Computed Tomography: Physical Principles and Recent Technical Advances

Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis

Computed tomography (CT) offers advanced biomedical imaging of the body and is broadly utilized for clinical diagnosis. Traditionally, clinical CT scans have not been used for volumetric bone mineral density (vBMD) assessment; however, computational advances can now leverage clinically obtained CT data for the secondary analysis of bone, known as opportunistic CT analysis. Initial applications focused on using clinically acquired CT scans for secondary osteoporosis screening, but opportunistic CT analysis can also be applied to answer research questions related to vBMD changes in response to various disease states. There are several considerations for opportunistic CT analysis, including scan acquisition, contrast enhancement, the internal calibration technique, and bone segmentation, but there remains no consensus on applying these methods. These factors may influence vBMD measures and therefore the robustness of the opportunistic CT analysis. Further research and standardization efforts are needed to establish a consensus and optimize the application of opportunistic CT analysis for accurate and reliable assessment of vBMD in clinical and research settings. This review summarizes the current state of opportunistic CT analysis, highlighting its potential and addressing the associated challenges.

Deep Learning for Medical Image-Based Cancer Diagnosis

(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

Evaluation of silicone rubber-lead shield's effectiveness in protecting the breast during thoracic CT

Radiation of thoracic computed tomography (CT) involves the breast although it is not considered an organ of interest. According to the International Commission on Radiological Protection (ICRP) No. 103, the breast is an organ with a high level of sensitivity when interacting with x-rays, increasing the potential risk of breast cancer. Therefore, the radiation dose must be optimized while maintaining image quality. The dose optimization can be accomplished using a radiation shield. This study aims to determine the effect of silicone rubber (SR)-lead (Pb) in various thicknesses as an alternative protective material limiting dose and preserving the image quality of the breast in thoracic CT. SR-Pb was made from SR and Pb by a simple method. The SR-Pb had thicknesses of 3, 6, 9, and 12 mm. The breast dose was measured using a CT dose profiler on the surface of the breast phantom. The CT number and the noise level of the resulting image were determined quantitatively. The dose without the radiation shield was 5.4 mGy. The doses measured using shielding with thicknesses of 3, 6, 9, and 12 mm were 5.2, 4.5, 4.3, and 3.3 mGy, respectively. Radiation shielding with a thickness of 12 mm reduced breast surface dose by up to 38%. The CT numbers and noise levels for the left and right breast phantom images were almost the same as those ​​without radiation shields indicating there were only slight artifacts in the image. Therefore, SR-Pb is considered a good shielding material which can be pplied in a clinical setting by placing it directly on the breast surface for dose optimization.

Comparison of image quality between a novel mobile CT scanner and current generation stationary CT scanners

PURPOSE

Point-of-care imaging with mobile CT scanners offers several advantages, provided that the image quality is satisfactory. Our aim was to compare image quality of a novel mobile CT to stationary scanners for patients in a neurosurgical intensive care unit (ICU).

METHODS

From November 2020 to April 2021, all patients above 18 years of age examined by a mobile CT scanner at a neurosurgical ICU were included if they also had a stationary head CT examination during the same hospitalization. Quantitative image quality parameters included attenuation and noise in six predefined regions of interest, as well as contrast-to-noise ratio between gray and white matter. Subjective image quality was rated on a 4-garde scale, by four radiologists blinded to scanner parameters.

RESULTS

Fifty patients were included in the final study population. Radiation dose and image attenuation values were similar for mobCT and stationary CTs. There was a small statistically significant difference in subjective quality rating between mobCT and stationary CT images. Two radiologists favored the stationary CT images, one was neutral, and one favored mobCT images. For overall image quality, 14% of mobCT images were rated grade 1 (poor image quality) compared to 8% for stationary CT images.

CONCLUSION

Point-of-care brain CT imaging was successfully performed on clinical neurosurgical ICU patients with small reduction in image quality, predominantly affecting the posterior fossa, compared to high-end stationary CT scanners.

