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Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m2, significantly higher than the 2514.06 ± 691.59 µGym2 obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts. [ABSTRACT FROM AUTHOR]

In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp–Davis–Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately 2.8°. Moreover, based on BRISQUE results, we confirm that the Shepp–Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images. [ABSTRACT FROM AUTHOR]

Metal artifacts notoriously pose significant challenge in computed tomography (CT), leading to inaccuracies in image formation and interpretation. Artifact reduction tools have been designed to improve cone beam computed tomography (CBCT) image quality by reducing artifacts caused by certain high-density materials. Metal artifact reduction (MAR) tools are specific algorithms that are applied during image reconstruction to minimize or eliminate artifacts degrading CBCT images. The purpose of the study is to evaluate the effect of a MAR algorithm on image quality in CBCT performed for evaluating patients before transarterial radioembolization (TARE). We retrospectively included 40 consecutive patients (aged 65 ± 13 years; 23 males) who underwent 45 CBCT examinations (Allura FD 20, XperCT Roll protocol, Philips Healthcare, Best, The Netherlands) in the setting of evaluation for TARE between January 2017 and December 2018. Artifacts caused by coils, catheters, and surgical clips were scored subjectively by four readers on a 5-point scale (1 = artifacts affecting diagnostic information to 5 = no artifacts) using a side-by-side display of uncorrected and MAR-corrected images. In addition, readers scored tumor visibility and vessel discrimination. MAR-corrected images were assigned higher scores, indicating better image quality. The differences between the measurements with and without MAR were most impressive for coils with a mean improvement of 1.6 points (95%CI [1.5 1.8]) on the 5-point likert scale, followed by catheters 1.4 points (95%CI [1.3 1.5]) and clips 0.7 points (95%CI [0.3 1.1]). Improvements for other artifact sources were consistent but relatively small (below 0.25 points on average). Interrater agreement was good to perfect (Kendall's W coefficient = 0.68–0.95) and was higher for MAR-corrected images, indicating that MAR improves diagnostic accuracy. A metal artifact reduction algorithm can improve diagnostic and interventional accuracy of cone beam CT in patients undergoing radioembolization by reducing artifacts caused by diagnostic catheters and coils, lowering interference of metal artifacts with adjacent major structures, and improving tumor visibility. [ABSTRACT FROM AUTHOR]

Background: High-quality images with minimum radiation dose are considered a challenge in Computed Tomography (CT) scans. Objective: The current study aimed to assess the efficacy of the Iterative Reconstruction in Image Space (IRIS) algorithm combined with Automatic Tube Current Modulation (ATCM) compared to Filtered Back Projection (FBP) in brain CT scans. Material and Methods: In this cross-sectional study, 200 patients underwent to brain CT scan, and images were then reconstructed using both FBP and IRIS. The CT Number (CTN), noise, and Signal-to-Noise Ratio (SNR) were computed for different tissues from CT images. The performance of two algorithms under different exposure conditions was evaluated using a water phantom. Two experienced radiologists assessed the image quality. Volume CT Dose Index (CTDIvol) and Dose Length Product (DLP) were recorded for each scan. Results: FBP reconstruction exhibited higher noise and lower SNR compared to IRIS, both with and without ATCM. Noise levels significantly increased for FBP combined with ATCM. Subjective analysis showed higher performance for IRIS without ATCM compared to other approaches. The mean CTDIvol with and without ATCM was 20.04±3.33 and 36.37±4.65 mGy, respectively. In the phantom study, the noise with IRIS remained lower than that with FBP even with a 42% dose reduction. Conclusion: IRIS algorithm can preserve the image quality when radiation dose is significantly reduced by ATCM in brain CT scan. Implementation of IRIS combined with ATCM is recommended for brain CT examinations. [ABSTRACT FROM AUTHOR]

Ultrafast X-ray computed tomography is an advanced imaging technique for multiphase flows. It has been used with great success for studying gas–liquid as well as gas–solid flows. Here, we apply this technique to analyze density-driven particle segregation in a rotating drum as an exemplary use case for analyzing industrial particle mixing systems. As glass particles are used as the denser of two granular species to be mixed, beam hardening artefacts occur and hamper the data analysis. In the general case of a distribution of arbitrary materials, the inverse problem of image reconstruction with energy-dependent attenuation is often ill-posed. Consequently, commonly known beam hardening correction algorithms are often quite complex. In our case, however, the number of materials is limited. We therefore propose a correction algorithm simplified by taking advantage of the known material properties, and demonstrate its ability to improve image quality and subsequent analyses significantly. [ABSTRACT FROM AUTHOR]

Purpose: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). Methods: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. Results: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P <.001). In general, DLIR showed superior subjective image quality for ULDCT images (P <.001) and comparable quality for LDCT images compared to ASIR-V50 (P =.01–1). The per-nodule sensitivities of observers for Lung-RADS category 3–4 nodules were 70.6–88.2% and 64.7–82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P =.50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P =.65). Conclusion: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) is a method for synthesizing volumetric or cross-sectional images of an object from a collection of projections. Popular reconstruction methods for CT are based on the assumption that the projection geometry, which describes the relative location and orientation of the radiation source, object, and detector for each projection, is exactly known. However, in practice, these geometric parameters are estimated quantities with uncertainty. A failure to accurately estimate the geometry may lead to reconstructions with severe misalignment artifacts that significantly decrease their scientific or diagnostic value. We propose a novel reconstruction method that jointly estimates the reconstruction and the center-of-rotation offset for fan-beam tomography. The reconstruction method is based on a Bayesian approach that yields a point estimate for the reconstruction and center-of-rotation offset and, in addition, provides valuable information regarding their uncertainty. This is achieved by approximately sampling from the joint posterior distribution of the reconstruction and center-of-rotation offset using a hierarchical Gibbs sampler. Our methodology is highly flexible and can be adapted to other geometric parameters and/or scan modes. Through numerical experiments based on real tomographic data, we compare the proposed Bayesian method to two existing approaches to the problem of correcting the geometry and demonstrate that our method achieves comparable or better results under challenging conditions. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) has been developed as a nondestructive technique for observing minute internal images in samples. It has been difficult to obtain photorealistic (clean or clear) CT images due to various unwanted artifacts generated during the CT scanning process, along with the limitations of back-projection algorithms. Recently, an iterative optimization algorithm has been developed that uses an entire sinogram to reduce errors caused by artifacts. In this paper, we introduce a new quantum algorithm for reconstructing CT images. This algorithm can be used with any type of light source as long as the projection is defined. Assuming an experimental sinogram produced by a Radon transform, to find the CT image of this sinogram, we express the CT image as a combination of qubits. After acquiring the Radon transform of the undetermined CT image, we combine the actual sinogram and the optimized qubits. The global energy optimization value used here can determine the value of qubits through a gate model quantum computer or quantum annealer. In particular, the new algorithm can also be used for cone-beam CT image reconstruction and for medical imaging. [ABSTRACT FROM AUTHOR]

