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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., (© 2024. The Author(s).)

Objective: We propose a dual-domain cone beam computed tomography (CBCT) reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction., Methods: The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules: projection preprocessing, differentiable domain transform, and image post-processing. The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray. The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes, where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation, thus enabling precise learning of cone-beam region data. The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises., Results: The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation. Compared with the latest methods, the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074, respectively., Conclusion: The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

Background: The aim of the study was to establish a weighted comprehensive evaluation model (WCEM) of image registration for cone‐beam computed tomography (CBCT) guided lung cancer radiotherapy that considers the geometric accuracy of gross target volume (GTV) and organs at risk (OARs), and assess the registration accuracy of different image registration methods to provide clinical references. Methods: The planning CT and CBCT images of 20 lung cancer patients were registered using diverse algorithms (bony and grayscale) and regions of interest (target, ipsilateral, and body). We compared the coverage ratio (CR) of the planning target volume (PTVCT) to GTVCBCT, as well as the dice similarity coefficient (DSC) of the GTV and OARs, considering the treatment position across various registration methods. Furthermore, we developed a mathematical model to assess registration results comprehensively. This model was evaluated and validated using CRFs across four automatic registration methods. Results: The grayscale registration method, coupled with the registration of the ipsilateral structure, exhibited the highest level of automatic registration accuracy, the DSC were 0.87 ± 0.09 (GTV), 0.71 ± 0.09 (esophagus), 0.74 ± 0.09 (spinal cord), and 0.91 ± 0.05 (heart), respectively. Our proposed WCEM proved to be both practical and effective. The results clearly indicated that the grayscale registration method, when applied to the ipsilateral structure, achieved the highest CRF score. The average CRF scores, excellent rates, good rate and qualification rates were 58 ± 26, 40%, 75%, and 85%, respectively. Conclusions: This study successfully developed a clinically relevant weighted evaluation model for CBCT‐guided lung cancer radiotherapy. Validation confirmed the grayscale method's optimal performance in ipsilateral structure registration. [ABSTRACT FROM AUTHOR]

Background: The routine application of dental implants for replacing missing teeth has revolutionized restorative and prosthetic dentistry. However, cone beam computed tomography (CBCT) evaluations of structures adjacent to the implants are limited by metal artifacts. There are several methods for reducing metal artifacts, but this remains a challenging task. This study aimed to examine the effectiveness of metal artifact reduction (MAR) algorithms in identifying injuries of implants to the inferior alveolar canal in CBCT images. Method: In this in vitro study, mono-cortical bone windows were created and the inferior alveolar canal was revealed. Using 36 implants, pilot drill and penetration damage of the implant tip into the canal was simulated and compared to the control implants with distance from the canal. CBCT images were evaluated by four experienced observers with and without the MAR algorithm and compared to direct vision as the gold standard. The values of accuracy, sensitivity, and specificity were obtained and compared by receiver operating characteristic (ROC) curve (α = 0.05). Result: The area under the ROC curve values for detection of pilot drill injuries varied between 0.840–0.917 and 0.639–0.854 in the active and inactive MAR conditions, respectively. The increase in ROC area was only significant for one of the observers (P = 0.010). For diagnosing penetrative injuries, the area under the ROC curve values was between 0.990–1.000 and 0.722–1.000 in the active and inactive MAR conditions, respectively. The improvement of ROC curve values in active MAR mode was only significant for one of the observers (P = 0.006). Conclusion: Activation of MAR improved the diagnostic values of CBCT images in detecting both types of implant-related injuries to the inferior alveolar canal. However, for most observers, this increase was not statistically significant. [ABSTRACT FROM AUTHOR]

In order to meet the heavy backprojection calculation of the backprojection filtration (BPF) algorithm in circular cone-beam computed tomography (CT), a fast backprojection method is developed, which uses the integral operator of the fixed scanning angle integral interval. The proposed method combines with Hilbert filtration and the filter of the Blackman window, and then, the fast BPF (F-BPF) algorithm is obtained. The experimental results of simulation data and real data demonstrate that the proposed algorithm is fast enough for high-quality reconstructed images only with half-circle projection data. [ABSTRACT FROM AUTHOR]

