CBCT Data Sets Research Articles

Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.

Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT scans. However, these approaches have relied on uniform datasets from a single center or imaging device but without considering patient ethnicity. In addition, previous works have considered a limited number of clinically relevant cephalometric landmarks and the approaches were computationally infeasible, both impairing integration into clinical workflow. Here our aim is to analyze the clinical applicability of a light-weight deep learning neural network for fast localization of 46 clinically significant cephalometric landmarks with multi-center, multi-ethnic, and multi-device data consisting of 309 CBCT scans from Finnish and Thai patients. The localization performance of our approach resulted in the mean distance of 1.99 ± 1.55 mm for the Finnish cohort and 1.96 ± 1.25 mm for the Thai cohort. This performance turned out to be clinically significant i.e., ≤ 2 mm with 61.7% and 64.3% of the landmarks with Finnish and Thai cohorts, respectively. Furthermore, the estimated landmarks were used to measure cephalometric characteristics successfully i.e., with ≤ 2 mm or ≤ 2° error, on 85.9% of the Finnish and 74.4% of the Thai cases. Between the two patient cohorts, 33 of the landmarks and all cephalometric characteristics had no statistically significant difference (p < 0.05) measured by the Mann-Whitney U test with Benjamini-Hochberg correction. Moreover, our method is found to be computationally light, i.e., providing the predictions with the mean duration of 0.77 s and 2.27 s with single machine GPU and CPU computing, respectively. Our findings advocate for the inclusion of this method into clinical settings based on its technical feasibility and robustness across varied clinical datasets.

Fractal Dimension on CBCT Images and Modular Neural Networks to Identify Reduced Bone Mineral Density in Women

This paper provides two different methods to diagnose osteoporosis in women; the first method is the fractal analysis evaluated by CBCT at two bone locations (the mandible and the second cervical vertebrae) to see if there is any correlation between the two. At the same time, the second method is deep convolutional neural networks (DCNNs). One hundred eighty-eight patients' mandibular CBCT images were used, and DCNN models based on the ResNet-101 framework were employed. Dual X-ray absorptiometry of the hip and lumbar spine revealed that 139 of the 188 postmenopausal women tested had osteoporosis, whereas 49 had average bone mineral density. The second cervical vertebra and the mandible were selected as locations of interest for FD analysis on the CBCT images. Measurement accuracy, both within and between observers' agreements, and correlations between two data sets were all calculated. To evaluate osteoporosis, we used a segmented, three-phase approach. Stage 1 was devoted to the identification of mandibular bone slices. In Stage 2, the coordinates for the mandible's cross-sectional views were established, and Stage 3 calculated the thickness of the mandible bone, emphasizing osteoporotic variations. The average FD values within the interest area of the mandible were significantly lower in people with osteoporosis than in those with average bone mineral density. At the same time, the two groups had no significant difference in FD values at the second cervical vertebra. For the mandibular site, areas beneath the curve were 0.644 (P = 0.008), while the area under the curve for the vertebral site was 0.531 (P = 0.720). DCNN training in the first stage yielded an astounding 98.85% training accuracy, the second stage decreased L1 loss to a meager 1.02 pixels, and the bone thickness computation method used in the last stage had a mean squared error of 0. 8377. We concluded that FD was underutilized even though it distinguished between women with normal BMD and those with osteoporosis in the mandibular area. Additionally, even with small mandibular CBCT datasets, the results show the value of a modular transfer learning approach for osteoporosis detection.

Radiation therapy margin reduction for patients with localized prostate cancer: A prospective study of the dosimetric impact and quality of life.

