Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography

Conventional non-local total variation (NLTV) approaches use the weight of a non-local means (NLM) filter, which degrades performance in low-dose cone-beam computed tomography (CBCT) images generated with a low milliampere-seconds (mAs) parameter value because a local patch used to determine the pixel weights comprises noisy-damaged pixels that reduce the similarity between corresponding patches. In this paper, we propose a novel type of NLTV based on a combination of mutual information (MI): MI-NLTV. It is based on a statistical measure for a similarity calculation between the corresponding bins of non-local patches vs. a reference patch. The weight is determined in terms of a statistical measure comprising the MI value between corresponding non-local patches and the reference-patch entropy. The MI-NLTV denoising process is applied to CBCT images generated by the analytical reconstruction algorithm using a ray-driven backprojector (RDB). The MI-NLTV objective function is minimized based on the steepest gradient descent optimization to augment the difference between a real structure and noise, cleaning noisy pixels without significant loss of the fine structure and details that remain in the reconstructed images. The proposed method was evaluated using patient data and actual phantom measurement data acquired with lower mAs. The results show that integrating the RDB further enhances the MI-NLTV denoising-based analytical reconstruction algorithm to achieve a higher CBCT image quality when compared with those generated by NLTV denoising-based approach, with an average of 15.97% higher contrast-to-noise ratio, 2.67% lower root mean square error, 0.12% lower spatial non-uniformity, 1.14% higher correlation, and an average of 18.11% higher detectability index. These quantitative results indicate that the incorporation of MI makes the NLTV more stable and robust than the conventional NLM filter for low-dose CBCT imaging. In addition, achieving clinically acceptable CBCT image quality despite low-mAs projection acquisition can reduce the burden on common online CBCT imaging, improving patient safety throughout the course of radiotherapy.

Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis

In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm for oral neck and facial surgery diseases (deep diagnosis of oral and maxillofacial diseases, referred to as DDOM) is brought out; in this method, the DDOM algorithm proposed for patient classification, lesion segmentation, and tooth segmentation, respectively, can effectively process the three-dimensional oral CBCT data of patients and carry out patient-level classification. The segmentation results show that the proposed segmentation method can effectively segment the independent teeth in CBCT images, and the vertical magnification error of tooth CBCT images is clear. The average magnification rate was 7.4%. By correcting the equation of R value and CBCT image vertical magnification rate, the magnification error of tooth image length could be reduced from 7.4. According to the CBCT image length of teeth, the distance R from tooth center to FOV center, and the vertical magnification of CBCT image, the data closer to the real tooth size can be obtained, in which the magnification error is reduced to 1.0%. Therefore, it is proved that the 3D oral cone beam electronic computer based on deep learning can effectively assist doctors in three aspects: patient diagnosis, lesion localization, and surgical planning.

Quality Assurance of Image Registration Using Combinatorial Rigid Registration Optimization (CORRO)

Purpose: Expert selected landmark points on clinical image pairs to provide a basis for rigid registration validation. Using combinatorial rigid registration optimization (CORRO) provide a statistically characterized reference data set for image registration of the pelvis by estimating optimal registration. Materials ad Methods: Landmarks for each CT/CBCT image pair for 58 cases were identified. From the landmark pairs, combination subsets of k-number of landmark pairs were generated without repeat, forming k-set for k=4, 8, and 12. A rigid registration between the image pairs was computed for each k-combination set (2,000-8,000,000). The mean and standard deviation of the registration were used as final registration for each image pair. Joint entropy was used to validate the output results. Results: An average of 154 (range: 91-212) landmark pairs were selected for each CT/CBCT image pair. The mean standard deviation of the registration output decreased as the k-size increased for all cases. In general, the joint entropy evaluated was found to be lower than results from commercially available software. Of all 58 cases 58.3% of the k=4, 15% of k=8 and 18.3% of k=12 resulted in the better registration using CORRO as compared to 8.3% from a commercial registration software. The minimum joint entropy was determined for one case and found to exist at the estimated registration mean in agreement with the CORRO algorithm. Conclusion: The results demonstrate that CORRO works even in the extreme case of the pelvic anatomy where the CBCT suffers from reduced quality due to increased noise levels. The estimated optimal registration using CORRO was found to be better than commercially available software for all k-sets tested. Additionally, the k-set of 4 resulted in overall best outcomes when compared to k=8 and 12, which is anticipated because k=8 and 12 are more likely to have combinations that affected the accuracy of the registration.