Introduction: Stereotactic body radiotherapy (SBRT) is an ablative method for lung malignancies. Here, the definition of the gross target volume (GTV) is subject to interobserver variation. In this study, we aimed to evaluate the interobserver variability during SBRT and its dosimetric impact, as well as to introduce a semi-automated delineation tool for both planning computer tomography (P-CT) and cone beam CT (CBCT) to help to standardise GTV delineation and adaptive volume-change registration. Methods: The interobserver variation of GTV manual contours from five physicians was analysed in 15 patients after lung SBRT on free breathing (FB) P-CT (n = 15) and CBCT (n = 90) before and after each fraction. The dosimetric impact from interobserver variations of GTV based on the original treatment plan was analysed. Next, the accuracy of an in-house easy-to-use semi-automatedsegmentation algorithm for pulmonary lesions was compared with gold standard contours in FB P-CT and CBCT, as well as 4D P-CT of additional 10 patients. Results: The interobserver variability in manual contours resulted in violations of dose coverage of the planning target volume (PTV), which, in turn, resulted in compromised tumour control probability in contours from four physicians. The validation of the semi-automated delineation algorithm using thorax phantom led to a highly reliable accuracy in defining GTVs. Comparing the unsupervised auto-contours with the gold standard delineation revealed high equal high concordance for FB P-CT, 4D P-CT and CBCT, with a DSC of 0.83, 0.76 and 0.8, respectively. The supervised use of the semiautomated delineation tool improved its accuracy, with DSCs of 0.86, 0.86 and 0.8 for FB P-CT, 4D P-CT and CBCT, respectively. The use of the algorithm was associated with a significantly shorter working time. The semi-automated delineation tool can accurately register volume changes in CBCTs. Conclusion: The segmentation algorithm provides a reliable, standardised and time-saving alternative for manual delineation in lung SBRT in P-CT and CBCT. [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]

Featured Application: Convolutional neural networks can accurately identify the Inferior Alveolar Canal, rapidly generating precise 3D data. The datasets and source code used in this paper are publicly available, allowing the reproducibility of the experiments performed. Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation. [ABSTRACT FROM AUTHOR]

Introduction: Several studies have reported the relation between the imaging dose and secondary cancer risk and have emphasized the need to minimize the additional imaging dose as low as reasonably achievable. The iterative cone-beam computed tomography (iCBCT) algorithm can improve the image quality by utilizing scatter correction and statistical reconstruction. We investigate the use of a novel iCBCT reconstruction algorithm to reduce the patient dose while maintaining low-contrast detectability and registration accuracy. Methods: Catphan and anthropomorphic phantoms were analyzed. All CBCT images were acquired with varying dose levels and reconstructed with a Feldkamp–Davis–Kress algorithm-based CBCT (FDK-CBCT) and iCBCT. The low-contrast detectability was subjectively assessed using a 9-point scale by 4 reviewers and objectively assessed using structure similarity index (SSIM). The soft tissue-based registration error was analyzed for each dose level and reconstruction technique. Results: The results of subjective low-contrast detectability found that the iCBCT acquired at two-thirds of a dose was superior to the FDK-CBCT acquired at a full dose (6.4 vs 5.4). Relative to FDK-CBCT acquired at full dose, SSIM was higher for iCBCT acquired at one-sixth dose in head and head and neck region while equivalent with iCBCT acquired at two-thirds dose in pelvis region. The soft tissue-based registration was 2.2 and 0.6 mm for FDK-CBCT and iCBCT, respectively. Conclusion: Use of iCBCT reconstruction algorithm can generally reduce the patient dose by approximately two-thirds compared to conventional reconstruction methods while maintaining low-contrast detectability and accuracy of registration. [ABSTRACT FROM AUTHOR]