To investigate the impact of reducing Clinical Target Volume (CTV) to Planning Target Volume (PTV) margins on delivered radiation therapy (RT) dose and patient reported quality-of-life (QOL) for patients with localized prostate cancer. Twenty patients were included in a single institution IRB-approved prospective study. Nine were planned with reduced margins (4mm at prostate/rectum interface, 5mm elsewhere), and 11 with standard margins (6/10mm). Cumulative delivered dose was calculated using deformable dose accumulation. Each daily CBCT dataset was deformed to the planning CT (pCT), dose was computed, and accumulated on the resampled pCT using a parameter-optimized, B-spline algorithm (Elastix, ITK/VTK). EPIC-26 patient reported QOL was prospectively collected pre-treatment, post-treatment, and at 2-, 6-, 12-, 18-, 24-, 36-, 48-, and 60-month follow-ups. Post -RT QOL scores were baseline corrected and standardized to a [0-100] scale using EPIC-26 methodology. Correlations between QOL scores and dosimetric parameters were investigated, and the overall QOL differences between the two groups (QOLMargin-reduced -QOLcontrol ) were calculated. The median QOL follow-up length for the 20 patients was 48months. Difference between delivered dose and planned dose did not reach statistical significance (p>0.1) for both targets and organs at risk between the two groups. At 4years post-RT, standardized mean QOLMargin-reduced -QOLcontrol were improved for Urinary Incontinence, Urinary Irritative/Obstructive, Bowel, and Sexual EPIC domains by 3.5, 14.8, 10.2, and 16.1, respectively (higher values better). The control group showed larger PTV/rectum and PTV/bladder intersection volumes (7.2±5.8, 18.2±8.1cc) than the margin-reduced group (2.6±1.8, 12.5±8.3cc), though the dose to these intersection volumes did not reach statistical significance (p>0.1) between the groups. PTV/rectum intersection volume showed a moderate correlation (r=-0.56, p<0.05) to Bowel EPIC domain. Results of this prospective study showed that margin-reduced group exhibited clinically meaningful improvement of QOL without compromising the target dose coverage.

Whole mandibular canal segmentation using transformed dental CBCT volume in Frenet frame

Accurate segmentation of the mandibular canal is essential in dental implant and maxillofacial surgery, which can help prevent nerve or vascular damage inside the mandibular canal. Achieving this is challenging because of the low contrast in CBCT scans and the small scales of mandibular canal areas. Several innovative methods have been proposed for mandibular canal segmentation with positive performance. However, most of these methods segment the mandibular canal based on sliding patches, which may adversely affect the morphological integrity of the tubular structure. In this study, we propose whole mandibular canal segmentation using transformed dental CBCT volume in the Frenet frame. Considering the connectivity of the mandibular canal, we propose to transform the CBCT volume to obtain a sub-volume containing the whole mandibular canal based on the Frenet frame to ensure complete 3D structural information. Moreover, to further improve the performance of mandibular canal segmentation, we use clDice to guarantee the integrity of the mandibular canal structure and segment the mandibular canal. Experimental results on our CBCT dataset show that integrating the proposed transformed volume in the Frenet frame into other state-of-the-art methods achieves a 0.5%∼12.1% improvement in Dice performance. Our proposed method can achieve impressive results with a Dice value of 0.865 (±0.035), and a clDice value of 0.971 (±0.020), suggesting that our method can segment the mandibular canal with superior performance.

Evaluation of generalization ability for deep learning-based auto-segmentation accuracy in limited field of view CBCT of male pelvic region.

The aim of this study was to evaluate generalization ability of segmentation accuracy for limited FOV CBCT in the male pelvic region using a full-image CNN. Auto-segmentation accuracy was evaluated using various datasets with different intensity distributions and FOV sizes. A total of 171 CBCT datasets from patients with prostate cancer were enrolled. There were 151, 10, and 10 CBCT datasets acquired from Vero4DRT, TrueBeam STx, and Clinac-iX, respectively. The FOV for Vero4DRT, TrueBeam STx, and Clinac-iX was 20, 26, and 25cm, respectively. The ROIs, including the bladder, prostate, rectum, and seminal vesicles, were manually delineated. The U2 -Net CNN network architecture was used to train the segmentation model. A total of 131 limited FOV CBCT datasets from Vero4DRT were used for training (104 datasets) and validation (27 datasets); thereafter the rest were for testing. The training routine was set to save the best weight values when the DSC in the validation set was maximized. Segmentation accuracy was qualitatively and quantitatively evaluated between the ground truth and predicted ROIs in the different testing datasets. The mean scores±standard deviation of visual evaluation for bladder, prostate, rectum, and seminal vesicle in all treatment machines were 1.0±0.7, 1.5±0.6, 1.4±0.6, and 2.1±0.8 points, respectively. The median DSC values for all imaging devices were ≥0.94 for the bladder, 0.84-0.87 for the prostate and rectum, and 0.48-0.69 for the seminal vesicles. Although the DSC values for the bladder and seminal vesicles were significantly different among the three imaging devices, the DSC value of the bladder changed by less than 1% point. The median MSD values for all imaging devices were ≤1.2mm for the bladder and 1.4-2.2mm for the prostate, rectum, and seminal vesicles. The MSD values for the seminal vesicles were significantly different between the three imaging devices. The proposed method is effective for testing datasets with different intensity distributions and FOV from training datasets.