Molecular imaging nanoprobes for theranostic applications

As a non-invasive imaging monitoring method, molecular imaging can provide the location and expression level of disease signature biomolecules in vivo, leading to early diagnosis of relevant diseases, improved treatment strategies, and accurate assessment of treating efficacy. In recent years, a variety of nanosized imaging probes have been developed and intensively investigated in fundamental/translational research and clinical practice. Meanwhile, as an interdisciplinary discipline, this field combines many subjects of chemistry, medicine, biology, radiology, and material science, etc. The successful molecular imaging not only requires advanced imaging equipment, but also the synthesis of efficient imaging probes. However, limited summary has been reported for recent advances of nanoprobes. In this paper, we summarized the recent progress of three common and main types of nanosized molecular imaging probes, including ultrasound (US) imaging nanoprobes, magnetic resonance imaging (MRI) nanoprobes, and computed tomography (CT) imaging nanoprobes. The applications of molecular imaging nanoprobes were discussed in details. Finally, we provided an outlook on the development of next generation molecular imaging nanoprobes.

Automatic computation of bone defective volume from tomographic images

One of the most difficult aims of modern biomaterial science is predicting the shape and volume of a bone defect and adjusting the implementation of a bone substitute. Prior to implantation, practitioners must carefully identify the architecture and volume of the defective bone to be filled. This information is often accessed via imaging techniques. The defective bone is frequently confused with its surroundings and the image background. The use of conventional segmentation for the selection and isolation of the cavity to be filled proves to be difficult. In this work, a defect in a dead bone is created and then imaged with the microtomography technique (343 cuts generated). The goal is to separate the defect's shape and volume from both the bone and the background image. An adaptive morphological operation technique was employed to complete these tasks. The proposed method allows for exact segmentation and calculation of the volume of the cavity to be filled. Using several calculated phantoms, the approach is subjectively and quantitatively evaluated: Compared to the high error value of the conventional method, the error value of the proposed one has no bearing on the overall data. The method's accuracy was also confirmed by comparing the calculated volume of the bone defect (0.91 cm3) and the volume of prepared calcium phosphate cement paste necessary for its filling (0.87 cm3). To challenge the method even further, another direct application on a mandibular bone is realized with an advanced number of cuts (1236 cuts). The result of this application proved that the proposed algorithm overcomes the performance of the classical approaches of segmentation with a gain of 2 min on average. A comparison study between the proposed method and other classical segmentation approaches is also presented. The effectiveness of the method is proved by the various reports and metrics generated. The automated procedure can be beneficial in implantology for realizing and guiding surgical acts, as well as in computer-aided scaffolding techniques.

Acute stroke imaging selection for mechanical thrombectomy in the extended time window: is it time to go back to basics? A review of current evidence

Treatment with endovascular therapy in the extended time window for acute ischaemic stroke with large vessel occlusion involves stringent selection criteria based on the two landmark studies DAWN and DEFUSE3. Current protocols typically include the requirement of advanced perfusion imaging which may exclude a substantial proportion of patients from receiving a potentially effective therapy. Efforts to offer endovascular reperfusion therapies to all appropriate candidates may be facilitated by the use of simplified imaging selection paradigms with widely available basic imaging techniques, such as non-contrast CT and CT angiography. Currently available evidence from our literature review suggests that patients meeting simplified imaging selection criteria may benefit as much as those patients selected using advanced imaging techniques (CT perfusion or MRI) from endovascular therapy in the extended time window. A comprehensive understanding of the role of imaging in patient selection is critical to optimising access to endovascular therapy in the extended time window and improving outcomes in acute stroke. This article provides an overview on current developments and future directions in this emerging area.