In the electron beam radiation therapy, customized blocks are mostly used to shape treatment fields to generate conformal doses. The goal of this study is to investigate quantitatively dosimetric uncertainties associated with heterogeneities, detectors used in the measurement of the beam data commissioning, and modeling of the interactions of high energy electrons with tissue. These uncertainties were investigated both by measurements with different detectors and calculations using electron Monte Carlo algorithm (eMC) in the Eclipse treatment planning system. Dose distributions for different field sizes were calculated using eMC and measured with a multiple‐diode‐array detector (MapCheck2) for cone sizes ranging from 6 to 25 cm. The dose distributions were calculated using the CT images of the MapCheck2 and water‐equivalent phantoms. In the umbra region (<20% isodose line), the eMC underestimated dose by a factor of 3 for high energy electron beams due to lack of consideration of bremsstrahlung emitted laterally that was not accounted by eMC in the low dose region outside the field. In the penumbra (20%–80% isodose line), the eMC overestimated dose (40%) for high energy 20 MeV electrons compared to the measured dose with small diodes in the high gradient dose region. This was mainly due to lack of consideration of volume averaging of the ion chamber used in beam data commissioning which was input to the eMC dose calculation algorithm. Large uncertainties in the CT numbers (25%) resulted from the image artifacts in the CT images of the MapCheck2 phantom due to metal artifacts. The eMC algorithm used the electron and material densities extracted from the CT numbers which resulted large dosimetric uncertainties (10%) in the material densities and corresponding stopping power ratios. The dose calculations with eMC are associated with large uncertainties particularly in penumbra and umbra regions and around heterogeneities which affect the low dose level that cover nearby normal tissue or critical structures. [ABSTRACT FROM AUTHOR]

Background: Estimation of eye lens dose is important in head computed tomography (CT) examination since the eye lens is a sensitive organ to ionizing radiation. Objective: The purpose of this study is to compare estimations of eye lens dose in head CT examinations using local size-specific dose estimate (SSDE) based on size-conversion factors of the American Association of Physicists in Medicine (AAPM) Report No. 293 with those based on size-conversion factors of the AAPM Report No. 220. Material and Methods: This experimental study is conducted on a group of patients who had undergone nasopharyngeal CT examination. Due to the longitudinal (z-axis) dose fluctuation, the average global SSDE and average local SSDE (i.e. particular slices where the eyes are located) were investigated. All estimates were compared to the measurement results using thermo-luminescent dosimeters (TLDs). The estimated and measured doses were implemented for 14 patients undergoing nasopharyngeal CT examination. Results: It was found that the percentage differences of the volume CT dose index (CTDIvol), average global SSDE based on AAPM No. 220 (SSDEo,g), average local SSDE based on AAPM No. 220 (SSDEo,l), average global SSDE based on AAPM No. 293 (SSDEn,g) and average local SSDE based on AAPM No. 293 (SSDEn,l) against the measured TLD doses were 22.5, 21.7, 15.0, 9.3, and 2.1%, respectively. All comparisons between dose estimates and TLD measurements gave p-values less than 0.001, except for SSDEn,l (p-value = 0.566). Conclusion: SSDE based on AAPM Report No. 293 can be used to accurately estimate eye lens radiation doses by performing the calculations on a number of specific slices containing the eyes. [ABSTRACT FROM AUTHOR]

This paper aimed to study the adoption of deep learning (DL) algorithm of oral lesions for segmentation of cone-beam computed tomography (CBCT) images. 90 patients with oral lesions were taken as research subjects, and they were grouped into blank, control, and experimental groups, whose images were treated by the manual segmentation method, threshold segmentation algorithm, and full convolutional neural network (FCNN) DL algorithm, respectively. Then, effects of different methods on oral lesion CBCT image recognition and segmentation were analyzed. The results showed that there was no substantial difference in the number of patients with different types of oral lesions among three groups (P > 0.05). The accuracy of lesion segmentation in the experimental group was as high as 98.3%, while those of the blank group and control group were 78.4% and 62.1%, respectively. The accuracy of segmentation of CBCT images in the blank group and control group was considerably inferior to the experimental group (P < 0.05). The segmentation effect on the lesion and the lesion model in the experimental group and control group was evidently superior to the blank group (P < 0.05). In short, the image segmentation accuracy of the FCNN DL method was better than the traditional manual segmentation and threshold segmentation algorithms. Applying the DL segmentation algorithm to CBCT images of oral lesions can accurately identify and segment the lesions. [ABSTRACT FROM AUTHOR]