Semantic Graph Attention With Explicit Anatomical Association Modeling for Tooth Segmentation From CBCT Images.

Accurate tooth identification and delineation in dental CBCT images are essential in clinical oral diagnosis and treatment. Teeth are positioned in the alveolar bone in a particular order, featuring similar appearances across adjacent and bilaterally symmetric teeth. However, existing tooth segmentation methods ignored such specific anatomical topology, which hampers the segmentation accuracy. Here we propose a semantic graph-based method to explicitly model the spatial associations between different anatomical targets (i.e., teeth) for their precise delineation in a coarse-to-fine fashion. First, to efficiently control the bilaterally symmetric confusion in segmentation, we employ a lightweight network to roughly separate teeth as four quadrants. Then, designing a semantic graph attention mechanism to explicitly model the anatomical topology of the teeth in each quadrant, based on which voxel-wise discriminative feature embeddings are learned for the accurate delineation of teeth boundaries. Extensive experiments on a clinical dental CBCT dataset demonstrate the superior performance of the proposed method compared with other state-of-the-art approaches.

Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks

IntroductionCone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT data sets. MethodsCBCT data sets from routine clinical operations (maxilla, mandible, or both) performed from January to October 2020 were retrospectively screened and selected. A 2-step approach was used for automatic PAL detection. First, tooth localization and identification were performed using the SpatialConfiguration-Net based on heatmap regression. Second, binary segmentation of lesions was performed using a modified U-Net architecture. A total of 144 CBCT images were used to train and test the networks. The method was evaluated using the 4-fold cross-validation technique. ResultsThe success detection rate of the tooth localization network ranged between 72.6% and 97.3%, whereas the sensitivity and specificity values of lesion detection were 97.1% and 88.0%, respectively. ConclusionsAlthough PALs showed variations in appearance, size, and shape in the CBCT data set and a high imbalance existed between teeth with and without PALs, the proposed fully automated method provided excellent results compared with related literature.

Unsupervised Cross-Modality Domain Adaptation Network for X-Ray to CT Registration.

2D/3D registration that achieves high accuracy and real-time computation is one of the enabling technologies for radiotherapy and image-guided surgeries. Recently, the Convolutional Neural Network (CNN) has been explored to significantly improve the accuracy and efficiency of 2D/3D registration. A pair of intraoperative 2-D x-ray images and synthetic data from pre-operative volume are often required to model the nonconvex mappings between registration parameters and image residual. However, a large clinical dataset collection with accurate poses for x-ray images can be very challenging or even impractical, while exclusive training on synthetic data can frequently cause performance degradation when tested on x-rays. Thus, we propose to train a model on source domain (i.e., synthetic data) to build appearance-pose relationship first and then use an unsupervised cross-modality domain adaptation network (UCMDAN) to adapt the model to target domain (i.e., X-rays) through adversarial learning. We propose to narrow the significant domain gap by alignment in both pixel and feature space. In particular, the image appearance transformation and domain-invariance feature learning by multiple aspects are conducted synergistically. Extensive experiments on CT and CBCT dataset show that the proposed UCMDAN outperforms the existing state-of-the-art domain adaptation approaches.

Alternate Level Set Evolutions With Controlled Switch for Tooth Segmentation

CBCT images have been widely used in digital orthodontics. In CBCT images, there are some tooth boundaries of blurriness and disappearance, and the teeth are of intensity inhomogeneity. In order to identify these boundaries and segment teeth, this paper develops an approach of alternate level set evolutions with controlled switch via slice-by-slice tooth segmentation: the user selects a voxel inside a tooth to initialize a small circle encircling the voxel, and the curve is automatically expanded until the tooth surface is formed. In each slice, the curve is expanded by two level set evolutions: the first level set evolution handles the intensity inhomogeneity and the second level set evolution solves the boundary blurriness and disappearance of the teeth. Experiments showed that the proposed method successfully segmented 12 CBCT datasets of 356 teeth and outperformed the geodesic active contour method and the popular method and the state-of-the-art method.

