shota Ichikawa and Hiroyuki Sugimori
Appl. Sci. 2023, 13(11), 6695; https://doi.org/10.3390/app13116695 - 31 May 2023
Viewed by 2193
Abstract
Although the widespread use of digital imaging has enabled real-time image display, images in chest X-ray examinations can be confirmed by the radiologist’s eyes. Considering the development of deep learning (DL) technology, its application will make it possible to immediately determine the need [...] Read more.
Although the widespread use of digital imaging has enabled real-time image display, images in chest X-ray examinations can be confirmed by the radiologist’s eyes. Considering the development of deep learning (DL) technology, its application will make it possible to immediately determine the need for a retake, which is expected to further improve examination throughput. In this study, we developed software for evaluating chest X-ray images to determine whether a repeat radiographic examination is necessary, based on the combined application of DL technologies, and evaluated its accuracy. The target population was 4809 chest images from a public database. Three classification models (CLMs) for lung field defects, obstacle shadows, and the location of obstacle shadows and a semantic segmentation model (SSM) for the lung field regions were developed using a fivefold cross validation. The CLM was evaluated using the overall accuracy in the confusion matrix, the SSM was evaluated using the mean intersection over union (mIoU), and the DL technology-combined software was evaluated using the total response time on this software (RT) per image for each model. The results of each CLM with respect to lung field defects, obstacle shadows, and obstacle shadow location were 89.8%, 91.7%, and 91.2%, respectively. The mIoU of the SSM was 0.920, and the software RT was 3.64 × 10−2 s. These results indicate that the software can immediately and accurately determine whether a chest image needs to be re-scanned. Full article
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13 pages, 2423 KiB  
Article
Optimization of Image Quality in Digital Mammography with the Response of a Selenium Detector by Monte Carlo Simulation
by Marek Szewczuk and Adam Konefał
Appl. Sci. 2023, 13(1), 171; https://doi.org/10.3390/app13010171 - 23 Dec 2022
Cited by 1 | Viewed by 1719
Abstract
Mammography machines must meet high requirements to ensure the quality of the generated images. On the other hand, due to the use of ionizing radiation, there is a need to minimize the dose received by patients. To optimize both of these parameters (dose [...] Read more.
Mammography machines must meet high requirements to ensure the quality of the generated images. On the other hand, due to the use of ionizing radiation, there is a need to minimize the dose received by patients. To optimize both of these parameters (dose and image quality), the response characteristics of image detectors and, depending on the composition of the breasts, the physical contrast of the examined structures should be considered. This study aimed to determine the optimal voltage values for a given breast thickness during imaging with the use of a selenium image detector. Analysis was carried out using the Monte Carlo simulation method with the GEANT4 code. Our results reveal that the combination of Mo anode together with Mo filtration (the system recommended in analog mammography) was the least favorable combination among those used in digital mammography machines with a selenium detector. Moreover, the use of Rh filtration instead of Mo was advantageous regardless of the thickness of the breast and resulted in a significant improvement in image quality with the same dose absorbed in the breast. The most advantageous solution was found to be an X-ray tube with a W anode. The highest values of the image quality-to-dose ratio were observed for breasts with dimensions ranging from 53 mm to 60 mm in thickness. Lower image quality was observed for breasts with smaller dimensions due to high breast glandularity, resulting in the deterioration of the physical contrast. Full article
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