Background: A significant proportion of the radiation dose from a PET-CT examination is dependent on the CT protocol, which should be optimised for clinical purposes. Matching protocols on different scanners within an imaging centre is important for the consistency of image quality and dose. This paper describes our experience translating low-dose CT protocols between scanner models utilising different automatic exposure control (AEC) methods and reconstruction algorithms. Methods: The scanners investigated were a newly installed Siemens Biograph mCT PET with 64-slice SOMATOM Definition AS CT using sinogram affirmed iterative reconstruction (SAFIRE) and two GE Discovery 710 PET scanners with 128-slice Optima 660 CT using adaptive statistical reconstruction (ASiR). Following exploratory phantom work, 33 adult patients of various sizes were scanned using the Siemens scanner and matched to patients scanned using our established GE protocol to give 33 patient pairs. A comparison of volumetric CT dose index (CTDIvol) and image noise within these patient pairs informed optimisation, specifically for obese patients. Another matched patient study containing 27 patient pairs was used to confirm protocol matching. Size-specific dose estimates (SSDEs) were calculated for patients in the second cohort. With the acquisition protocol for the Siemens scanner determined, clinicians visually graded the images to identify optimal reconstruction parameters. Results: In the first matched patient study, the mean percentage difference in CTDIvol for Siemens compared to GE was − 10.7% (range − 41.7 to 50.1%), and the mean percentage difference in noise measured in the patients' liver was 7.6% (range − 31.0 to 76.8%). In the second matched patient study, the mean percentage difference in CTDIvol for Siemens compared to GE was − 20.5% (range − 43.1 to 1.9%), and the mean percentage difference in noise was 19.8% (range − 27.0 to 146.8%). For these patients, the mean SSDEs for patients scanned on the Siemens and GE scanners were 3.27 (range 2.83 to 4.22) mGy and 4.09 (range 2.81 to 4.82) mGy, respectively. The analysis of the visual grading study indicated no preference for any of the SAFIRE strengths. Conclusions: Given the different implementations of acquisition parameters and reconstruction algorithms between vendors, careful consideration is required to ensure optimisation and standardisation of protocols. [ABSTRACT FROM AUTHOR]

Purpose: Each radiotherapy center should have a site‐specific planning target volume (PTV) margins and image‐guided (IG) radiotherapy (IGRT) correction protocols to compensate for the geometric errors that can occur during treatment. This study developed an automated algorithm for the calculation and evaluation of these parameters from cone beam computed tomography (CBCT)‐based IG‐intensity modulated radiotherapy (IG‐IMRT) treatment. Methods and materials: A MATLAB algorithm was developed to extract the setup errors in three translational directions (x, y, and z) from the data logged by the CBCT system during treatment delivery. The algorithm also calculates the resulted population setup error and PTV margin based on the van Herk margin recipe and subsequently estimates their respective values for no action level (NAL) and extended no action level (eNAL) offline correction protocols. The algorithm was tested on 25 head and neck cancer (HNC) patients treated using IG‐IMRT. Results: The algorithms calculated that the HNC patients require a PTV margin of 3.1, 2.7, and 3.2 mm in the x‐, y‐, and z‐direction, respectively, without IGRT. The margin can be reduced to 2.0, 2.2, and 3.0 mm in the x‐, y‐, and z‐direction, respectively, with NAL and 1.6, 1.7, and 2.2 mm in the x‐, y‐, and z‐direction, respectively, with eNAL protocol. The results obtained were verified to be the same with the margins calculated using an Excel spreadsheet. The algorithm calculates the weekly offline setup error correction values automatically and reduces the risk of input data error observed in the spreadsheet. Conclusions: In conclusion, the algorithm provides an automated method for optimization and reduction of PTV margin using logged setup errors from CBCT‐based IGRT. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). Materials and Methods: A retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm. Results: The mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. Conclusion: The artifacts from the CBCT's limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution. [ABSTRACT FROM AUTHOR]

Background: Beam hardening and scattering artifacts from high-density objects such as dental implants adversely affect the image quality and subsequently the detection of fenestration or dehiscence around dental implants. Objective: This study aimed to assess the efficacy of metal artifact reduction (MAR) algorithm of two cone-beam computed tomography (CBCT) systems for detection of peri-implant fenestration and dehiscence. Material and Methods: In this experimental study, thirty-six titanium implants were placed in bone blocks of bovine ribs. Fenestration and dehiscence were created in the buccal bone around implants. CBCT images were obtained using Cranex 3D and ProMax 3D CBCT systems with and without MAR algorithm. Two experienced radiologists observed the images. Data were analyzed using SPSS software. The Kappa coefficient of agreement, the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy of different imaging modalities were calculated and analyzed. Results: In both CBCT systems, the use of MAR algorithm decreased the area under the ROC curve and subsequently the diagnostic accuracy for the detection of fenestration and dehiscence. The sensitivity, specificity and accuracy of both CBCT systems were higher in absence of the MAR algorithm. The specificity of ProMax 3D for detection of fenestration was equal with/without the MAR algorithm. Conclusion: Although CBCT is suitable for detection of peri-implant defects, the application of the MAR algorithm does not enhance the detection of peri-implant fenestration and dehiscence. [ABSTRACT FROM AUTHOR]