How does CBCT reconstruction algorithm impact on deformably mapped targets and accumulated dose distributions?

PurposeWe performed quantitative analysis of differences in deformable image registration (DIR) and deformable dose accumulation (DDA) computed on CBCT datasets reconstructed using the standard (Feldkamp‐Davis‐Kress: FDK_CBCT) and a novel iterative (iterative_CBCT) CBCT reconstruction algorithms.MethodsBoth FDK_CBCT and iterative_CBCT images were reconstructed for 323 fractions of treatment for 10 prostate cancer patients. Planning CT images were deformably registered to each CBCT image data set. After daily dose distributions were computed, they were mapped to planning CT to obtain deformed doses. Dosimetric and image registration results based CBCT images reconstructed by two algorithms were compared at three levels: (A) voxel doses over entire dose calculation volume, (B) clinical constraint results on targets and sensitive structures, and (C) contours propagated to CBCT images using DIR results based on three algorithms (SmartAdapt, Velocity, and Elastix) were compared with manually delineated contours as ground truth.Results(A) Average daily dose differences and average normalized DDA differences between FDK_CBCT and iterative_CBCT were ≤1 cGy. Maximum daily point dose differences increased from 0.22 ± 0.06 Gy (before the deformable dose mapping operation) to 1.33 ± 0.38 Gy after the deformable dose mapping. Maximum differences of normalized DDA per fraction were up to 0.80 Gy (0.42 ± 0.19 Gy). (B) Differences in target minimum doses were up to 8.31 Gy (−0.62 ± 4.60 Gy) and differences in critical structure doses were 0.70 ± 1.49 Gy. (C) For mapped prostate contours based on iterative_CBCT (relative to standard FDK_CBCT), dice similarity coefficient increased by 0.10 ± 0.09 (p < 0.0001), mass center distances decreased by 2.5 ± 3.0 mm (p < 0.00005), and Hausdorff distances decreased by 3.3 ± 4.4 mm (p < 0.00015).ConclusionsThe new iterative CBCT reconstruction algorithm leads to different mapped volumes of interest, deformed and cumulative doses than results based on conventional FDK_CBCT.

CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement.

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

Modern 3D cephalometry in pediatric orthodontics-downsizing the FOV and development of a new 3D cephalometric analysis within a minimized large FOV for dose reduction.

Dose reduction achieved by downsizing the field of view (FOV) in CBCT scans has brought no benefit for pediatric orthodontics, until now. Standard 2D or 3D full-size cephalometric analyses require large FOVs and high effective doses. The aim of this study was to compare a new 3D reduced-FOV analysis using the Frankfurt horizontal (FH) plane as reference plane with a conventional full-size analysis using the Sella-Nasion (S-N) plane as reference plane. Thirty-eight CBCT data sets were evaluated using full- and reduced-FOV analysis. The measurements of a total of 20 skeletal and dental standard 3D full-size variables were compared with the measurements of 22 corresponding 3D reduced-FOV variables. Statistical analysis was performed to prove mathematic relation between standard and alternative variables. Regression analyses were carried out. Coefficients of determination (R2) between 0.15 and 0.95 (p < 0.001-0.055) were described. All variables showed obvious relations of different strength except for SNA and its alternative Po_R-Or_R-A (°) (R2 = 0.15, p = 0.055), but a second variable Ba_A (mm) showed stronger relation (R2 = 0.28, p = 0.003). All standard variables related to the reference plane S-N could be described with alternative variables related to the FH. Further research should define more reliable landmarks for coordinate systems and reference points. Minimized large FOVs meet the demand of 3D cephalometric analyses and enable the application of CBCT scans in pediatric orthodontic patients in many specific indications. Dose reduction is accompanied by increasing access to all the advantages of 3D imaging over 2D imaging.

Does overweight affect the sagittal dimension of the posterior airway space in a non-OSAS population? A case control study.

