Search

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]

The Feldkamp, Davis and Kress algorithm is a computationally efficient reconstruction method for three-dimensional cone-beam computed tomography. However, it suffers from severe artefacts when the number of projections is insufficient. Although recent deep learning–based methods have succeeded in reconstructing such sparse-view projections, it is still challenging to reconstruct a large 3D volume efficiently because of heavy memory consumption and difficulty in obtaining sufficient training data. Therefore, we propose a deep learning method to overcome these drawbacks. Our method consecutively reconstructs short bars in 3D CT volume using the intensities of pixels on the moving trajectory projected on the detector of these bars. Then, we feed such pixel intensities into a neural network and train it to simulate the filtering and back-projection processes of the FDK algorithm. Since the reconstruction volume is separated into bars, the neural network can be used with only a small amount of memory. Furthermore, the network can be trained sufficiently with only a few training samples because plenty of bar data can be extracted even from a single CT image. We experimentally demonstrate that our approach works efficiently for both simulated and actual sparse-view CBCT data using training data extracted from only a single CT image. [ABSTRACT FROM AUTHOR]

Objectives: Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objectifiable data. The aim of this study was to train and evaluate an algorithm based on deep learning methods for fully automated detection of craniofacial landmarks in cone-beam computed tomography (CBCT) in terms of accuracy, speed, and reproducibility. Materials and methods: A total of 931 CBCTs were used to train the algorithm. To test the algorithm, 35 landmarks were located manually by three experts and automatically by the algorithm in 114 CBCTs. The time and distance between the measured values and the ground truth previously determined by an orthodontist were analyzed. Intraindividual variations in manual localization of landmarks were determined using 50 CBCTs analyzed twice. Results: The results showed no statistically significant difference between the two measurement methods. Overall, with a mean error of 2.73 mm, the AI was 2.12% better and 95% faster than the experts. In the area of bilateral cranial structures, the AI was able to achieve better results than the experts on average. Conclusion: The achieved accuracy of automatic landmark detection was in a clinically acceptable range, is comparable in precision to manual landmark determination, and requires less time. Clinical relevance: Further enlargement of the database and continued development and optimization of the algorithm may lead to ubiquitous fully automated localization and analysis of CBCT datasets in future routine clinical practice. [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]

Purpose: Cone‐beam CT (CBCT) has been widely utilized in image‐guided radiotherapy. Planning CT (pCT)‐aided CBCT scatter correction could further enhance image quality and extend CBCT application to dose calculation and adaptive planning. Nevertheless, existing pCT‐based approaches demand accurate registration between pCT and CBCT, leading to limited imaging performance and increased computational cost when large anatomical discrepancies exist. In this work, we proposed a robust and fast CBCT scatter correction method using local filtration technique and rigid registration between pCT and CBCT (LF‐RR). Methods: First of all, the pCT was rigidly registered with CBCT, then forward projection was performed on registered pCT to create scatter‐free projections. The raw scatter signals were obtained via subtracting the scatter‐free projections from the measured CBCT projections. Based on frequency and intensity threshold criteria, reliable scatter signals were selected from the raw scatter signals, and further filtered for global scatter estimation via local filtration technique. Finally, corrected CBCT was reconstructed with the projections generated by subtracting the scatter estimation from the raw CBCT projections using FDK algorithm. The LF‐RR method was evaluated via comparison with another pCT‐based scatter correction method based on Median and Gaussian filters (MG method). Results: Proposed method was first validated on an anthropomorphic pelvis phantom, and showed satisfied performance on scatter removal even when anatomical mismatches were intentionally created on pCT. The quantitative analysis was further performed on four clinical CBCT images. Compared with the uncorrected CBCT, CBCT corrected by MG with rigid registration (MG‐RR), MG with deformable registration (MG‐DR), and LF‐RR reduced the CT number error from 79±35 to 25±18,17±13 and 7±3 HU for adipose and from 115±61 to 36±22,30±24, 7±3 HU for muscle, respectively. After correction, the spatial non‐uniformity (SNU) of CBCT corrected with MG‐RR, MG‐DR and LF‐RR was 51±13,60±21, and 21±9 HU for adipose, and 50±22,57±41, and 25±6 HU for muscle, respectively. Meanwhile, the contrast‐to‐noise ratio (CNR) between muscle and adipose was increased by a factor of 2.70, 2.89 and 2.56, respectively. Using the LF‐RR method, the scatter correction of 656 projections can be finished within 10 s and the corrected volumetric images (200 slices) can be obtained within 2 min. Conclusion: We developed a fast and robust pCT‐based CBCT scatter correction method which exploits the local‐filtration technique to promote the accuracy of scatter estimation and is resistant to pCT‐to‐CBCT registration uncertainties. Both phantom and patient studies showed the superiority of the proposed correction in imaging accuracy and computational efficiency, indicating promisingfuture clinical application. [ABSTRACT FROM AUTHOR]