Dental CBCT and panoramic imaging play a pivotal role in dental diagnosis and treatment planning, alongside the indispensable use of computed tomography (CT) and X-ray imaging in dentistry, particularly for surgical planning. Given the widespread adoption of dental cone beam CT in clinics today, we explore a novel approach in this study—utilizing CT's three-dimensional (3D) data to reconstruct a two-dimensional (2D) panoramic dental image. This method eliminates the requirement for an extra panoramic scan. In this work, we propose a novel framework to generate an enhanced and extended 2D panoramic view by using the dental arch extracted from 3D CBCT. Our method involves segmenting the patient's dental arch from their 3D CBCT image by identifying horizontal slices with above-average intensity, followed by morphological operations, including dilation, Gaussian filtering, and skeletonization, to delineate the dental arch line. Additionally, we extend the dental arch beyond the wisdom teeth using quadratic curve fitting. Finally, we employ Maximum Intensity Projection on rotated cubic segments aligned with the dental arch curve to produce captivating panoramic images. The panoramic view produced using our proposed method, when compared to the results obtained from BlueSky and OpenInventor, exhibited superior enhancements and greater accuracy in panoramic visualization. [ABSTRACT FROM AUTHOR]

Refusal of autopsy occurs for various reasons, including religious beliefs and the risk of infectious diseases such as coronavirus disease 2019. Dental autopsy involves invasive procedures, including incision and access openings in the oral cavity. Radiographic techniques can be used as an alternative to the conventional autopsy process in the field of forensic odontology, providing a non-invasive approach that does not involve tissue damage. The current study aimed to analyse the current status of the applicability of oral and maxillofacial radiology for virtual forensic odontological autopsy. A systematic review was conducted in accord with the Preferred Reporting Items for Systematic Review and Meta-Analysis protocol. The literature search was conducted from December 2021 to October 2022 using the PubMed, ScienceDirect, and SAGE Journals databases. Article selection was carried out by eliminating duplication, screening titles and abstracts, and reading the entire content of the article. A thematic analysis method was used to identify themes in the collected data. A total of 15 articles were included, and several uses of oral and maxillofacial radiography in virtual autopsy and forensic odontology were identified. Four techniques were identified that can be used for virtual autopsy in forensic odontology. The use of computed tomography (CT) scanning for virtual autopsy was reported in six articles, cone beam CT was reported in five articles, magnetic resonance imaging was reported in two articles, and multidetector CT was reported in two articles. In the studies included in this review, the identified oral and maxillofacial radiograph techniques that are used as the fundamental methods of virtual autopsy in forensic odontology are CT scanning (including multidetector CT), cone beam CT, and magnetic resonance imaging. The different methods of oral and maxillofacial radiography for virtual autopsy were identified as having advantages as well as limitations in their use in forensic odontology. Most of the included studies indicated that the virtual autopsy approach cannot yet stand alone as an identification method, but provides a useful adjunct for gathering dental evidence. Key points Virtual autopsy is a valuable tool for non-invasive autopsies in the field of forensic odontology. CT scans (including MDCT), CBCT, and MRI can be used for virtual autopsy in the identification of individuals based on dental evidence. The use of oral and maxillofacial radiographs can shorten the time required for forensic odontology activities. Artefacts caused by amalgam and other metal restorations on radiographic images can raise doubts in the identification process. Although the use of oral and maxillofacial radiology has many advantages, it cannot yet stand alone as an identification method. [ABSTRACT FROM AUTHOR]

Background: This paper describes the protocol for the Nano X Image Guidance (Nano X IG) trial, a single-institution, clinical imaging study. The Nano X is a prototype fixed-beam radiotherapy system developed to investigate the feasibility of a low-cost, compact radiotherapy system to increase global access to radiation therapy. This study aims to assess the feasibility of volumetric image guidance with cone beam computed tomography (CBCT) acquired during horizontal patient rotation on the Nano X radiotherapy system. Methods: In the Nano X IG study, we will determine whether radiotherapy image guidance can be performed with the Nano X radiotherapy system where the patient is horizontally rotated while scan projections are acquired. We will acquire both conventional CBCT scans and Nano X CBCT scans for 30 patients aged 18 and above and receiving radiotherapy for head/neck or upper abdomen cancers. For each patient, a panel of experts will assess the image quality of Nano X CBCT scans against conventional CBCT scans. Each patient will receive two Nano X CBCT scans to determine the image quality reproducibility, the extent and reproducibility of patient motion and assess patient tolerance. Discussion: Fixed-beam radiotherapy systems have the potential to help ease the current shortfall and increase global access to radiotherapy treatment. Advances in image guidance could facilitate fixed-beam radiotherapy using horizontal patient rotation. The efficacy of this radiotherapy approach is dependent on our ability to image and adapt to motion due to rotation and for patients to tolerate rotation during treatment. Trial registration: ClinicalTrials.gov, NCT04488224. Registered on 27 July 2020. [ABSTRACT FROM AUTHOR]

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient's dental arch from the patient's CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image. [ABSTRACT FROM AUTHOR]

Simple Summary: Cone-beam computed tomography (CBCT) not only plays an important role in image-guided radiation therapy (IGRT) but also has the potential for dose calculation. Because CBCT suffers from poor image quality and uncertainties in the Hounsfield unit (HU) values, the accuracy of dose calculation with CBCT is insufficient for clinical use. This study investigated deep learning approaches that utilize a generative adversarial network (GAN) with an additional registration network (RegNet) to generate synthetic CT (sCT) from CBCT. Our study addressed the limitation of having paired CT and CBCT with their anatomy perfectly aligned for supervised training. RegNet can dynamically estimate the correct labels, enabling supervised learning with noisy labels, whereas GAN learns the bidirectional mapping from CBCT to CT. The HU values for sCT were sufficiently accurate for dose calculation, while preserving the anatomy of CBCT with clear structural boundaries. Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient's anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to use cone beam computed tomography (CBCT) to (1) define the virtual valid length of pterygoid implants in maxillary atrophic patients from the prosthetic prioritized driven position and (2) measure the implant length engaged in the pterygoid process according to the HU difference of the pterygoid maxillary junction. Materials and methods: Virtual pterygoid implants were planned with CBCT of maxillary atrophic patients in the software. The entry and angulation of the implant were planned according to the prosthetic prioritized driven position in the 3D reconstruction image. The planned implant length and the valid length defined as the implant between the pterygoid maxillary junction and pterygoid fossa were recorded. The relationship between the implant and sinus cavity was also evaluated. Results: A total of 120 CBCT samples were enrolled and virtually planned. The mean age of the patients was 56.2 ± 13.2 years. One hundred and sixteen samples could successfully place virtual implants according to the criterion. The mean implant length and mean implant length beyond the pterygoid maxillary junction were 16.3 ± 4.2 mm (range, 11.5–18 mm) and 7.1 ± 3.3 mm (range, 1.5–11.4 mm), respectively. Ninety percent of virtually planned implants had a close relationship with the sinus cavity, and implants exhibited longer lengths when they had no relation with the sinus. Conclusion: From a prosthetic prioritized driven position with fixed entry and angulation, pterygoid implants achieve adequate bone anchorage length beyond the pterygoid maxillary junction. Due to the individual anatomy and the volume of the maxillary sinus, the implants presented a different positional relationship with the maxillary sinus. [ABSTRACT FROM AUTHOR]