Background: To develop a low-dose cone beam CT (LD-CBCT) reconstruction method named simultaneous algebraic reconstruction technique and dual-dictionary learning (SART-DDL) joint algorithm for image guided radiation therapy (IGRT) and evaluate its imaging quality and clinical application ability.Methods: In this retrospective study, 62 CBCT image sets from February 2018 to July 2018 at west china hospital were randomly collected from 42 head and neck patients (mean [standard deviation] age, 49.7 [11.4] years, 12 females and 30 males). All image sets were retrospectively reconstructed by SART-DDL (resultant D-CBCT image sets) with 18% less clinical raw projections. Reconstruction quality was evaluated by quantitative parameters compared with SART and Total Variation minimization (SART-TV) joint reconstruction algorithm with paired t test. Five-grade subjective grading evaluations were done by two oncologists in a blind manner compared with clinically used Feldkamp-Davis-Kress algorithm CBCT images (resultant F-CBCT image sets) and the grading results were compared by paired Wilcoxon rank test. Registration results between D-CBCT and F-CBCT were compared. D-CBCT image geometry fidelity was tested.Results: The mean peak signal to noise ratio of D-CBCT was 1.7 dB higher than SART-TV reconstructions (P < .001, SART-DDL vs SART-TV, 36.36 ± 0.55 dB vs 34.68 ± 0.28 dB). All D-CBCT images were recognized as clinically acceptable without significant difference with F-CBCT in subjective grading (P > .05). In clinical registration, the maximum translational and rotational difference was 1.8 mm and 1.7 degree respectively. The horizontal, vertical and sagittal geometry fidelity of D-CBCT were acceptable.Conclusions: The image quality, geometry fidelity and clinical application ability of D-CBCT are comparable to that of the F-CBCT for head-and-neck patients with 18% less projections by SART-DDL. [ABSTRACT FROM AUTHOR]

Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious; the COVID-19 lesions are localized by combining the activation regions in the classification network and the unsupervised connected components. 499 CT volumes were used for training and 131 CT volumes were used for testing. Our algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient’s CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability and discover lesion regions in chest CT without the need for annotating the lesions for training. The easily-trained and high-performance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection. [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to assess the effectiveness of a metal artifact reduction (MAR) algorithm activated at different times during cone-beam computed tomography (CBCT) acquisition on the magnitude of artifacts generated by a zirconium implant. Materials and Methods: Volumes were obtained with and without a zirconium implant in a human mandible, using the OP300 Maxio unit. Three modes were tested: without MAR, with MAR activated after acquisition, and with MAR activated before acquisition. Artifacts were assessed in terms of the standard deviation (SD) of gray values and the contrast-to-noise ratio (CNR) in 6 regions of interest with different distances (10 to 35 mm, from the nearest to the farthest) and angulations (70° to 135°) from the implant region. Results: In the acquisitions without MAR, the regions closer to the implant (10 and 15 mm) had a higher SD and lower CNR than the farther regions. When MAR was activated (before or after), SD values did not differ among the regions (P>0.05). The region closest to the implant presented a significantly lower CNR in the acquisitions without MAR than when MAR was activated after the acquisition; however, activating MAR before the acquisition did not yield significant differences from either of the other conditions. Conclusion: Both modes of MAR activation were effective in decreasing the magnitude of CBCT artifacts, especially when the effects of the artifacts were more noticeable. [ABSTRACT FROM AUTHOR]

In recent years, we have seen more and more interest in the field of medical images and shape comparison motivated by the latest advances in microcomputed tomography (μCT) acquisition, modelling, and visualization technologies. Usually, biologists need to evaluate the effect of different root canal preparation systems. Current root canal preparation evaluation methods are based on the volume difference, area difference, and transportation of two root canals before and after treatment. The purpose of root canal preparation is to minimize the volume difference and ensure the complete removal of the smear layer. Previous methods can reflect some general geometric differences, but they are not enough to evaluate the quality of root canal shape. To solve this problem, we proposed a novel root canal evaluation method based on spectrum and eigenfunctions of Steklov operators, which can be served as a better alternative to current methods in root canal preparation evaluation. Firstly, the ideal root canal model was simulated according to the root canal model before and after preparation. Secondly, the Steklov spectrum of the two models was calculated. Thirdly, based on the spectrum and the histogram of the Gaussian curvature on the surface, the weight of each eigenvalue was computed. Therefore, the Steklov spectrum distance (SSD), which measures shape difference between the root canals, was defined. Finally, the calculation method that quantifies the root canal preparation effect of root canals was obtained. Through experiments, our method manifested high robustness and accuracy compared with existing state-of-the-art approaches. It also demonstrates the significance of our algorithm's advantages on a variety of challenging root canals through result comparison with counterpart methods. [ABSTRACT FROM AUTHOR]

