CBCT Slice Thickness Impacts Diagnostic Accuracy of Periapical Lesion Volume.

In radiation treatments for head and neck tumors, cone-beam computed tomography (CBCT) is employed for patient positioning and dose calculation of adaptive radiotherapy. However, the quality of CBCT is degraded by the scatter and noise, majorly impacting the accuracy of patient positioning and dose calculation. To improve the quality of CBCT for patients with head and neck cancer, a projection-domain CBCT correction method was proposed using a cycle-consistent generative adversarial network (cycle-GAN) and a nonlocal means filter based on a reference digitally reconstructed radiograph (DRR). A cycle-GAN was initially trained to learn mapping from CBCT projections to a DRR using the data obtained from 30 patients. For each patient, 671 CBCT projections were measured for CBCT reconstruction. Moreover, 360 Digital Reconstructed Radiographs (DRR) were computed from each patient's planning computed tomography (CT), whose projection angles ranged from 0° to 359° with an interval of 1°. By applying the trained generator of the cycle-GAN to the unseen CBCT projection, a synthetic DRR with considerably less scatter was obtained. However, annular artifacts were observed in the CBCT reconstructed with synthetic DRR. To address this issue, a nonlocal means filter based on reference DRR was used to further correct the synthetic DRR, which corrected the synthetic DRR using the calculated DRR as a reference image. Finally, the CBCT with no annular artifact and little noise was reconstructed with the corrected synthetic DRR. The proposed method was tested using the data of six patients. The corrected synthetic DRR and CBCT were compared with the corresponding real DRR and CT images. The structural preservation ability of the proposed method was evaluated using the Dice coefficients of the automatically extracted nasal cavity. Moreover, The image quality of CBCT corrected with the proposed method was objectively assessed with an five-point human scoring system and compared with CT, original CBCT and CBCT corrected with other strategies. The mean absolute value (MAE) of the relative error between the corrected synthetic and real DRR was <8%. The MAE between the corrected CBCT and corresponding CT was <30 HU. Moreover, the Dice coefficient of nasal cavity between the corrected CBCT image and the original image exceeded 98.8 for all the patients. Last but not least, the objective assessment of image qualityshowed the proposed method had an average score of 4.2 in overall image quality, which was higher than that of the original CBCT, CBCT reconstructed with synthetic DRR, and CBCT reconstructed with projections filtered with NLMF only. The proposed method can considerably improve the CBCT image quality with little anatomical distortion, improving the accuracy of radiotherapy for head and neck patients. This article is protected by copyright. All rights reserved.

Traditionally, healing after surgical endodontic retreatment (SER); i.e. apicectomy with or without a retrograde filling, is assessed in periapical radiographs (PR). Recently, the use of cone beam CT (CBCT) has increased within endodontics. Generally, CBCT detects more periapical lesions than PR, but basic research on the true nature of these lesions is missing. The objective was to assess the diagnostic validity of PR and CBCT for determining inflammation in SER cases that were re-operated (SER-R) due to unsuccessful healing, using histology of the periapical lesion as reference for inflammation. Records from 149 patients, receiving SER 2004-10, were screened. In total 108 patients (119 teeth) were recalled for clinical follow-up examination, PR and CBCT, of which 74 patients (83 teeth) participated. Three observers assessed PR and CBCT as "successful healing" or "unsuccessful healing" using Rud and Molven's criteria. SER-R was offered to all non-healed teeth with expected favourable prognosis for subsequent functional retention. During SER-R, biopsy was performed and histopathology verified whether or not inflammation was present. All re-operated cases were assessed non-healed in CBCT while 11 of these were assessed successfully healed in PR. Nineteen biopsies were examined. Histopathologic diagnosis revealed 42% (teeth = 8) without periapical inflammation, 16% (teeth = 3) with mild inflammation and 42% (teeth = 8) with moderate to intense inflammation. A correct diagnosis was obtained in 58% with CBCT (true positives) and 63% with PR (true positives+true negatives). Of the re-operated teeth, 42% had no periapical inflammatory lesion, and hence no benefit from SER-R. Not all lesions observed in CBCT represented periapical inflammatory lesions.