Radiation attenuation properties of B2O3-ZnO-Al2O3-Bi2O3-Sm2O3 glasses

The goal of this study is to examine theoretically radiation absorption properties of zinc alumino bismuth borate (BZnAlBiSm) glasses with chemical formula 60B2O3-9ZnO-(30-x)Al2O3-xBi2O3-1Sm2O3 where x = 5, 10, 15 and 20 mol%. The linear and mass attenuation coefficents of BZnAlBiSm glasses turn out as this trend BZnAlBiSm-1<BZnAlBiSm-2<BZnAlBiSm-3<BZnAlBiSm-4. The BZnAlBiSm-4 with the smallest half value layers, tenth value layers and mean free paths in the examined energy ranges has the superior radiation shielding characteristics among the BZnAlBiSm glasses. The BZnAlBiSm-4 glass has the highest radiation protection efficiency among the BZnAlBiSm glasses. The transmission factors increase as photon energy range enhances from 0.015 to 15 MeV and thickness declines from 2.5 to 0.5 cm. The energy buildup factors and energy absorption buildup factors of BZnAlBiSm glasses reduce from 15 to 1 mfp. Variation of the effective atomic number and effective electron density as a function of photon energy for BZnAlBiSm glasses are similar. The projected ranges of electron, proton, alpha and carbon for the BZnAlBiSm glasses increase as photon energy enlarges. The fast neutron removal cross sections of the BZnAlBiSm glasses vary in order of BZnAlBiSm-4<BZnAlBiSm-3<BZnAlBiSm-2<BZnAlBiSm-1. Thus, it can be concluded that BZnAlBiSm-1 has the highest neutron absorption ability among the BZnAlBiSm glasses.

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.

Functional CT Contrast Nanoagents for the Tumor Microenvironment

Understanding the detailed tumor microenvironment (TME) is essential to achieve effective treatment of tumor, because TME has an extremely profound influence on the occurrence, development, invasion, and metastasis of tumor. It is of great significance to realize accurate diagnosis of the TME by using functional computed tomography (CT) contrast nanoagents (FCTNAs). Here, an overview of FCTNAs that respond to the overexpressed receptors, acidic microenvironment, overexpressed glutathione and enzymes, and hypoxia in tumor is provided, and also prospects the advance of novel spectral CT technique to detect the TME precisely. Utilizing FCTNAs is expected to achieve accurate monitoring of the TME and further provide guidance for the effective personalized tumor treatment in clinic.

Image Quality and Dose Comparison of Single-Energy CT (SECT) and Dual-Energy CT (DECT)

CT and its comprehensive usage have become one of the most indispensable components in medical field especially in the diagnosis of several diseases. SECT and DECT have developed CT diagnostic potentials in several means. In this review article we have discussed the basic principles of single-energy and dual-energy computed tomography and their important physical differences which can cause better diagnostic evaluation. Moreover, different organs diagnostic evaluations through single-energy and dual-energy computed tomography have been discussed. Conventional or single-energy CT (SECT) uses a single polychromatic X-ray beam (ranging from 70 to 140 kVp with a standard of 120 kVp) emitted from a single source and received by a single detector. The concept of dual-energy computed tomography (DECT) is almost as old as the CT technology itself; DECT initially required substantially higher radiation doses (nearly two times higher than those employed in single-energy CT) and presented problems associated with spatial misregistration of the two different kV image datasets between the two separate acquisitions. The basic principles of single-energy and dual-energy computed tomography and their important physical differences can cause better diagnostic evaluation. Moreover, different organs diagnostic evaluations through single-energy and dual-energy computed tomography have been discussed. According to diverse data and statistics it is controversial to definitely indicate the accurate comparison of image quality and dose amount.

Effects of image distortion and Hounsfield unit variations on radiation treatment plans: An extended field-of-view reconstruction in a large bore CT scanner

This study aimed to evaluate the effect of image distortion and Hounsfield unit (HU) variation due to the extended field-of-view (eFOV) of the large-bore (LB) computed tomography (CT) on dose distribution. Both home-made inhomogeneity and breast phantoms were scanned at the geometric center position and four different offset positions. We also performed dose optimizations based on different breast phantom CT sets for evaluating the effects of image artifacts on the intensity-modulated radiation techniques. The volume changes were 0.0% to 0.5% in the air, −0.5% to 3.0% in the water, and 4.0% to 5.0% in the high-density material of the inhomogeneity phantom. Both phantoms scanning results indicate that more distortions occurred in the eFOV area due to the biased scanning center. The gamma index differences ranged from 0.87% to 4.87% for the FIF plan and from 0.52% to 6.26% for the VMAT plan. This resulted in decrease of the minimum (7.3–13.1%), maximum (−0.8–2.2%), and mean doses (−0.2–4.4%). We recommend that it should be evaluated whether the applied CT would have an appropriate eFOV range for clinical radiation treatment planning for patients.