ObjectiveThe null hypothesis was that, in a non‐obstructive sleep apnea syndrome population, overweight do not reduce the antero‐posterior dimension of the posterior airway space.Materials and MethodsThe author retrospectively reviewed the records of subjects evaluated at the Maxillofacial Surgery Unit, Department of Neurosciences, University of Padova Medical School, Padova, Italy, from 2016 to 2018. Only patients with complete demographic, anthropological and CBCT dataset were enrolled. OSAS patient were also ruled‐out. Enrolled patients were divided into overweight (28 cases) and non‐overweight (32 controls) groups according to the patient's Body Mass Index. Each two‐dimensional cephalometric radiography obtained from the cone‐beam computer tomography dataset was evaluated in order to measure linear and angular distances between standardized cephalometric landmarks. The two‐sample t‐test was the statistical test applied to compare the case and control data.ResultsThere were no statistical differences between the two study groups for any of the evaluated variables: the null hypothesis was accepted.ConclusionThis study showed that in a non‐obstructive sleep‐apnea population, overweight and class I obesity does not influence the airway space in the antero‐posterior dimension. Further investigation should focus on categorized overweight‐obese population. Accurate and reliable protocol for tridimensional airways assessment should be implemented.

A Prospective Study of the Dosimetric Impact and Quality of Life from Margin Reduction for Patients with Localized Prostate Cancer

To investigate the impact of reducing CTV to PTV margins on dosimetry and patient reported Quality of Life (QOL) for patients with localized prostate cancer. Twenty patients were included in a single institution IRB-approved prospective study. Nine were planned using reduced margins (5 mm uniformly except 4 mm at prostate/rectum boundary), and 11 were control patients with standard margins (10/6 mm). Cumulative delivered dose was calculated using deformable dose accumulation. Each daily CBCT dataset was deformed to the planning CT (pCT), dose was computed, and accumulated on the resampled pCT using a parameter-optimized, B-spline algorithm (Elastix, ITK/VTK). EPIC-26 based patient reported QOL data was collected pre-treatment, post-treatment, and at 2, 6, 12, 18, 24, 36, 48 and 60 months follow-up. Post-radiation therapy (RT) QOL scores were baseline corrected and standardized to a [0-100] scale using EPIC-26 methodology. QOL comparisons between the margin-reduced and the standard cohorts including the averages within each arm and the overall mean QOL differences between the groups (QOLM-R-QOLcontrol) were made at each time point. The median QOL follow-up length for the 20 patients was 48 months. Cumulative delivered dose and planned dose did not reach statistical significance (p>0.1) for targets and organs at risk, for both mean and maximum dose. At 4 years after RT, standardized and baseline-corrected mean QOLM-R-QOLcontrol were improved for “Urinary Incontinence”, “Urinary Irritative/Obstructive”, “Bowel”, and “Sexual” EPIC domains by 3.5, 14.8, 10.2, and 16.1, respectively (higher value is better). The control group showed larger PTV/rectum and PTV/bladder intersection volumes (7.2±5.8, 18.2±8.1 cc) than the margin-reduced group (2.6±1.8, 12.5±8.3 cc), though the dose to these intersection volumes did not reach statistical significance (p>0.1) between the groups. PTV/rectum intersection volume showed a moderate correlation (R = -0.6) to “Bowel” EPIC domain. Margin-reduced group exhibited clinically meaningful improvement of QOL without compromising the target dose coverage.Abstract 4152; TableTime PointUrinary IncontinenceUrinary Irritative/ObstructiveBowelSexualHormonalEnd of RT (N = 20)-6.413.46.36.6-2.62-month post RT (N = 20)3.18.26.68.3-3.26-month post RT (N = 19)-2.08.519.014.94.412-month post RT (N = 19)5.510.38.019.97.124-month post RT (N = 17)-1.59.515.614.6-13.036-month post RT (N = 16)5.616.59.020.2-13.848-month post RT (N = 13)-3.96.34.96.2-14.2Mean QOLM-R - QOLcontrol3.514.810.216.1-4.9Minimally important difference range [Skolarus et al. Urology 2015]6-95-74-610-124-6 Open table in a new tab

Vertical-guided bone regeneration with a titanium-reinforced d-PTFE membrane utilizing a novel split-thickness flap design: a prospective case series