To test the ability of periapical radiography (PA) and cone-beam computed tomography (CBCT) to determine the presence/absence of periapical lesions and examine the reliability of volumetric measurements of periapical lesions on CBCT scans. After tooth extractions in human mandibles, bone defects were cut at the base of extraction sockets to mimic periapical bone lesions. The teeth were then returned into the extraction sockets. Sixty-three roots of anterior teeth, premolars and molars with artificial periapical lesions and 37 roots without lesions were examined with PA and CBCT. Presence/absence of periapical lesion was noted. The CBCT-based volume of each lesion (Vct) was measured using Amira software 5.4 (Visage Imaging GmbH, Berlin, Germany). A replica of each lesion was created using silicone impression material, and the volume of the replica was measured using a water displacement method, representing the physical volume of the lesion (Vp). Regression analysis was used to test the correlation between the Vp and Vct values. The positive and negative predictive values and accuracy for CBCT in diagnosing periapical lesions were all 1, compared with 1, 0.64 and 0.79 for PA diagnosis. Twenty-one (33%) lesions were undetected by PA. The Vp (21.5 ± 11.0 mm(3) ) and Vct (21.4 ± 11.5 mm(3) ) values of 63 lesions were highly correlated (R(2) = 96.9%, P < 0.001). Cone-beam computed tomography is more accurate than PA in diagnosing periapical lesions associated with mandibular teeth. The volumes of artificial mandibular periapical lesions were accurately measured with CBCT data.

In iterative methods for cone-beam computed tomography (CBCT) reconstruction, the use of a huge system matrix is the primary computational bottleneck and is still an obstacle to the more widespread use of these methods in practice. In this paper, to put iterative methods to practical applications, we propose a pragmatic idea, the-so-called dual-resolution voxellation scheme, for a small region-of-interest (ROI) reconstruction in CBCT in which voxels outside the ROI are binned with a double resolution such as 2×2×2, 4×4×4, 8×8×8, 16×16×16, etc., and the voxel sizewithin the ROI remains unchanged. In some situations of medical diagnosis, physicians are interested only in a small ROI containing a target diagnosis from the examined structure. We implemented an efficient compressed-sensing (CS)-based reconstruction algorithm with the proposed voxellation scheme incorporated and performed both simulation and experimental works to investigate the imaging characteristics. Our results indicate that the proposed voxellation scheme seems to be effective in reducing the computational cost considerably for a small ROI reconstruction in iterative CBCT, with the image quality inside the ROI not being noticeably impaired.

Purpose. Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) reconstruction is extremely challenging due to memory limitations. In this study, a novel DL technique is developed to resolve the memory issue, and its feasibility is demonstrated for CBCT reconstruction from sparsely sampled projection data. Methods. The novel geometry-guided deep learning (GDL) technique is composed of a GDL reconstruction module and a post-processing module. The GDL reconstruction module learns and performs projection-to-image domain transformation by replacing the traditional single fully connected layer with an array of small fully connected layers in the network architecture based on the projection geometry. The DL post-processing module further improves image quality after reconstruction. We demonstrated the feasibility and advantage of the model by comparing ground truth CBCT with CBCT images reconstructed using (1) GDL reconstruction module only, (2) GDL reconstruction module with DL post-processing module, (3) Feldkamp, Davis, and Kress (FDK) only, (4) FDK with DL post-processing module, (5) ray-tracing only, and (6) ray-tracing with DL post-processing module. The differences are quantified by peak-signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-square error (RMSE). Results. CBCT images reconstructed with GDL show improvements in quantitative scores of PSNR, SSIM, and RMSE. Reconstruction time per image for all reconstruction methods are comparable. Compared to current DL methods using large fully connected layers, the estimated memory requirement using GDL is four orders of magnitude less, making DL CBCT reconstruction feasible. Conclusion. With much lower memory requirement compared to other existing networks, the GDL technique is demonstrated to be the first DL technique that can rapidly and accurately reconstruct CBCT images from sparsely sampled data.