In-house fabrication of three-dimensional (3D) models for medical use has become easier in recent years. Cone beam computed tomography (CBCT) images are increasingly used as source data for fabricating osseous 3D models. The creation of a 3D CAD model begins with the segmentation of hard and soft tissues of the DICOM images and the creation of an STL model; however, it can be difficult to determine the binarization threshold in CBCT images. In this study, how the different CBCT scanning and imaging conditions of two different CBCT scanners affect the determination of the binarization threshold was evaluated. The key to efficient STL creation through voxel intensity distribution analysis was then explored. It was found that determination of the binarization threshold is easy for image datasets with a large number of voxels, sharp peak shapes, and narrow intensity distributions. Although the intensity distribution of voxels varied greatly among the image datasets, it was difficult to find correlations between different X-ray tube currents or image reconstruction filters that explained the differences. The objective observation of voxel intensity distribution may contribute to the determination of the binarization threshold for 3D model creation. [ABSTRACT FROM AUTHOR]

Background: Rapid kV cone-beam computed tomography (CBCT) scans are achievable in under 20 s on select linear accelerator systems to generate volumetric images in three dimensions (3D). Daily pre-treatment four-dimensional CBCT (4DCBCT) is recommended in image-guided lung radiotherapy to mitigate the detrimental effects of respiratory motion on treatment quality. Purpose: To demonstrate the potential for thoracic 4DCBCT reconstruction using projection data that was simulated using a clinical rapid 3DCBCT acquisition protocol. Methods: We simulated conventional (1320 projections over 4 min) and rapid (491 projections over 16.6 s) CBCT acquisitions using 4D computed tomography (CT) volumes of 14 lung cancer patients. Conventional acquisition data were reconstructed using the 4D Feldkamp-Davis-Kress (FDK) algorithm. Rapid acquisition data were reconstructed using 3DFDK, 4DFDK, and MotionCompensated FDK (MCFDK). Image quality was evaluated using Contrastto-Noise Ratio (CNR), Tissue Interface Width (TIW), Root-Mean-Square Error (RMSE), and Structural SIMilarity (SSIM). Results: The conventional acquisition 4DFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.96, 8.02 mm, 83.5, and 0.54, respectively. The rapid acquisition 3DFDK reconstructions had median CNR, TIW, RMSE, and SSIM of 2.99, 13.6 mm, 112, and 0.44 respectively. The rapid acquisition MCFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.98, 10.2 mm, 103, and 0.46, respectively. Rapid acquisition 4DFDK reconstruction quality was insufficient for any practical use due to sparse angular projection sampling. Conclusions: Results suggest that 4D motion-compensated reconstruction of rapid acquisition thoracic CBCT data are feasible with image quality approaching conventional acquisition CBCT data reconstructed using standard 4DFDK. [ABSTRACT FROM AUTHOR]

Background: Children have a potential risk from radiation exposure because they are more sensitive to radiation than adults. Objective: The purpose of this work is to estimate image quality according to tube voltage (kV) and radiation dose in pediatric computed tomography (CT) using deep learning reconstruction (DLR). Methods: Phantom images of children and adults were obtained for kV, radiation dose, and image reconstruction methods. The CT emits a fan beam to the opposite detector, and the geometry of the detector was symmetrical. Phantom images of children and adults were acquired at a volume CT dose index (CTDIvol) from 0.5 to 10.0 mGy for tube voltages at 80, 100, and 120 kV. A DLR was used to reconstruct the phantom image, and filtered back projection (FBP) and iterative reconstruction (IR) were also performed for comparison with the DLR. Image quality was evaluated by measuring the contrast-to-noise ratio (CNR) and noise. Results: Under the same imaging conditions, the DLR images of pediatric and adult phantoms generally provided improved CNR and noise compared with the FBP and IR images. At a similar CNR and noise, the FBP, IR, and DLR of the pediatric images showed a dose reduction compared with the FBP, IR, and DLR of the adult images, respectively. In terms of the effect of tube voltage, the CNR of the 100 kV DLR images was higher than that of the 120 kV DLR images. Conclusion: According to the results, since pediatric CT images maintain the same image quality at lower doses compared with adult CT images, DLR can improve image quality while reducing the radiation dose in children's abdominal CT scans. [ABSTRACT FROM AUTHOR]

Featured Application: The development of this co-registration method that combines CBCT with photographic images and subsequent realistic image reconstruction can be used during the orthognathic surgical planning stage. Therefore, both orthodontists and maxillofacial surgeons will obtain more realistic and precise result predictions, allowing them to better clarify the patient's surgical prognosis. Orthognathic surgery is a procedure used to correct intermaxillary discrepancies, thus promoting significant improvements in chewing and breathing. During the surgical planning stage, orthodontists often use two-dimensional imaging techniques. The assessment is based on CBCT images and dental cast models to overcome these limitations; however, the evaluation of soft tissues remains complex. The aim of the present study was to develop a co-registration method of CBCT and photo images that would result in realistic facial image reconstruction. CBCT images were three-dimensionally rendered, and the soft tissues were subsequently segmented resulting in the cranial external surface. A co-registration between the obtained surface and a frontal photo of the subject was then carried out. From this mapping, a photorealistic model capable of replicating the features of the face was generated. To assess the quality of this procedure, seven orthodontists were asked to fill in a survey on the models obtained. The survey results showed that orthodontists consider the three-dimensional model obtained to be realistic and of high quality. This process can automatically obtain a three-dimensional model from CBCT images, which in turn may enhance the predictability of surgical-orthognathic planning. [ABSTRACT FROM AUTHOR]

Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. First, it automatically generates upper and lower jaws panoramic images to overcome the computational complexity caused by high-dimensional data and the curse of dimensionality associated with limited training dataset. The obtained 2D panoramic images are then used to identify 2D individual teeth and capture loose- and tight- regions of interest (ROIs) of 3D individual teeth. Finally, accurate 3D individual tooth segmentation is achieved using both loose and tight ROIs. Experimental results showed that the proposed method achieved an F1-score of 93.35 percent for tooth identification and a Dice similarity coefficient of 94.79 percent for individual 3D tooth segmentation. The results demonstrate that the proposed method provides an effective clinical and practical framework for digital dentistry. [ABSTRACT FROM AUTHOR]

Background: Iterative reconstruction for cone-beam computed tomography (CBCT) has been applied to improve image quality and reduce radiation dose. In a case where an object's actual projection is larger than a flat panel detector, CBCT images contain truncated data or incomplete projections, which degrade image quality inside the field of view (FOV). In this work, we propose truncation effect reduction for fast iterative reconstruction in CBCT imaging. Methods: The volume matrix size of the FOV and the height of projection images were extrapolated to a suitable size. These extended projections were reconstructed by fast iterative reconstruction. Moreover, a smoothing parameter for noise regularization in iterative reconstruction was modified to reduce the accumulated error while processing. The proposed work was evaluated by image quality measurements and compared with conventional filtered backprojection (FBP). To validate the proposed method, we used a head phantom for evaluation and preliminarily tested on a human dataset. Results: In the experimental results, the reconstructed images from the head phantom showed enhanced image quality. In addition, fast iterative reconstruction can be run continuously while maintaining a consistent mean-percentage-error value for many iterations. The contrast-to-noise ratio of the soft-tissue images was improved. Visualization of low contrast in the ventricle and soft-tissue images was much improved compared to those from FBP using the same dose index of 5 mGy. Conclusions: Our proposed method showed satisfactory performance to reduce the truncation effect, especially inside the FOV with better image quality for soft-tissue imaging. The convergence of fast iterative reconstruction tends to be stable for many iterations. [ABSTRACT FROM AUTHOR]

Panoramic images have been widely used in the diagnosis of dental diseases. In the process of panoramic image reconstruction, the position of the dental arch curve usually affects the quality of display content, especially the completion level of the panoramic image. In addition, the metal implants in the patient's mouth often lead the contrast of the panoramic image to decrease. This paper describes a method to automatically synthesize panoramic images from dental cone beam computed tomography (CBCT) data. The proposed method has two essential features: the first feature is that the method can detect the dental arch curve through axial maximum intensity projection images over different ranges, and the second feature is that our method is able to adjust the intensity distribution of the implant in critical areas, to reduce the impact of the implant on the contrast of the panoramic image. The proposed method was tested on 50 CBCT datasets; the panoramic images generated by this method were compared with images attained from three other commonly used approaches and then subjectively scored by three experienced dentists. In the comprehensive image contrast score, the method in this paper has the highest score of 11.16 ± 2.64 points. The results show that the panoramic images generated by this method have better image contrast. [ABSTRACT FROM AUTHOR]

Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient’s anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT.

Bone tissue engineering strategies aimed at treating critical-sized craniofacial defects often utilize novel biomaterials and scaffolding. Rapid manufacturing of defect-matching geometries using 3D-printing strategies is a promising strategy to treat craniofacial bone loss to improve aesthetic and regenerative outcomes. To validate manufacturing quality, a robust, three-dimensional quality assurance pipeline is needed to provide an objective, quantitative metric of print quality if porous scaffolds are to be translated from laboratory to clinical settings. Previously published methods of assessing scaffold print quality utilized one- and two-dimensional measurements (e.g., strut widths, pore widths, and pore area) or, in some cases, the print quality of a single phantom is assumed to be representative of the quality of all subsequent prints. More robust volume correlation between anatomic shapes has been accomplished; however, it requires manual user correction in challenging cases such as porous objects like bone scaffolds. Here, we designed porous, anatomically-shaped scaffolds with homogenous or heterogenous porous structures. We 3D-printed the designs with acrylonitrile butadiene styrene (ABS) and used cone-beam computed tomography (CBCT) to obtain 3D image reconstructions. We applied the iterative closest point algorithm to superimpose the computational scaffold designs with the CBCT images to obtain a 3D volumetric overlap. In order to avoid false convergences while using an autonomous workflow for volumetric correlation, we developed an independent iterative closest point (I-ICP10) algorithm using MATLAB®, which applied ten initial conditions for the spatial orientation of the CBCT images relative to the original design. Following successful correlation, scaffold quality can be quantified and visualized on a sub-voxel scale for any part of the volume. [ABSTRACT FROM AUTHOR]

Micro cone-beam computed tomography (µCBCT) imaging is of utmost importance for carrying out extensive preclinical research in rodents. The imaging of animals is an essential step prior to preclinical precision irradiation, but also in the longitudinal assessment of treatment outcomes. However, imaging artifacts such as beam hardening will occur due to the low energetic nature of the X-ray imaging beam (i.e., 60 kVp). Beam hardening artifacts are especially difficult to resolve in a 'pancake' imaging geometry with stationary source and detector, where the animal is rotated around its sagittal axis, and the X-ray imaging beam crosses a wide range of thicknesses. In this study, a seven-layer U-Net based network architecture (vMonoCT) is adopted to predict virtual monoenergetic X-ray projections from polyenergetic X-ray projections. A Monte Carlo simulation model is developed to compose a training dataset of 1890 projection pairs. Here, a series of digital anthropomorphic mouse phantoms was derived from the reference DigiMouse phantom as simulation geometry. vMonoCT was trained on 1512 projection pairs (= 80%) and tested on 378 projection pairs (= 20%). The percentage error calculated for the test dataset was 1.7 ± 0.4%. Additionally, the vMonoCT model was evaluated on a retrospective projection dataset of five mice and one frozen cadaver. It was found that beam hardening artifacts were minimized after image reconstruction of the vMonoCT-corrected projections, and that anatomically incorrect gradient errors were corrected in the cranium up to 15%. Our results disclose the potential of Artificial Intelligence to enhance the µCBCT image quality in biomedical applications. vMonoCT is expected to contribute to the reproducibility of quantitative preclinical applications such as precision irradiations in X-ray cabinets, and to the evaluation of longitudinal imaging data in extensive preclinical studies. [ABSTRACT FROM AUTHOR]