Establishing dense correspondences of cone-beam computed tomography (CBCT) images is a crucial step for the attribute transfer and morphological variation assessment in clinical orthodontics. In this paper, a novel method, unsupervised spatially consistent clustering forest, is proposed to tackle the challenges for automatic supervoxel-wise correspondences of CBCT images. A complexity analysis of the proposed method with respect to the clustering hypotheses is provided with a data-dependent learning guarantee. The learning bound considers both the sequential tree traversals determined by questions stored in branch nodes and the clustering compactness of leaf nodes. A novel tree-pruning algorithm, guided by the learning bound, is also proposed to remove locally inconsistent leaf nodes. The resulting forest yields spatially consistent affinity estimations, thanks to the pruning penalizing trees with inconsistent leaf assignments and the combinational contextual feature channels used to learn the forest. A forest-based metric is utilized to derive the pairwise affinities and dense correspondences of CBCT images. The proposed method has been applied to the label propagation of clinically captured CBCT images. In the experiments, the method outperforms variants of both supervised and unsupervised forest-based methods and state-of-the-art label-propagation methods, achieving the mean dice similarity coefficients of 0.92, 0.89, 0.94, and 0.93 for the mandible, the maxilla, the zygoma arch, and the teeth data, respectively. [ABSTRACT FROM AUTHOR]

To reduce the radiation dose given to patients, a tube current modulation (TCM) method has been widely used in diagnostic CT systems. However, the TCM method has not yet been applied to a kV-CBCT system on a LINAC machine. The purpose of this study is to investigate if a TCM method would be desirable in a kV-CBCT system for image-guided radiation therapy (IGRT) or not. We have developed an attenuation–based TCM method using prior knowledge from planning CT images of patients. The TCM method can provide optimized dose reductions without degrading image quality for kV-CBCT imaging. Here, we investigate whether or not our suggested TCM method is desirable to use in kV-CBCT systems to confirm and revise the exact position of a patient for IGRT. Patients go through diagnostic CT scans for RT planning; therefore, using information from prior CT images can enable estimations of the total X-ray attenuation through a patient’s body in a CBCT setting for radiation treatment. Having this planning CT image allows to use the proposed TCM method in RT. The proposed TCM method provides a minimal amount of current for each projection, as well as total current, required to reconstruct the current modulated CBCT image with an image quality similar to that of CBCT. After applying a calculated TCM current for each projection, projection images were acquired and the current modulated CBCT image was reconstructed using a FDK algorithm. To validate the proposed approach, we used a numerical XCAT phantom and a real ATOM phantom and evaluated the performance of the proposed method via visual and quantitative image quality metrics. The organ dose due to imaging radiation was calculated in both cases and compared using the GATE simulation toolkit. As shown in the quantitative evaluation, normalized noise and SSIM values of the TCM were similar to those of conventional CBCT images. In addition, the proposed TCM method yielded comparable image quality to that of conventional CBCT images for both simulations and experimental studies as organ doses were decreased. We have successfully demonstrated the feasibility and dosimetric merit of a prototypical TCM method for kV-CBCT via simulations and experimental study. The results indicate that the proposed TCM method and overall framework can be a viable option for CBCT imaging that utilizes an optimal dose reduction without degrading image quality. Thus, this method reduces the probability for side effects due to radiation exposure. [ABSTRACT FROM AUTHOR]