Periapical lesions of teeth are typically evaluated using periapical X-rays (PA) or cone-beam computer tomography (CBCT); however, ultrasound imaging (US) can also be used to detect bone defects. A comparative analysis is necessary to establish the diagnostic accuracy of US for the detection of periapical lesions in comparison with PA and CBCT. This study aimed to evaluate and compare the measurement precision of US against PA and CBCT in detecting periapical lesions. This study included 43 maxillary and mandibular teeth with periapical lesions. All teeth were examined clinically, radiographically, and ultrasonographically. Observers evaluated and measured the periapical lesions on CBCT, PA, and US images. The comparison of lesion size showed that it differs significantly between the different methods of examination. A statistically significant difference was found between CBCT and US (mean difference = 0.99 mm, 95% CI [0.43-1.55]), as well as between CBCT and PA (mean difference = 0.61 mm, 95% CI [0.17-1.05]). No difference was found between the US and PA methods (p = 0.193). US cannot replace PA radiography in detecting pathologies but it can accurately measure and characterize periapical lesions with minimal radiation exposure. CBCT is the most precise and radiation-intensive method so it should only be used for complex cases.

Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts. We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction. Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinicalexperts. The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clinical evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction). For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre- and post-processing plugins in the existing 3D CBCT reconstruction and trained end-to-end yield significant improvements in image quality and reduction of motionartifacts.

Purpose: Compressed sensing‐based iterative cone beam CT (CBCT) reconstruction techniques can reconstruct CBCT from under‐sampled noisy projection data, allowing for imaging dose reduction. The long computation time prevents them from clinical applications. Although GPU dramatically improves computational efficiency, the computation time is still too long. The purpose of this project is to develop a reconstruction algorithm on a multi‐GPU platform. Methods: We have developed tight‐frame(TF) based CBCT reconstruction system on a workstation with 4 NVIDIA GTX590 GPUs. The algorithm iterates two steps: a conjugate gradient least square step (CGLS) enforcing projection condition and a regularization step improving image quality through TF domain. The first step involves frequent forward and backward x‐ray projections, which is accelerated by distributing tasks corresponding to different projection angles among GPUs. A parallel‐reduction algorithm is employed to accumulate data at all GPUs. The regularization step is achieved by having each GPU processing a sub‐volume. Boundary‐layer data between sub‐volumes are kept to maintain correct boundary conditions. A half‐fan reweighting technique is also invented to mitigate ring artifacts caused by imperfect scanning geometry. Results: Under the quad‐GPU system, the CGLS step, the regularization step are accelerated by 3.2∼3.6 times and 1.6∼2.6times compared to single‐GPU version, respectively. The overall speed‐up factor is 3.06∼3.51 times. As for the absolute time, it takes 0.41∼3.90 sec per iteration step depending on the image resolution and number of projections. Considering it usually takes about 10 iteration steps for the algorithm to achieve satisfactory image quality, the total reconstruction time ranges from a few seconds to up to 40 seconds. High quality CBCT images have been obtained in our system. The reweighing strategy also removes the ring artifacts in half‐fan cases. Conclusion: A TF‐based CBCT reconstruction on a multi‐GPU platform has been successfully developed. The achieved efficiency and image quality facilitates clinical implementations. This work is supported in part by NIH (1R01CA154747‐01), Varian Medical Systems through a Master Research Agreement, the Early Career Award from Thrasher Research Fund, and the University of California Lab Fees Research Program.

Comparison of the accuracy (trueness and precision) of virtual dentofacial patients digitized by three different methods based on 3D facial and dental images