Purpose: Four‐dimensional cone‐beam computed tomography (4D CBCT) is developed to reconstruct a sequence of phase‐resolved images, which could assist in verifying the patient's position and offering information for cancer treatment planning. However, 4D CBCT images suffer from severe streaking artifacts and noise due to the extreme sparse‐view CT reconstruction problem for each phase. As a result, it would cause inaccuracy of treatment estimation. The purpose of this paper was to develop a new 4D CBCT reconstruction method to generate a series of high spatiotemporal 4D CBCT images. Methods: Considering the advantage of (DL) on representing structural features and correlation between neighboring pixels effectively, we construct a novel DL‐based method for the 4D CBCT reconstruction. In this study, both a motion‐aware dictionary and a spatially structural 2D dictionary are trained for 4D CBCT by excavating the spatiotemporal correlation among ten phase‐resolved images and the spatial information in each image, respectively. Specifically, two reconstruction models are produced in this study. The first one is the motion‐aware dictionary learning‐based 4D CBCT algorithm, called motion‐aware DL based 4D CBCT (MaDL). The second one is the MaDL equipped with a prior knowledge constraint, called pMaDL. Qualitative and quantitative evaluations are performed using a 4D extended cardiac torso (XCAT) phantom, simulated patient data, and two sets of patient data sets. Several state‐of‐the‐art 4D CBCT algorithms, such as the McKinnon–Bates (MKB) algorithm, prior image constrained compressed sensing (PICCS), and the high‐quality initial image‐guided 4D CBCT reconstruction method (HQI‐4DCBCT) are applied for comparison to validate the performance of the proposed MaDL and prior constraint MaDL (pMaDL) pmadl reconstruction frameworks. Results: Experimental results validate that the proposed MaDL can output the reconstructions with few streaking artifacts but some structural information such as tumors and blood vessels, may still be missed. Meanwhile, the results of the proposed pMaDL demonstrate an improved spatiotemporal resolution of the reconstructed 4D CBCT images. In these improved 4D CBCT reconstructions, streaking artifacts are suppressed primarily and detailed structures are also restored. Regarding the XCAT phantom, quantitative evaluations indicate that an average of 58.70%, 45.25%, and 40.10% decrease in terms of root‐mean‐square error (RMSE) and an average of 2.10, 1.37, and 1.37 times in terms of structural similarity index (SSIM) are achieved by the proposed pMaDL method when compared with piccs, PICCS, MaDL(2D), and MaDL(2D), respectively. Moreover the proposed pMaDL achieves a comparable performance with HQI‐4DCBCT algorithm in terms of RMSE and SSIM metrics. However, pMaDL has a better ability to suppress streaking artifacts than HQI‐4DCBCT. Conclusions: The proposed algorithm could reconstruct a set of 4D CBCT images with both high spatiotemporal resolution and detailed features preservation. Moreover the proposed pMaDL can effectively suppress the streaking artifacts in the resultant reconstructions, while achieving an overall improved spatiotemporal resolution by incorporating the motion‐aware dictionary with a prior constraint into the proposed 4D CBCT iterative framework. [ABSTRACT FROM AUTHOR]

Proton therapy treatment for lungs remains challenging as images enabling the detection of inter- and intra-fractional motion, which could be used for proton dose adaptation, are not readily available. 4D computed tomography (4DCT) provides high image quality but is rarely available in-room, while in-room 4D cone beam computed tomography (4DCBCT) suffers from image quality limitations stemming mostly from scatter detection. This study investigated the feasibility of using virtual 4D computed tomography (4DvCT) as a prior for a phase-per-phase scatter correction algorithm yielding a 4D scatter corrected cone beam computed tomography image (4DCBCTcor), which can be used for proton dose calculation. 4DCT and 4DCBCT scans of a porcine lung phantom, which generated reproducible ventilation, were acquired with matching breathing patterns. Diffeomorphic Morphons, a deformable image registration algorithm, was used to register the mid-position 4DCT to the mid-position 4DCBCT and yield a 4DvCT. The 4DCBCT was reconstructed using motion-aware reconstruction based on spatial and temporal regularization (MA-ROOSTER). Successively for each phase, digitally reconstructed radiographs of the 4DvCT, simulated without scatter, were exploited to correct scatter in the corresponding CBCT projections. The 4DCBCTcor was then reconstructed with MA-ROOSTER using the corrected CBCT projections and the same settings and deformation vector fields as those already used for reconstructing the 4DCBCT. The 4DCBCTcor and the 4DvCT were evaluated phase-by-phase, performing proton dose calculations and comparison to those of a ground truth 4DCT by means of dose-volume-histograms (DVH) and gamma pass-rates (PR). For accumulated doses, DVH parameters deviated by at most 1.7% in the 4DvCT and 2.0% in the 4DCBCTcor case. The gamma PR for a (2%, 2 mm) criterion with 10% threshold were at least 93.2% (4DvCT) and 94.2% (4DCBCTcor), respectively. The 4DCBCTcor technique enabled accurate proton dose calculation, which indicates the potential for applicability to clinical 4DCBCT scans. [ABSTRACT FROM AUTHOR]