ObjectivesTo evaluate the feasibility of a newly proposed minimally invasive split-thickness flap design without vertical-releasing incisions for vertical bone regeneration performed in either a simultaneous or staged approach and to analyze the prevalence of adverse events during postoperative healing.Materials and methodsFollowing preparation of a split-thickness flap and bilaminar elevation of the mucosa and underlying periosteum, the alveolar bone was exposed over the defects, vertical GBR was performed by means of a titanium-reinforced high-density polytetrafluoroethylene membrane combined with particulated autogenous bone (AP) and bovine-derived xenograft (BDX) in 1:1 ratio. At 9 months after reconstructive surgery, vertical and horizontal hard tissue gain was evaluated based on clinical and radiographic examination.ResultsTwenty-four vertical alveolar ridge defects in 19 patients were treated with vertical GBR. In case of 6 surgical sites, implant placement was performed at the time of the GBR (simultaneous group); in the remaining 18 surgical, sites implant placement was performed 9 months after the ridge augmentation (staged group). After uneventful healing in 23 cases, hard tissue fill was detected in each site. Direct clinical measurements confirmed vertical and horizontal hard tissue gain averaging 3.2 ± 1.9 mm and 6.5 ± 0.5 mm respectively, in the simultaneous group and 4.5 ± 2.2 mm and 8.7 ± 2.3 mm respectively, in the staged group. Additional radiographic evaluation based on CBCT data sets in the staged group revealed mean vertical and horizontal hard tissue fill of 4.2 ± 2.0 mm and 8.5 ± 2.4 mm. Radiographic volume gain was 1.1 ± 0.4 cm3.ConclusionVertical GBR consisting of a split-thickness flap and using titanium-reinforced non-resorbable membrane in conjunction with a 1:1 mixture of AP+BDX may lead to a predictable vertical and horizontal hard tissue reconstruction.Clinical relevanceThe used split-thickness flap design may represent a valuable approach to increase the success rate of vertical GBR, resulting in predicable hard tissue regeneration, and favorable wound healing with low rate of membrane exposure.

Retrospective analysis of the impact of respiratory motion in treatment margins for frameless lung SBRT based on respiratory-correlated CBCT data-sets.

PurposeTo investigate the impact of respiratory motion in the treatment margins for lung SBRT frameless treatments and to validate our treatment margins using 4D CBCT data analysis.MethodsTwo hundred and twenty nine fractions with early stage NSCLC were retrospectively analyzed. All patients were treated in frameless and free breathing conditions. The treatment margins were calculated according to van Herk equation in Mid‐Ventilation. For each fraction, three 4D CBCT scans, pre‐ and postcorrection, and posttreatment, were acquired to assess target baseline shift, target localization accuracy and intra‐fraction motion errors. A bootstrap analysis was performed to assess the minimum number of patients required to define treatment margins.ResultsThe retrospectively calculated target‐baseline shift, target localization accuracy and intra‐fraction motion errors agreed with the literature. The best tailored margins to our cohort of patients were retrospectively computed and resulted in agreement with already published data. The bootstrap analysis showed that fifteen patients were enough to assess treatment margins.ConclusionsThe treatment margins applied to our patient’s cohort resulted in good agreement with the retrospectively calculated margins based on 4D CBCT data. Moreover, the bootstrap analysis revealed to be a promising method to verify the reliability of the applied treatment margins for safe lung SBRT delivery.

How Does CBCT Image Quality Impact on Deformably Mapped Targets and Accumulated Dose Distributions?