Cone-beam computed tomography (CBCT) is the main imaging tool for image-guided radiotherapy but its functionality is limited by a small imaging volume and restricted image position (imaged at the central instead of the treatment position for peripheral lesions to avoid collisions). In this paper, the authors present the concept of "panoramic CBCT," which can image patients at the treatment position with an imaging volume as large as practically needed. In this novel panoramic CBCT technique, the target is scanned sequentially from multiple view angles. For each view angle, a half scan (180° + θ(cone) where θ(cone) is the cone angle) is performed with the imaging panel positioned in any location along the beam path. The panoramic projection images of all views for the same gantry angle are then stitched together with the direct image stitching method (i.e., according to the reported imaging position) and full-fan, half-scan CBCT reconstruction is performed using the stitched projection images. To validate this imaging technique, the authors simulated cone-beam projection images of the Mathematical Cardiac Torso (MCAT) thorax phantom for three panoramic views. Gaps, repeated/missing columns, and different exposure levels were introduced between adjacent views to simulate imperfect image stitching due to uncertainties in imaging position or output fluctuation. A modified simultaneous algebraic reconstruction technique (modified SART) was developed to reconstruct CBCT images directly from the stitched projection images. As a gold standard, full-fan, full-scan (360° gantry rotation) CBCT reconstructions were also performed using projection images of one imaging panel large enough to encompass the target. Contrast-to-noise ratio (CNR) and geometric distortion were evaluated to quantify the quality of reconstructed images. Monte Carlo simulations were performed to evaluate the effect of scattering on the image quality and imaging dose for both standard and panoramic CBCT. Truncated images with artifacts were observed for the CBCT reconstruction using projection images of the central view only. When the image stitching was perfect, complete reconstruction was obtained for the panoramic CBCT using the modified SART with the image quality similar to the gold standard (full-scan, full-fan CBCT using one large imaging panel). Imperfect image stitching, on the other hand, lead to (streak, line, or ring) reconstruction artifacts, reduced CNR, and/or distorted geometry. Results from Monte Carlo simulations showed that, for identical imaging quality, the imaging dose was lower for the panoramic CBCT than that acquired with one large imaging panel. For the same imaging dose, the CNR of the three-view panoramic CBCT was 50% higher than that of the regular CBCT using one big panel. The authors have developed a panoramic CBCT technique and demonstrated with simulation data that it can image tumors of any location for patients of any size at the treatment position with comparable or less imaging dose and time. However, the image quality of this CBCT technique is sensitive to the reconstruction artifacts caused by imperfect image stitching. Better algorithms are therefore needed to improve the accuracy of image stitching for panoramic CBCT.

Cone‐beam CT (CBCT) has been widely used in image guided radiation therapy (IGRT) to acquire updated volumetric anatomical information before treatment fractions for accurate patient alignment purpose. However the excessive x‐ray imaging doses (a few cGy per scan) delivered to patients from serial CBCT scans raise a clinical concern in most IGRT procedures. This fact has greatly limited exploitation of IGRT's maximal potential. The imaging dose can be effectively reduced by reducing the number of x‐ray projections and/or lowering mAs levels in a CBCT scan. The image quality reconstructed from conventional FDK‐type algorithms however will be highly degraded. Recently total variation (TV) method has demonstrated its ability to reconstruct CBCT images from a few number of noisy x‐ray projections. Nonetheless such a method can hardly be applied in real clinical environments due to its long computational time (a few hours). It is highly desirable to develop a fast reconstruction scheme to obtain high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose.Utilizing GPU to speed up the computationally intensive tasks in CBCT reconstruction problems has drawn a lot of attention recently. In this talk GPU‐based CBCT reconstruction algorithms will be reviewed with an emphasis on an iterative CBCT reconstruction algorithm via TV regularization. We have recently developed a GPU‐friendly version of the forward‐backward splitting algorithm to solve the TV‐based reconstruction problem. Multi‐grid technique is also employed. It is found that 40 x‐ray projections are sufficient to reconstruct CBCT images with satisfactory quality for IGRT patient alignment purpose. Phantom studies indicate that CBCT images can be successfully reconstructed with our algorithm under as low as 0.1 mAs/projection level. Comparing with currently widely used full‐fan head‐and‐neck scanning protocol of about 360 projections with 0.4 mAs/projection it is estimated that an overall 36 times dose reduction has been achieved. Moreover the reconstruction time is about 130 sec on an NVIDIA Tesla C1060 GPU card which is estimated ∼100 times faster than similar iterative reconstruction approaches. The high computational efficiency and satisfactory image quality make the iterative low dose CBCT reconstruction approach feasible in real clinical environments.Learning Objectives:1. Understand basic concepts of GPU‐based CBCT reconstruction.2. Understand main challenges in GPU‐based iterative CBCT reconstruction approach and how an iterative CBCT reconstruction problem is solved on GPU.Conflict of Interest: The research presented here is partially supported by NVIDIA.