Cone-beam computed tomography (CBCT) images are presently used for geometric verification for daily patient positioning. In this work, we have compared the images of CBCT with the images of conventional fan beam CT (FBCT) in terms of image quality and Hounsfield units (HUs). We also compared the dose calculated using CBCT with that of FBCT. Homogenous RW3 plates and Catphan phantom were scanned by FBCT and CBCT. In RW3 and Catphan phantom, percentage depth dose (PDD), profiles, isodose distributions (for intensity modulated radiotherapy plans), and calculated dose volume histograms were compared. The HU difference was within ± 20 HU (central region) and ± 30 HU (peripheral region) for homogeneous RW3 plates. In the Catphan phantom, the difference in HU was ± 20 HU in the central area and peripheral areas. The HU differences were within ± 30 HU for all HU ranges starting from -1000 to 990 in phantom and patient images. In treatment plans done with simple symmetric and asymmetric fields, dose difference (DD) between CBCT plan and FBCT plan was within 1.2% for both phantoms. In intensity modulated radiotherapy (IMRT) treatment plans, for different target volumes, the difference was <2%. This feasibility study investigated HU variation and dose calculation accuracy between FBCT and CBCT based planning and has validated inverse planning algorithms with CBCT. In our study, we observed a larger deviation of HU values in the peripheral region compared to the central region. This is due to the ring artifact and scatter contribution which may prevent the use of CBCT as the primary imaging modality for radiotherapy treatment planning. The reconstruction algorithm needs to be modified further for improving the image quality and accuracy in HU values. However, our study with TG-119 and intensity modulated radiotherapy test targets shows that CBCT can be used for adaptive replanning as the recalculation of dose with the anisotropic analytical algorithm is in full accord with conventional planning CT except in the build-up regions. Patient images with CBCT have to be carefully analyzed for any artifacts before using them for such dose calculations. [ABSTRACT FROM AUTHOR]

Background: In IGRT of deformable head-and-neck anatomy, patient setup corrections are derived by rigid registration methods. In practice, experienced radiation therapists often correct the resulting vectors, thus indicating a different prioritization of alignment of local structures. Purpose of this study is to transfer the knowledge experts apply when correcting the automatically generated result (pre-match) to automated registration.Methods: Datasets of 25 head-and-neck-cancer patients with daily CBCTs and corresponding approved setup correction vectors were analyzed. Local similarity measures were evaluated to identify the criteria for human corrections with regard to alignment quality, analogous to the radiomics approach. Clustering of similarity improvement patterns is applied to reveal priorities in the alignment quality.Results: The radiation therapists prioritized to align the spinal cord closest to the high-dose area. Both target volumes followed with second and third highest priority. The bony pre-match influenced the human correction along the crania-caudal axis. Based on the extracted priorities, a new rigid registration procedure is constructed which is capable of reproducing the corrections of experts.Conclusions: The proposed approach extracts knowledge of experts performing IGRT corrections to enable new rigid registration methods that are capable of mimicking human decisions. In the future, the deduction of knowledge-based corrections for different cohorts can be established automating such supervised learning approaches. [ABSTRACT FROM AUTHOR]

In recent years, there has been increased interest in low-dose X-ray cone beam computed tomography (CBCT) in many fields, including dentistry, guided radiotherapy and small animal imaging. Despite reducing the radiation dose, low-dose CBCT has not gained widespread acceptance in routine clinical practice. In addition to performing more evaluation studies, developing a fast and high-quality reconstruction algorithm is required. In this work, we propose an iterative reconstruction method that accelerates ordered-subsets (OS) reconstruction using a power factor. Furthermore, we combine it with the total-variation (TV) minimization method. Both simulation and phantom studies were conducted to evaluate the performance of the proposed method. Results show that the proposed method can accelerate conventional OS methods, greatly increase the convergence speed in early iterations. Moreover, applying the TV minimization to the power acceleration scheme can further improve the image quality while preserving the fast convergence rate. [ABSTRACT FROM AUTHOR]

In industrial computed tomography (CT), the mismatch between the X-ray energy and the effective thickness makes it difficult to ensure the integrity of projection data using the traditional scanning model, because of the limitations of the object’s complex structure. So, we have developed a CT imaging method that is based on a spherical trajectory. Considering an unrestrained trajectory for iterative reconstruction, an iterative algorithm can be used to realise the CT reconstruction of a spherical trajectory for complete projection data only. Also, an inclined circle trajectory is used as an example of a spherical trajectory to illustrate the accuracy and feasibility of this new scanning method. The simulation results indicate that the new method produces superior results for a larger cone-beam angle, a limited angle and tabular objects compared with traditional circle trajectory scanning. [ABSTRACT FROM AUTHOR]