Iodine contrast agent is widely used in liver cancer radiotherapy at CT simulation stage to enhance detectability of tumor. However, its application in cone beam CT (CBCT) for image guidance before treatment delivery is still limited because of poor image quality and excessive dose of contrast agent during multiple treatment fractions. We previously developed a multienergy element-resolved (MEER) CBCT framework that included x-ray projection data acquisition on a conventional CBCT platform in a kVp-switching model and a dictionary-based image reconstruction algorithm that simultaneously reconstructed x-ray attenuation images at each kilovoltage peak (kVp), an electron density image, and elemental composition images. In this study, we investigated feasibility using MEER-CBCT for low-concentration iodine contrast agent visualization. We performed simulation and experimental studies using a phantom with inserts containing water and different concentrations of iodine solution and the MEER-CBCT scan with 600 projections in a full gantry rotation, in which the kVp level sequentially changed among 80, 100, and 120 kVps. We included iodine material in the dictionary of the reconstruction algorithm. We analyzed iodine detectability as quantified by contrast-to-noise ratio (CNR) and compared results with those of CBCT images reconstructed by the standard filter back projection (FBP) method with 600 projections. MEER-CBCT achieved similar contrast enhancement as FBP method but significantly higher CNR. At 2.5% iodine solution concentration, FBP method achieved 170 HU enhancement and CNR of 2.0, considered the standard CNR for successful tumor visualization. MEER-CBCT achieved the same CNR but at ~6.3 times lower iodine concentration of 0.4%. [ABSTRACT FROM AUTHOR]

This study analyzed the impact of CBCT on the level of confidence in diagnostic and treatment thinking in mandibular lower molar (M3M) clinical management. Thirty cases for which panoramic radiographs and CBTC images were available were selected and classified according to radiologic signs indicating the proximity of the M3M to the mandibular canal (interruption of the radiopaque borders of the canal of the mandibular canal wall, darkening of the roots, and diversion or narrowing of the canal, n = 10 for each classification). Twelve oral and maxillofacial surgeons (OMS) contributed to this study by answering two questionnaires. The first questionnaire contained a clinical description of the case and a panoramic radiograph. After 30 days, a second questionnaire with the same clinical illustrations and tomographic multiplanar reconstruction images was administered. Both questionnaires asked specialists to rate diagnostic confidence, the surgical complexity, chosen treatment, and surgical confidence. In approximately 40% of answers, CBCT images had a positive impact on ratings of diagnostic confidence and treatment thinking confidence, and in 24.4%, they increased the surgical complexity score. There was no change in the treatment plan following the use of CBCT, but the CBCT examination was a determining factor for diagnosis and treatment planning in 72.8% of the answers CBCT improved the confidence level in diagnostic and treatment thinking of the M3M management while also increasing the perceived level of surgical complexity. The findings of this study support the need to consider using CBCT in diagnosis and treatment planning for M3Ms with radiographic signs such as darkening of the roots, interruption of the radiopaque borders of the mandibular canal, or deviation of the mandibular canal and narrowing of the roots. [ABSTRACT FROM AUTHOR]

Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data. [ABSTRACT FROM AUTHOR]

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to compare three different methods for measurement of cochlear duct length (CDL) in the clinical setting for two different imaging modalities, namely computed tomography (CT) and cone-beam computed tomography (CBCT). Patients and Methods: One hundred temporal bone data sets (CT: n=50; CBCT: n=50) of non-malformed cochleae were retrospectively analyzed using three different CDL estimation techniques: 3D curved multiplanar reconstruction (cMPR), 2D cMPR and the A-value formula. Results: The data sets belonged to 60 patients (34 males, 26 females; mean age=50.28±18.58 years). For both imaging modalities, application of the 3D cMPR estimation technique led to significantly greater mean CDL values than the two-dimensional methods (p<0.0083). The CDL measurements viewed in CT imaging software were significantly shorter than the corresponding CBCT measurements (p<0.05). Using a linear mixed model, differences in CDL by sex (p=0.796), age (p=0.377) and side of ear (p=0.690) were not significant. Conclusion: The 3D cMPR technique was found to provide the most accurate in vivo CDL measurement in non-malformed cochlea in both CT and CBCT imaging compared to 2D methods. The study results also suggest that the higher spatial resolution in CBCT imaging results in more precise CDL determination than in CT. [ABSTRACT FROM AUTHOR]

To investigate the prevalence and severity of external apical root resorption (EARR) volumetrically with clear aligner therapy using cone-beam computed tomography (CBCT) as well as determine the possible risk factors and develop a prediction model for EARR. In this retrospective study, 320 incisors from 40 Class II patients treated with aligners (Invisalign) were included in this study. CBCT images were obtained at pretreatment (T0) and posttreatment (T1). Root volume was calculated by three-dimensional reconstruction of CBCT images, and apical tooth movement was measured from superimposed CBCT images. Changes in root volume were compared using paired t-tests, and the relationship between root volume loss and potential risk factors was analyzed by multiple linear regression. All of the measured incisors showed root volume loss, with an average of 11.48 ± 6.70 mm3, and the prevalence of severe resorption was 0.625%. The prediction model for EARR included variables of posttreatment sagittal root position (SRP), extraction, tooth type, and apical intrusion and extrusion displacements, with an R2 of 0.51. Age, sex, duration, pretreatment SRP, attachment, advancement, and retraction movements were excluded from the model. Most incisors showed mild to moderate resorption during aligner treatment; only a minimal percentage showed severe resorption. Posttreatment SRP (which showed the highest association with EARR), extraction, tooth type, and apical intrusion and extrusion displacements were risk factors for EARR. [ABSTRACT FROM AUTHOR]

Acquired adult flatfoot is a frequent deformity which implies multiple, complex and combined 3D modifications of the foot skeletal structure. The difficult thorough evaluation of the degree of severity pre-op and the corresponding assessment post-op can now be overcome by cone-beam (CBCT) technology, which can provide access to the 3D skeletal structure in weight-bearing. This study aims to report flatfoot deformities originally in 3D and in weight-bearing, with measurements taken using two different bone segmentation techniques. 21 such patients, with indication for surgical corrections, underwent CBCT (Carestream, US) while standing on one leg. From these scans, 3D models of each bone of the foot were reconstructed by using two different state-of-the-art segmentation tools: a semi-automatic (Mimics Innovation Suite, Materialise, Belgium), and an automatic (Bonelogic Ortho Foot and Ankle, Disior, Finland). From both reconstructed models, Principal Component Analysis was used to define anatomical reference frames, and original foot and ankle angles and other parameters were calculated mostly based on the longitudinal axis of the bones, in anatomical plane projections and in 3D. Both bone model reconstructions revealed a considerable valgus of the calcareous, plantarflexion and internal rotation of the talus, and typical Meary's angles in the lateral and transverse plane projections. The mean difference from these angles between semi-automatic and automatic segmentations was larger than 3.5 degrees for only 3 of the 32 measurements, and a large number of these differences were not statistically significant. CBCT and the present techniques for bone shape reconstruction finally provide a novel and valuable 3D assessment of complex foot deformities in weight-bearing, eliminating previous limitations associated to unloaded feet and bidimensional measures. Corresponding measurements on the bone models from the two segmentation tools compared well. Other more representative measurements can be defined in the future using CBCT and these techniques. [ABSTRACT FROM AUTHOR]