PREDOCTORAL EDUCATION IN CONE BEAM COMPUTED TOMOGRAPHY

In radiation treatment, a cone-beam computed tomography (CBCT) scan is conducted for precise positioning of tumors, and the image quality is usually degraded by motion artifacts due to patient’s respiration and movement during scanning. Four-dimensional (4D) CBCT reconstruction with phase binning is typically used to overcome these difficulties. Albeit motion artifacts might be reduced with 4D CBCT, the overall image quality is typically worsened by severe streak artifacts due to the sparse-angle projections available in the 3D reconstruction for each motion phase. This study presents a method for reducing streak artifacts effectively in conventional 4D CBCT reconstruction by using a state-of-the-art convolutional neural network (a residual U-Net was used). We performed a computational simulation and an experiment to investigate the image quality and evaluate the effectiveness of the proposed method. The proposed 4D CBCT reconstruction method reduced streak artifacts noticeably, and its effectiveness was validated by comparing its results to those of other reconstruction methods such as the filtered-backprojection, a compressed-sensing, and a simple CNN-based algorithm for the 4D CBCT datasets.

ABSTRACTIntroduction:Periapical lesions are a common clinical finding, often detected through imaging techniques such as conventional radiography (CR) and cone-beam computed tomography (CBCT). This study aims to compare the diagnostic accuracy of CBCT with CR in identifying periapical lesions.Methods:This comparative study involved patients undergoing dental treatment. Each patient received both CBCT and conventional radiography for periapical lesion detection. The diagnostic outcomes of both modalities were evaluated based on lesion size, position, and diagnostic clarity. Statistical analysis was conducted to compare the sensitivity, specificity, and accuracy of CBCT and CR.Results:CBCT demonstrated superior diagnostic accuracy compared to CR in detecting periapical lesions. The average sensitivity and specificity of CBCT were higher, particularly in detecting smaller and more complex lesions. Statistical analysis showed a significant difference between CBCT and CR in lesion detection (P < 0.05).Conclusion:CBCT is a more reliable imaging modality for the detection of periapical lesions compared to conventional radiography, offering higher diagnostic accuracy. Further studies are needed to establish CBCT as a standard diagnostic tool in clinical practice.

To evaluate the usefulness of the mandibular cortical index (MCI) obtained by digital panoramic radiography (DPR) and by panoramic reconstruction (PR) of cone-beam CT (CBCT) with three different slice thicknesses for the screening of low bone mineral density (BMD) in post-menopausal women. Two trained oral and maxillofacial radiologists assessed the MCI based on the morphology of the mandibular bone cortex (classified as C1, C2 or C3). The DPR and PR of CBCT with slice thicknesses of 5, 15 or 25 mm were compared to the BMD obtained by dual-energy X-ray absorptiometry (DXA) in post-menopausal women. Measures related to accuracy were calculated with MedCalc software. The confidence interval was set at 95%. 54 women (mean age 58.70 ± 7.35 years) participated in the study. The sensitivity and specificity values obtained for DPR were 52.6% and 56.2%, respectively, and values for PR of CBCT with 5, 15, and 25 mm slice thicknesses were 63.1% and 43.7%, 50.0% and 50.0%, and 52.6% and 62.5%, respectively. For the tools evaluated, the positive likelihood ratio ranged from 1.00 to 1.40 and negative likelihood ratio from 0.76 to 1.00. The positive predictive value (PPV) ranged from 70.4 to 76.9% and the negative predictive value (NPV) from 29.6 to 35.7%. Among the examinations, the highest value for area under the curve (AUC) was obtained for CBCT with 25 mm slice thickness (57.6%). The MCI calculated by DPR and CBCT differed with regard to accuracy. Within the limitations of this study, the PR of CBCT with 25 mm slice thicknesses seems to be the most accurate among the examinations evaluated. Should the dentist be attentive, DPR and CBCT may be useful tools for the screening of low BMD in post-menopausal women, facilitating their timely referral for further assessment.