We perform quantitative analysis of differences in deformable image registration (DIR) and deformable dose accumulation (DDA) computed on CBCT datasets reconstructed using the standard (Feldkamp-Davis-Kress: FDK_CBCT) and a novel iterative (iterative_CBCT) CBCT reconstruction algorithms. Nine head/neck (H&N) and 10 prostate cancer patients were randomly selected for retrospective analysis. Both FDK_CBCT and iterative_CBCT images were reconstructed for 608 fractions of treatment. Planning CT images were deformably registered to CBCT image sets using an intensity-based, B-spline algorithm (Elastix ITK/VTK) with optimized parameters. After dose distributions were computed for each fraction on both deformed CT image sets, they were mapped to the planning CT to evaluate the deformed dose. Results based on FDK_CBCT and iterative_CBCT images were compared before and after the dose mapping operation, respectively. Dose constraints for targets and critical structures were evaluated using DDA based on different CBCT images. Dice similarity coefficient (DSC), mass-center distance, and Hausdorff distance analysis of the prostate gland deformed from the planning CT to CBCT were compared against manually delineated contours. Differences due to CBCT images reconstructed with standard and iterative modes were evaluated. Average daily dose differences and normalized DDA differences were ≤1 cGy. Comparing with results before dose mapping operation, maximum daily point dose differences increased from 0.81 ± 0.54 Gy to 1.19 ± 0.48 Gy for H&N patients, and increased from 0.22 ± 0.06 Gy to 1.33 ± 0.38 Gy for prostate patients after the dose mapping operation. Maximum normalized DDA differences were up to 0.43 Gy (0.29 ± 0.09 Gy) and 0.80 Gy (0.42 ± 0.19 Gy) for H&N and prostate patients, respectively. Differences in target minimum doses were up to 0.82 Gy (0.47 ± 0.32 Gy) and 8.31 Gy (-0.62 ± 4.60 Gy) and differences in critical structure doses were 0.10 ± 0.55 Gy and 0.70 ± 1.49 Gy, for H&N and prostate patients, respectively. It is noteworthy that the largest differences occurred in regions with the highest dose gradients, as expected. Comparison between mapped prostate gland and manually defined contours showed that for iterative_CBCT (relative to standard FDK_CBCT), DSC increased by 0.18 ± 0.11 (from mean 0.57 to 0.75), mass-center distances decreased by 4.4 ± 3.3 mm, and Hausdorff distances decreased by 6.1 ± 5.1 mm. Better image quality afforded with iterative_CBCT enhances accuracy of DIR between planning CT and CBCT. This will improve accuracy of mapped volumes of interest, deformed dose distribution, and DDA. iCBCT has bigger effect on image quality in regions like the prostate because the increased patient scatter in such areas has a larger impact on image quality degradation versus sites like the head/neck.

The Use of Three-Dimensional (3D) Biometric Measurements to Predict Additional Alignment Procedures in Total Ankle Replacement

Category:Ankle ArthritisIntroduction/Purpose:Preoperative evaluation of ankle and hindfoot deformities in patients undergoing total ankle replacement (TAR) is challenging. Characterizing deformity based on conventional radiographs is limited by its two-dimensional (2D) nature and is influenced by anatomical and operator-related bias. The final decision to perform associated corrective alignment procedures, that include calcaneal osteotomies, midfoot and forefoot osteotomies/fusions, and soft tissue balancing, is made intraoperatively following insertion of the TAR components. The use of weightbearing cone beam CT (WB CBCT) images and three-dimensional (3D) biometric tools, encompassing the hindfoot alignment, foot tripod, and center of the ankle joint, may be an effective surgical planning instrument. In this retrospective study, we assessed the ability of these tools to predict additional procedures performed at the time of TAR.Methods:In this retrospective study, we enrolled 22 patients that underwent TAR and had preoperative WB CBCT studies. We excluded seven patients with isolated ankle CBCT images and three with metallic hindfoot implants. Patients demographics, type and number of additional alignment procedures were noted. The WB CBCT data sets were screened using built-in semi-automatic measurement software. 3D coordinates (x/y/z) were identified for the specific anatomical landmarks required to calculate Foot and Ankle Offset (FAO), which included the WB points of the first, fifth metatarsal heads and calcaneus, as well as the highest point of the talar dome. FAO is a representation of the torque (offset) between the hindfoot/forefoot midline and the center of the talus, and is given as a percentage of foot-length. Patients were then divided into varus and valgus hindfoot alignment groups accordingly to their measured FAO, using available literature FAO values for normally aligned feet (2.3%, ±2.9%).Results:We included 12 patients (6F/6 M), mean age 65 (range, 47-80). The mean preoperative FAO was 5.73 (CI -0.99 - 12.55). Seven patients had valgus and 5 patients had varus alignment of the hindfoot. The mean number of additional procedures was 2 (range, 0 to 5), and included: calcaneal osteotomy (58%), Achilles lengthening/Gastroc recession (58%), Cotton osteotomy (25%), First TMT fusion (17%), talonavicular fusion, naviculo-cuneiform fusion, and Brostrom procedure (8%). The FAO positively correlated with occurrence of additional alignment procedures (p=0.003). Patients with valgus hindfoot alignment had significantly increased number of additional procedures (2.7; CI 1.6 – 3.8) when compared to varus alignment patients (1.2; CI -0.1 – 2.5) (p=0.03), and were found to have a 1.8 fold greater chance to have a calcaneal osteotomy.Conclusion:This is the first study to evaluate the role of 3D biometric tools and semi-automatic WB CBCT measurements in the preoperative assessment of foot alignment in patients undergoing TAR. We found that increased Foot and Ankle Offset (FAO) significantly predicts the occurrence and number of additional alignment procedures, including calcaneal osteotomies. We believe that the use of biometrics and semi-automatic measurements, which account for the relationship between the center of the ankle and the tripod of the foot, can enhance the preoperative assessment, surgical planning and outcomes of TAR patients. Prospective and postoperative studies demonstrating correction are needed.