Purpose: Flat‐panel detector (FPD) based dual‐energy cone‐beam computed tomography (DE‐CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single‐scan DE‐CBCT image reconstruction and decomposition. Methods: A rotatable Mo filter was inserted between an x‐ray source and imaged object to alternately produce low and high‐energy x‐ray spectra. First, filtered‐backprojection (FBP) method was applied on down‐sampled projections to reconstruct low and high‐energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high‐quality dual‐energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual‐energy images were further used for low‐noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan®600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two‐scan method and a commonly used filtering method (HYPR‐LR). Results: On the Catphan®600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise‐power‐spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast‐to‐noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft‐tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two‐scan method were less than 4 HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two‐scan results, and by over 40.4% compared with HYPR‐LR results. Implementation of the whole algorithm took 44.5 s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. Conclusions: Using down‐sampled projections in single‐scan DE‐CBCT, the proposed method could effectively and efficiently produce high‐quality DE‐CBCT images and low‐noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation. [ABSTRACT FROM AUTHOR]

Purpose: Postoperative imaging following cochlear implant (CI) placement is currently the only means of diagnosing proper electrode position. Manual multiplanar reconstruction (MPR) analysis of CT and CBCT is time-consuming and requires extensive training. This study aims to evaluate the rate of CI misalignment and to determine the amount of time necessary to reach a diagnosis of correct versus incorrect CI placement for readers of different experience levels, using a novel algorithm for image analysis (ACIR) compared to MPR analysis.Method: The retrospective single centre study included 333 patients with cochlear implant surgery between May 2002 and May 2021. Postoperative CT and CBCT images were evaluated in three subgroups and the time to diagnosis was documented. Group 1: image evaluation using conventional MPR analysis; group 2: image evaluation by an experienced neuroradiologist via a novel ultra-fast algorithm; group 3: image evaluation by a young specialist via novel ultra-fast algorithm. T-test and Pearson's chi-squared test were used for inter-group comparisons.Results: 333 patients (63.3 ± 15.9 years; 188 men) with 335 CIs were evaluated. The rate of CI misalignment diagnosed from 3D imaging was 14.3% (n = 48). MPR analysis required 255.7 ± 70.4 s per temporal bone, whereas Slicer plugin reduced analysis time to 83.3 ± 7.7 s (p < 0.001) for the experienced reader and 89.6 ± 8.7 s for the young specialist (p < 0.001).Conclusion: 3D postoperative imaging reveals high incidences of CI misalignment. Application of a novel ultra-fast algorithm significantly reduces the time for diagnosis compared to MPR analysis for readers of varying experience levels. [ABSTRACT FROM AUTHOR]

In this paper, we establish a procedure for the enhancement of cone-beam computed tomography (CBCT) dental-maxillofacial images; this can be useful in order to face the problem of rapid prototyping, i.e., to generate a 3D printable file of a dental prosthesis. In the proposed procedure, a crucial role is played by the so-called sampling Kantorovich (SK) algorithm for the reconstruction and image noise reduction. For the latter algorithm, it has already been shown to be effective in the reconstruction and enhancement of real-world images affected by noise in connection to engineering and biomedical problems. The SK algorithm is given by an optimized implementation of the well-known sampling Kantorovich operators and their approximation properties. A comparison between CBTC images processed by the SK algorithm and other well-known methods of digital image processing known in the literature is also given. We finally remark that the above-treated topic has a strong multidisciplinary nature and involves concrete biomedical applications of mathematics. In this type of research, theoretical and experimental disciplines merge in order to find solutions to real-world problems. [ABSTRACT FROM AUTHOR]