Historically, conventional radiographs have been the primary tool to morphometrically evaluate the talocrural joint, which is comprised of the distal tibia, distal fibula, and proximal talus. More recently, high-resolution volumetric imaging, including computed tomography (CT), has enabled the generation of three-dimensional (3D) reconstructions of the talocrural joint. Weightbearing cone-beam CT (WBCT) technology provides additional benefit to assess 3D spatial relationships and joint congruency while the patient is load bearing. In this study we applied statistical shape modeling, a computational morphometrics technique, to objectively quantify anatomical variation, joint level coverage, joint space distance, and congruency at the talocrural joint. Shape models were developed from segmented WBCT images and included the distal tibia, distal fibula, and full talus. Key anatomical variation across subjects included the fibular notch on the tibia, talar trochlea sagittal plane rate of curvature, tibial plafond curvature with medial malleolus prominence, and changes in the fibular shaft diameter. The shape analysis also revealed a highly congruent talocrural joint with minimal inter-individual morphometric differences at the articular regions. These data are helpful to improve understanding of ankle joint pathologies and to guide refinement of operative treatments. [ABSTRACT FROM AUTHOR]

CT scan by use of a beam-filter placed between the x-ray source and the patient allows a single-scan low-dose dual-energy imaging with a minimal hardware modification to the existing CT systems. We have earlier demonstrated the feasibility of such imaging method with a multi-slit beam-filter reciprocating along the direction perpendicular to the CT rotation axis in a cone-beam CT system. However, such method would face mechanical challenges when the beam-filter is supposed to cooperate with a fast-rotating gantry in a diagnostic CT system. In this work, we propose a new scanning method and associated image reconstruction algorithm that can overcome these challenges. We propose to slide a beam-filter that has multi-slit structure with its slits being at a slanted angle with the CT gantry rotation axis during a scan. A streaky pattern would show up in the sinogram domain as a result. Using a notch filter in the Fourier domain of the sinogram, we removed the streaks and reconstructed an image by use of the filtered-backprojection algorithm. The remaining image artifacts were suppressed by applying ${l}_{{0}}$ norm based smoothing. Using this image as a prior, we have reconstructed low- and high-energy CT images in the iterative reconstruction framework. An image-based material decomposition then followed. We conducted a simulation study to test its feasibility using the XCAT phantom and also an experimental study using the Catphan phantom, a head phantom, an iodine-solution phantom, and a monkey in anesthesia, and showed its successful performance in image reconstruction and in material decomposition. [ABSTRACT FROM AUTHOR]

Compared to single source systems, stereo X-ray CT systems allow acquiring projection data within a reduced amount of time, for an extended field-of-view, or for dual X-ray energies. To exploit the benefit of a dual X-ray system, its acquisition geometry needs to be calibrated. Unfortunately, in modular stereo X-ray CT setups, geometry misalignment occurs each time the setup is changed, which calls for an efficient calibration procedure. Although many studies have been dealing with geometry calibration of an X-ray CT system, little research targets the calibration of a dual cone-beam X-ray CT system. In this work, we present a phantom-based calibration procedure to accurately estimate the geometry of a stereo cone-beam X-ray CT system. With simulated as well as real experiments, it is shown that the calibration procedure can be used to accurately estimate the geometry of a modular stereo X-ray CT system thereby reducing the misalignment artifacts in the reconstruction volumes. [ABSTRACT FROM AUTHOR]

This study develops an improved Feldkamp–Davis–Kress (FDK) reconstruction algorithm using non-local total variation (NLTV) denoising and a cubic B-spline interpolation-based backprojector to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The NLTV objective function is minimized on all log-transformed projections using steepest gradient descent optimization with an adaptive control of the step size to augment the difference between a real structure and noise. The proposed algorithm was evaluated using a phantom data set acquired from a low-dose protocol with lower milliampere-seconds (mAs).The combination of NLTV minimization and cubic B-spline interpolation rendered the enhanced reconstruction images with significantly reduced noise compared to conventional FDK and local total variation with anisotropic penalty. The artifacts were remarkably suppressed in the reconstructed images. Quantitative analysis of reconstruction images using low-dose projections acquired from low mAs showed a contrast-to-noise ratio with spatial resolution comparable to images reconstructed using projections acquired from high mAs. The proposed approach produced the lowest RMSE and the highest correlation. These results indicate that the proposed algorithm enables application of the conventional FDK algorithm for low mAs image reconstruction in low-dose CBCT imaging, thereby eliminating the need for more computationally demanding algorithms. The substantial reductions in radiation exposure associated with the low mAs projection acquisition may facilitate wider practical applications of daily online CBCT imaging. [ABSTRACT FROM AUTHOR]

Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part of industrial quality control, materials science and medical imaging. The need to acquire and process each scan in a short time naturally leads to trade-offs between speed and reconstruction quality, creating a need for fast reconstruction algorithms capable of creating accurate reconstructions from limited data. In this paper, we introduce the Neural Network Feldkamp-Davis-Kress (NN-FDK) algorithm. This algorithm adds a machine learning component to the FDK algorithm to improve its reconstruction accuracy while maintaining its computational efficiency. Moreover, the NN-FDK algorithm is designed such that it has low training data requirements and is fast to train. This ensures that the proposed algorithm can be used to improve image quality in high-throughput CT scanning settings, where FDK is currently used to keep pace with the acquisition speed using readily available computational resources. We compare the NN-FDK algorithm to two standard CT reconstruction algorithms and to two popular deep neural networks trained to remove reconstruction artifacts from the 2D slices of an FDK reconstruction. We show that the NN-FDK reconstruction algorithm is substantially faster in computing a reconstruction than all the tested alternative methods except for the standard FDK algorithm and we show it can compute accurate CCB CT reconstructions in cases of high noise, a low number of projection angles or large cone angles. Moreover, we show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy. [ABSTRACT FROM AUTHOR]

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep superresolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology. [ABSTRACT FROM AUTHOR]

