A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software.

Purpose: Cone Beam CT (CBCT) kilovoltage imaging devices are increasingly available for daily imaging in radiotherapy departments. Flat‐panel based CBCT scanners present a distinctive set of artifacts due mostly to increased scatter, longer data acquisition time and reduced detector quantum efficiency as compared to helical Fan Beam CT (FBCT) systems. Our purpose is to characterize image quality from FBCT and CBCT scanners based on noise, contrast and dose, using FBCT as a benchmark. Method and Materials: we acquired phantom and clinical patient images with a CBCT Varian On‐Board Imager as well as with a FBCT Picker PQ5000 single‐row helical scanner. The CBCT scanner was equipped with antiscatter grid and bowtie filter. By comparing CBCT and FBCT images of a high contrast resolution insert, the CBCT reconstruction voxel size and filter were adjusted until the spatial resolution of the FBCT and CBCT images was approximately matched. Dose was measured with standard CTDI and Farmer chambers. Noise, contrast and SNR were evaluated and compared. Results: CBCT images of both phantom and patient were relatively free of streaking and cupping artifacts, indicating that the grid had successfully attenuated most of the scatter. Low contrast detectability threshold is similar for the two modalities, when CBCT dose is about twice as large as FBCT. Noise and non‐uniformities are more prevalent in patient CBCT images, but pelvic soft tissue structures are well discernible. For patient and phantom images Dose×SNR2 is about 4 times lower for FBCT than in CBCT, which is about 1.5–2 times larger than expected, given the measured grid transmission and detector quantum efficiency. Conclusion: In this study, resolution‐matched CBCT and FBCT images could exhibit similar SNRs and contrast‐to‐noise ratios through a combination of increased imaging dose and reduced spatial resolution.

The present study is based on evidence indicating a potential correlation between cone-beam CT (CBCT) measurements of tumor size, shape, and the stage of locally advanced rectal cancer. To further investigate this relationship, the study quantitatively assesses the correlation between positioning CT (pCT) and CBCT in the radiomics features of these cancers, and examines their potential for substitution. In this study, 103 patients diagnosed with locally advanced rectal cancer and undergoing neoadjuvant chemoradiotherapy were selected as participants. Their CBCT and pCT images were used to divide the participants into two groups: a training set and a validation set, with a 7:3 ratio. An improved conventional 3D-RUNet (CLA-UNet) deep learning model was trained on the training set data and then applied to the validation set. The DSC, HD95 and ASSD were calculated for quantitative evaluation purposes. Then, radiomics features were extracted from 30 patients of the test set. The experiments demonstrate that, the modified model achieves an average DSC score 0.792 for pCT and 0.672 for CBCT scans. 1037 features were extracted from each patient's CBCT and pCT images, 73 image features were found to have R values greater than 0.9, including three features related to the staging and prognosis of rectal cancer. In this study, we proposed an automatic, fast, and consistent method for rectal cancer GTV segmentation for pCT and CBCT scans. The findings of radiomic results indicate that CBCT images have significant research value in the field of radiomics.