Improved Simultaneous Algebraic Reconstruction Technique Algorithm for Positron-Emission Tomography Image Reconstruction via Minimizing the Fast Total Variation

CONTEXT

There has been considerable progress in the instrumentation for data measurement and computer methods for generating images of measured PET data. These computer methods have been developed to solve the inverse problem, also known as the "image reconstruction from projections" problem.

AIM

In this paper, we propose a modified Simultaneous Algebraic Reconstruction Technique (SART) algorithm to improve the quality of image reconstruction by incorporating total variation (TV) minimization into the iterative SART algorithm.

METHODOLOGY

The SART updates the estimated image by forward projecting the initial image onto the sinogram space. Then, the difference between the estimated sinogram and the given sinogram is back-projected onto the image domain. This difference is then subtracted from the initial image to obtain a corrected image. Fast total variation (FTV) minimization is applied to the image obtained in the SART step. The second step is the result obtained from the previous FTV update. The SART and the FTV minimization steps run iteratively in an alternating manner. Fifty iterations were applied to the SART algorithm used in each of the regularization-based methods. In addition to the conventional SART algorithm, spatial smoothing was used to enhance the quality of the image. All images were sized at 128 × 128 pixels.

RESULTS

The proposed algorithm successfully accomplished edge preservation. A detailed scrutiny revealed that the reconstruction algorithms differed; for example, the SART and the proposed FTV-SART algorithm effectively preserved the hot lesion edges, whereas artifacts and deviations were more likely to occur in the ART algorithm than in the other algorithms.

CONCLUSIONS

Compared to the standard SART, the proposed algorithm is more robust in removing background noise while preserving edges to suppress the existent image artifacts. The quality measurements and visual inspections show a significant improvement in image quality compared to the conventional SART and Algebraic Reconstruction Technique (ART) algorithms.

Computed gray levels in multislice and cone-beam computed tomography

INTRODUCTION

Gray level is the range of shades of gray in the pixels, representing the x-ray attenuation coefficient that allows for tissue density assessments in computed tomography (CT). An in-vitro study was performed to investigate the relationship between computed gray levels in 3 cone-beam CT (CBCT) scanners and 1 multislice spiral CT device using 5 software programs.

METHODS

Six materials (air, water, wax, acrylic, plaster, and gutta-percha) were scanned with the CBCT and CT scanners, and the computed gray levels for each material at predetermined points were measured with OsiriX Medical Imaging software (Geneva, Switzerland), OnDemand3D (CyberMed International, Seoul, Korea), E-Film (Merge Healthcare, Milwaukee, Wis), Dolphin Imaging (Dolphin Imaging & Management Solutions, Chatsworth, Calif), and InVivo Dental Software (Anatomage, San Jose, Calif). The repeatability of these measurements was calculated with intraclass correlation coefficients, and the gray levels were averaged to represent each material. Repeated analysis of variance tests were used to assess the differences in gray levels among scanners and materials.

RESULTS

There were no differences in mean gray levels with the different software programs. There were significant differences in gray levels between scanners for each material evaluated (P <0.001).

CONCLUSIONS

The software programs were reliable and had no influence on the CT and CBCT gray level measurements. However, the gray levels might have discrepancies when different CT and CBCT scanners are used. Therefore, caution is essential when interpreting or evaluating CBCT images because of the significant differences in gray levels between different CBCT scanners, and between CBCT and CT values.