Real-time respiratory triggered four dimensional cone-beam CT halves imaging dose compared to conventional 4D CBCT

Four dimensional cone-beam computed tomography (4D CBCT) improves patient positioning and the accuracy of radiation therapy for patients with mobile tumours. Generally, 4D CBCT requires many hundreds of x-ray projections to measure target trajectories and the imaging frequency is not adapted to the patient’s respiratory signal resulting in over-sampling. In contrast, respiratory triggered 4D CBCT (RT 4D CBCT) is an acquisition technique that has been experimentally implemented and has shown to reduce the number of x-ray projections and thus 4D CBCT dose with minimal impact on image quality. The aim of this work is to experimentally investigate RT 4D CBCT in situ and measure target trajectory mean position, image quality and imaging dose from this approach. A commercially available phantom with programmable target motion was programmed with nine target trajectories derived from patient-measured respiratory traces known to span the range of image quality when used for 4D CBCT reconstruction. 4D CBCT datasets were acquired for each target trajectory using the RT 4D CBCT acquisition technique and the conventional 4D CBCT acquisition technique. From the reconstructed 4D CBCT datasets, target trajectory mean positions, imaging dose and image quality metrics were calculated and compared between the two techniques. Target trajectory and mean position were measured by tracking the target’s displacement in the phantom; imaging dose was measured by counting the total number of x-ray projections acquired; and image quality was assessed by calculating the contrast-to-noise ratio (CNR), signal-to-noise ration (SNR) and edge response width (ERW). For each of the nine cases, the target trajectory mean position as determined by RT 4D CBCT and conventional 4D CBCT varied from the reference source trajectory mean position by 0.7 mm or less except for one case where a conventional 4D CBCT mean position varied by 1.3 mm. On the average of these nine studies, RT 4D CBCT required half as many projections as conventional 4D CBCT giving a 50% reduction in imaging dose. Overall, the image quality metrics (CNR and SNR) were marginally worse for RT 4D CBCT; ERW metric showed no statistically significant difference between the RT 4D CBCT and conventional 4D CBCT reconstructed datasets. Respiratory triggered 4D CBCT couples the real-time respiratory signal to the 4D CBCT image acquisition system and requires less imaging dose than conventional 4D CBCT to determine target trajectory mean positions.

Quantification of temporal resolution improvement factor in SMART-RECON based time-resolved C-arm Cone beam computed tomography angiography (TR-CBCTA)

In a recently published paper by Li et al (2018 Phys. Med. Biol. 63 075001), reconstruction parameters were optimized for the so-called synchronized multiArtifact reduction with tomographic reconstruction (SMART-RECON) method to enable time-resolved Cone beam computed tomography angiography (TR-CBCTA) from a single short-scan CBCT data set. However, the paper did not quantitatively address how much temporal resolution can be improved by using the SMART-RECON algorithm in TR-CBCTA. The purpose of this note was to present a method to quantify this temporal resolution improvement factor and to use that method to quantify the improvement in temporal resolution using SMART-RECON and optimized reconstruction parameters. In the method proposed in this note, the potential temporal blurring caused by SMART-RECON was modeled as a convolution between a temporal Gaussian blurring kernel and the ground truth temporal enhancement curve. The width parameter of the resulting Gaussian blurring kernel was used as a surrogate metric for temporal resolution to quantify the achievable temporal resolution for the conventional filtered backprojection (FBP) and SMART-RECON methods. The ratio of the surrogate temporal resolutions for the two reconstruction methods was calculated to quantify the factor of temporal resolution improvement. The quantitative results show that the temporal resolution is improved by a factor of 4.5 using SMART-RECON compared with FBP.