CBCT image artefacts generated by implants located inside the field of view or in the exomass

Objectives: To compare artefacts in cone-beam computed tomography (CBCT) arising from implants of different materials located either inside the field-of-view (FOV) or in the exomass, and to test different image-acquisition parameters to reduce them. Methods: CBCT scans of a human mandible prepared with either a titanium, titanium-zirconium, or zirconia implant were acquired with the Planmeca ProMax utilizing FOV sizes of 8 × 5 cm and 4 × 5 cm, which placed the implant inside the FOV (8 × 5 cm) or in the exomass (4 × 5 cm). The scanning parameters considered three conditions of metal artefact reduction (MAR), disabled, low, and high, and two kVp levels (80 and 90). The standard deviation (SD) of grey values of regions of interest was obtained. The effects of implant material, implant position, MAR condition, kVp level, and their interactions were evaluated by Analysis of Variance (α = 5%). Results: The zirconia implant produced the highest SD values (more heterogeneous grey values, corresponding to greater artefact expression), followed by titanium-zirconium, and titanium. In general, implants in the exomass produced images with higher SD values than implants inside the FOV. MAR was effective in decreasing SD values, especially from the zirconia implant, only when the implant was inside the FOV. Images with 80 kVp had higher SD values than those with 90 kVp, regardless of the other factors (p < 0.05). Conclusions: Implants in the exomass lead to greater artefact expression than when they are inside the FOV. Special attention should be paid to scanning parameters that reduce metal-related artefacts, such as MAR activation and increasing kVp. This is especially important with a zirconia implant inside the FOV.

Evaluation of the setup discrepancy between 6D ExacTrac and cone beam computed tomography in spine stereotactic body radiation therapy

The objective of this study was to analyze the difference in residual setup errors between 6D ExacTrac and 3D cone-beam computed tomography (CBCT) image-guided systems in spinal stereotactic body radiation therapy (SBRT). We investigated 76 patients with spinal tumors who received SBRT using Novalis Tx at our institution between January 2013 and September 2020. A Vac-lok (EZ-FIX®, Arlico Medical Company, South Korea) fixture and an assistive device, based on the region involved, were used to immobilize patients and to increase the inter-fractional setup reproducibility. The difference in the root mean square (RMS) between the 6D ExacTrac and 3D CBCT was -0.75 mm, 0.45 mm, 0.16 mm, and -0.03°; the RMS value was 1.31 mm, 1.06 mm, 0.87 mm, and 0.64°; and the standard deviation was 0.80 mm, 0.72 mm, 0.62 mm, and 0.44° for lateral, longitudinal, vertical, and yaw directions, respectively. The difference in the average RMS between ExacTrac and CBCT was <1.03 mm in the translation direction and <0.47° in the rotational direction; the results were statistically significant in the lateral, longitudinal, and vertical directions, but not in the yaw direction. Thus, it is necessary to verify the ExacTrac image according to the CBCT image.

Are cone beam CT image matching skills transferrable from planning CT to planning MRI for MR-only prostate radiotherapy?

Objectives: Treatment verification for MR-only planning has focused on fiducial marker matching, however, these are difficult to identify on MR. An alternative is using the MRI for soft-tissue matching with cone beam computed tomography images (MR-CBCT). However, therapeutic radiographers have limited experience of MRI. This study aimed to assess transferability of therapeutic radiographers CT-CBCT prostate image matching skills to MR-CBCT image matching. Methods: 23 therapeutic radiographers with 3 months–5 years’ experience of online daily CT-CBCT soft-tissue matching prostate cancer patients participated. Each observer completed a baseline assessment of 10 CT-CBCT prostate soft-tissue image matches, followed by 10 MR-CBCT prostate soft-tissue image match assessment. A MRI anatomy training intervention was delivered and the 10 MR-CBCT prostate soft-tissue image match assessment was repeated. Limits of agreement were calculated as the disagreement of the observers with mean of all observers. Results: Limits of agreement at CT-CBCT baseline were 2.8 mm, 2.8 mm, 0.7 mm (vertical, longitudinal, lateral). MR-CBCT matches prior to training were 3.3 mm, 3.1 mm, 0.9 mm, and after training 2.6 mm, 2.4 mm, 1.1 mm (vertical, longitudinal, lateral). Results show similar limits of agreement across the assessments, and variation reduced following the training intervention. Conclusion: This suggests therapeutic radiographers’ prostate CBCT image matching skills are transferrable to a MRI planning scan, since MR-CBCT matching has comparable observer variation to CT-CBCT matching. Advances in knowledge: This is the first publication assessing interobserver MR-CBCT prostate soft tissue matching in an MR-only pathway.