The recent application of Fourier Based Iterative Reconstruction Method (FIRM) has made it possible to achieve high-quality 2D images from a fan beam Computed Tomography (CT) scan with a limited number of projections in a fast manner. The proposed methodology in this article is designed to provide 3D Radon space in linogram fashion to facilitate the use of FIRM with cone beam projections (CBP) for the reconstruction of 3D images in a sparse view angles Cone Beam CT (CBCT). For this reason, in the first phase, the 3D Radon space is generated using CBP data after discretization and optimization of the famous Grangeat’s formula. The method used in this process involves fast Pseudo Polar Fourier transform (PPFT) based on 2D and 3D Discrete Radon Transformation (DRT) algorithms with no wraparound effects. In the second phase, we describe reconstruction of the objects with available Radon values, using direct inverse of 3D PPFT. The method presented in this section eliminates noises caused by interpolation from polar to Cartesian space and exhibits no thorn, V-shaped and wrinkle artifacts. This method reduces the complexity to O(n3 log n) for images of size $ {{\mathrm {n}}\times {\mathrm {n}} \times {\mathrm {n}}}$. The Cone to Radon conversion (Cone2Radon) Toolbox in the first phase and MATLAB/Python toolbox in the second phase were tested on three digital phantoms and experiments demonstrate fast and accurate cone beam image reconstruction due to proposed modifications in all three stages of Grangeat’s method. [ABSTRACT FROM AUTHOR]

Temporal resolution in time-resolved cone-beam CT (TR-CBCT) imaging is often limited by the time needed to acquire a complete data set for image reconstruction. With the recent developments of performing nearly limited-view artifact-free reconstruction from data in a limited-view angle range and a prior image, temporal resolution of TR-CBCT imaging can be improved. One such an example is the use of Simultaneous Multiple Artifacts Reduction in Tomographic RECONstruction (SMART-RECON) technique. However, with SMART-RECON, one can only improve temporal resolution up to 1 frame per second (fps) which is an improvement of 4.5 times over that of the conventional FBP reconstruction. In this paper, a new technique referred to as enhanced SMART-RECON (eSMART-RECON) was introduced to enhance the temporal performance of SMART-RECON in a multi-sweep CBCT data acquisition protocol. Both numerical simulation studies with ground truth and in vivo human subject studies using C-arm CBCT acquisition systems were conducted to demonstrate the following key results: for a multi-sweep CBCT acquisition protocol, eSMART-RECON enables 4–7.5 fps temporal resolution for TR-CBCT which is 4–7.5 times better than that offered by the original SMART-RECON, and 18–34 times better than that offered by the conventional FBP reconstruction. [ABSTRACT FROM AUTHOR]

In this paper, the accuracy of material decomposition (MD) using an energy discriminating photon counting detector was studied. An MD framework was established and validated using calcium hydroxyapatite (CaHA) inserts of known densities (50 mg/cm3, 100 mg/cm3, 250 mg/cm3, 400 mg/cm3), and diameters (1.2, 3.0, and 5.0 mm). These inserts were placed in a cardiac rod phantom that mimics a tissue equivalent heart and measured using an experimental photon counting detector cone beam computed tomography (PCD-CBCT) setup. The quantitative coronary calcium scores (density, mass, and volume) obtained from the MD framework were compared with the nominal values. In addition, three different calibration techniques, signal-to-equivalent thickness calibration (STC), polynomial correction (PC), and projected equivalent thickness calibration (PETC) were compared to investigate the effect of the calibration method on the quantitative values. The obtained MD estimates agreed well with the nominal values for density (mass) with mean absolute percent errors (MAPEs) 8 ± 11% (9 ± 15%) and 4 ± 6% (9 ± 14%) for STC and PETC calibration methods, respectively. PC displayed large MAPEs for density (27 ± 9%), and mass (25 ± 12%). Volume estimation resulted in large deviations between true and measured values with notable MAPEs for STC (40 ± 90%), PC (40 ± 80%), and PETC (40 ± 90%). The framework demonstrated the feasibility of quantitative CaHA mass and density scoring using PCD-CBCT. [ABSTRACT FROM AUTHOR]

4-Dimensional cone-beam computed tomography (4D-CBCT) offers several key advantages over conventional 3D-CBCT in moving target localization/delineation, structure de-blurring, target motion tracking, treatment dose accumulation and adaptive radiation therapy. However, the use of the 4D-CBCT in current radiation therapy practices has been limited, mostly due to its sub-optimal image quality from limited angular sampling of cone-beam projections. In this study, we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement, and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction (SMEIR). Based on the original SMEIR scheme, biomechanical modeling-guided SMEIR (SMEIR-Bio) was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs. To improve the efficiency of reconstruction, we recently developed a U-net-based deformation-vector-field (DVF) optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs (SMEIR-Unet), without explicit biomechanical modeling. Details of each of the SMEIR, SMEIR-Bio and SMEIR-Unet techniques were included in this study, along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs. We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy, and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Temporal bone (TB) studies are essential during the development of new arrays. Postoperative cochlear histology is still regarded as golden standard for the assessment of electrode localization and trauma though it is time consuming, expensive and technically very demanding. The aim of this study is to investigate whether pre-operative evacuation of perilymph improve the assessment of electrode localization and insertion trauma in TBs applying fusion imaging. The results were compared to a prior validated image fusion technique based on the quantification of the electrode placement. MATERIALS and METHODS: 12 prototype electrodes were implanted in fresh frozen TBs. The perilymph was evacuated from the scale prior to pre-operative cone-beam computer tomography (CBCT). The TB were then immersed in Ringer solution to rehydrated both scalae. After electrode insertion post-operative CBCT were obtained. 3D fusions of the pre- and postoperative registration were reconstructed. The electrode localization with respect to the basilar membrane was visually assessed. RESULTS: The visualization of the BM on the pre-operative scans was achieved beyond the second turn in all TBs. The visual assessment was found to be as accurate as the previously validated fusion technique. There was no statistically significant difference between the methods (p=0.564). The image reconstructions and evaluations, however, were faster to perform and the insertion results are immediately available. CONCLUSION: CBCT in combination with pre- and postoperative image fusion is an accurate method for the post-operative assessment of insertion trauma in TBs. This new application facilitates the identification of the BM and allows for a visual assessment of insertion trauma. [ABSTRACT FROM AUTHOR]

Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled cone-beam computed tomography (CBCT). For training, CBCT images were reconstructed using TV-based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and peak signal-to-noise ratio (PSNR). SR-CNN substantially enhanced image details in the TV-based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 half-fan projections to image quality comparable to the reference fully-sampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared with the conventional FDK and TV-based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN-based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in under-sampled 3D/4D-CBCT, which can be very valuable for image-guided radiotherapy. [ABSTRACT FROM AUTHOR]

Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT slices effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales.The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method. [ABSTRACT FROM AUTHOR]

Identification and treatment of obstructive airway disorders (OADs) are greatly aided by imaging of the geometry of the airway lumen. Anatomical optical coherence tomography (aOCT) is a promising high-speed and minimally invasive endoscopic imaging modality for providing micrometer-resolution scans of the upper airway. Resistance to airflow in OADs is directly caused by the reduction in luminal cross-sectional area (CSA). It is hypothesized that aOCT can produce airway CSA measurements as accurate as that from computed tomography (CT). Scans of machine hollowed cylindrical tubes were used to develop methods for segmentation and measurement of airway lumen in CT and aOCT. Simulated scans of virtual cones were used to validate 3-D resampling and reconstruction methods in aOCT. Then, measurements of two segments of a 3-D printed pediatric airway phantom from aOCT and CT independently were compared to ground truth CSA. In continuous unobstructed regions, the mean CSA difference for each phantom segment was 2.2 ± 3.5 and 1.5 ± 5.3 mm2 for aOCT, and −3.4 ± 4.3 and −1.9 ± 1.2 mm2 for CT. Because of the similar magnitude of these differences, these results support the hypotheses and underscore the potential for aOCT as a viable alternative to CT in airway imaging, while offering greater potential to capture respiratory dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we consider dense volumetric modeling of moving samples such as body parts. Most dense modeling methods consider samples observed with a moving X-ray device and cannot easily handle moving samples. We propose instead a novel method to observe shape motion from a fixed X-ray device and to build dense in-depth attenuation information. This yields a low-cost, low-dose 3-D imaging solution, taking benefit of equipment widely available in clinical environments. Our first innovation is to combine a video-based surface motion capture system with a single low-cost/low-dose fixed planar X-ray device, in order to retrieve the sample motion and attenuation information with minimal radiation exposure. Our second innovation is to rely on Bayesian inference to solve for a dense attenuation volume given planar radioscopic images of a moving sample. This approach enables multiple sources of noise to be considered and takes advantage of very limited prior information to solve an otherwise ill-posed problem. Results show that the proposed strategy is able to reconstruct dense volumetric attenuation models from a very limited number of radiographic views over time on synthetic and in-situ data. [ABSTRACT FROM AUTHOR]

This paper aims to eliminate the hardening artifacts in computed tomography (CT) images caused by the polychromatic X-ray spectrum. To this end, a polynomial fitting correction method using non-liner segmentation method was developed through projection space optimization for single medium (water) and the mixed media (water-bone), and verified through simulation and phantom experiments. Unlike the traditional image space optimization strategies, the proposed method has low computing load and few noises, and only requires a one-off image reconstruction to remove the hardening artifacts. The experimental results show that the proposed method can remove CT hardening artifacts and improve the accuracy of image reconstruction, indicating that the project space-based correction method based on non-liner segmentation method can effectively eliminate artifacts and restore the true CT values. [ABSTRACT FROM AUTHOR]

Background. Visualization and calculation of the airway dimensions are important because an increase of airway resistance may lead to life-threatening emergencies. The visualization and calculation of the airway are possible using radiography technique with their advance software. The aim of this study was to compare and to test the reliability of the measurement of the upper airway volume and minimum area using airway analysis function in two software. Methods. The sample consisted of 11 cone-beam computed tomography (CBCT) scans data, evaluated using the Invivo5 (Anatomage) and Romexis (version 3.8.2.R, Planmeca) software which afford image reconstruction, and airway analysis. The measurements were done twice with one week gap between the two measurements. The measurement obtained was analyzed with t -tests and intraclass correlation coefficient (ICC), with confidence intervals (CI) was set at 95%. Results. From the analysis, the mean reading of volume and minimum area is not significantly different between Invivo5 and Romexis. Excellent intrarater reliability values were found for the both measurement on both software, with ICC values ranging from 0.940 to 0.998. Discussion. The results suggested that both software can be used in further studies to investigate upper airway, thereby contributing to the diagnosis of upper airway obstructions. [ABSTRACT FROM AUTHOR]

The authors propose a dual-energy approach to reduce cone-beam artefacts in a circular orbit cone-beam computed tomography (CT) system. When there exist multiple dense structures within a large cone angle, two-pass algorithm is ineffective for reducing cone-beam artefacts because high-density materials are incorrectly segmented with simple thresholding. The proposed algorithm utilises projection data obtained with dual-energy X-ray spectra to estimate projections of the high-density materials from multi-density anatomical structures. Using bone and water as two of basis functions, the projection data of bone materials can be precisely estimated via dual-energy scans. Then, bone-only images are reconstructed by a constrained total-variation minimisation-based iterative algorithm. The reconstructed bone images are used to generate cone-beam artefacts and the final corrected images are acquired by subtracting the generated cone-beam artefacts from the original Feldkamp, Davis, and Kress images. The proposed method was validated using an extended cardiac-torso phantom with complex vertebral anatomy and compared with the two-pass algorithm. The results show that the proposed method restores distorted bony structures and intensity values, especially in regions with large cone angles. A qualitative evaluation of the mean-squared errors and structural similarities demonstrates the effectiveness of the proposed method compared to the two-pass algorithm. [ABSTRACT FROM AUTHOR]

The polychromatic X-ray spectrum and the energy-dependent attenuation coefficient of materials cause beam hardening artifacts in CT reconstructed volumes. These artifacts appear as cupping and streak artifacts depending on the material composition and the geometry of the imaged object. CT scanners employ projection linearization to transform polychromatic attenuation to monochromatic attenuation using a polynomial model. Polynomial coefficients are computed during calibration or using prior information such as X-ray spectrum and attenuation properties of the materials. In this paper, we are presenting a novel method to correct beam hardening artifacts by enforcing cone beam consistency conditions on the projection data. We used consistency conditions derived from Grangeat’s fundamental relation between cone beam projection data and 3-D Radon transform. The optimal polynomial coefficients for artifact reduction are iteratively estimated by minimizing the inconsistency of a set of projection pairs. The results from simulated and real datasets show the visible reduction of artifacts. Our studies also demonstrate the robustness of the algorithm when the projections are perturbed with other physical measurement and geometrical errors. The proposed method requires neither calibration nor prior information like X-ray spectrum, attenuation properties of the materials and detector response. The algorithm can be used for beam hardening correction in clinical, pre-clinical, and industrial CT systems. [ABSTRACT FROM AUTHOR]