BackgroundThe emergence of robotic Cone Beam Computed Tomography (CBCT) imaging systems in trauma departments has enabled 3D anatomical assessment of musculoskeletal injuries, supplementing conventional 2D fluoroscopic imaging for examination, diagnosis, and treatment planning. To date, the primary focus has been on trauma sites in the extremities.PurposeTo determine if CBCT images can be used during the treatment planning process in spinal instrumentation and laminectomy procedures, allowing accurate 3D‐printed pedicle screw and laminectomy drill guides to be generated for the cervical and thoracic spine.MethodsThe accuracy of drill guides generated from CBCT images was assessed using animal cadavers (ovine and porcine). Preoperative scans were acquired using a robotic CBCT C‐arm system, the Siemens ARTIS pheno (Siemens Healthcare, GmbH, Germany). The CBCT images were imported into 3D‐Slicer version 4.10.2 (www.slicer.org) where vertebral models and specific guides were developed and subsequently 3D‐printed. In the ovine cadaver, 11 pedicle screw guides from the T1–T5 and T7–T12 vertebra and six laminectomy guides from the C2–C7 vertebra were planned and printed. In the porcine cadaver, nine pedicle screw guides from the C3–T4 vertebra were planned and printed. For the pedicle screw guides, accuracy was assessed by three observers according to pedicle breach via the Gertzbein–Robbins grading system as well as measured mean axial and sagittal screw error via postoperative CBCT and CT scans. For the laminectomies, the guides were designed to leave 1 mm of lamina. The average thickness of the lamina at the mid‐point was used to assess the accuracy of the guides, measured via postoperative CBCT and CT scans from three observers. For all measurements, the intraclass correlation coefficient (ICC) was calculated to determine observer reliability.ResultsCompared with the planned screw angles for both the ovine and porcine procedures (n = 32), the mean axial and sagittal screw error measured on the postoperative CBCT scans from three observers were 3.9 ± 1.9° and 1.8 ± 0.8°, respectively. The ICC among the observes was 0.855 and 0.849 for the axial and sagittal measurements, respectively, indicating good reliability. In the ovine cadaver, directly comparing the measured axial and sagittal screw angle of the visible screws (n = 14) in the postoperative CBCT and conventional CT scans from three observers revealed an average difference 1.9 ± 1.0° in axial angle and 1.8 ± 1.0° in the sagittal angle. The average thickness of the lamina at the middle of each vertebra, as measured on‐screen in the postoperative CBCT scans by three observes was 1.6 ± 0.2 mm. The ICC among observers was 0.693, indicating moderate reliability. No lamina breaches were observed in the postoperative images.ConclusionHere, CBCT images have been used to generate accurate 3D‐printed pedicle screw and laminectomy drill guides for use in the cervical and thoracic spine. The results demonstrate sufficient precision compared with those previously reported, generated from standard preoperative CT and MRI scans, potentially expanding the treatment planning capabilities of robotic CBCT imaging systems in trauma departments and operating rooms.

Endodontic Working Length Measurement Using Cone-beam Computed Tomographic Images Obtained at Different Voxel Sizes and Field of Views, Periapical Radiography, and Apex Locator: A Comparative Ex Vivo Study

BackgroundManual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft and hard tissue craniofacial landmarks on CBCT in patients with various types of malocclusion.MethodsA total of 498 CBCT images were collected. Following the calibration procedure, two experienced clinicians identified 43 landmarks in the x-, y-, and z-coordinate planes on the CBCT images using Checkpoint Software, creating the ground truth by averaging the landmark coordinates. To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR).ResultsEach landmark prediction took approximately 4.2 s on a conventional graphics processing unit. The mean absolute error across all coordinates was 0.74 mm. The overall MRE for the 43 landmarks was 1.76 ± 1.13 mm, and the SDR was 60.16%, 91.05%, and 97.58% within 2-, 3-, and 4-mm error ranges of manual marking, respectively. The average MRE of the hard tissue landmarks (32/43) was 1.73 mm, while that for soft tissue landmarks (11/43) was 1.84 mm.ConclusionsOur proposed algorithm demonstrates a clinically acceptable level of accuracy and robustness for automatic detection of CBCT soft- and hard-tissue landmarks across all types of malformations. The potential for artificial intelligence to assist in identifying three dimensional-CT landmarks in routine clinical practice and analysing large datasets for future research is promising.

To present the ballistic gelatin as a new material capable of simulating the soft tissues in cone-beam CT (CBCT) images. CBCT images of three piglet heads were acquired with their soft tissues intact (standard group). Subsequently, the piglet heads were fixed in a container using metallic pins and moulded with acrylic resin; the soft tissues were then removed and replaced by ballistic gelatin, with the same thickness of the original soft tissues. The images were evaluated by two oral radiologists, to check the adaptation on bone surfaces, thickness and density, penetration into large bone cavities and cancellous bone, and the presence of air bubbles using a 5-score scale. Additionally, an objective analysis was carried out by one oral radiologist. For each CBCT scan, three axial reconstructions were selected to represent the mandibular, occlusal, and maxillary levels. The mean and standard deviation (SD) of the grey values were calculated in four regions of interest determined on soft tissue areas and compared by two-way ANOVA. The ballistic gelatin showed subjective scores ranging from good to excellent for all parameters evaluated. There was no significant difference in the mean and SD values of the grey values between ballistic gelatin and the gold standard groups for all levels (p > 0.05). Higher SD values were observed in the occlusal level for both groups (p < 0.05). Ballistic gelatin has visual and objective similarity with the gold standard. Thus, the ballistic gelatin is a promising material capable of simulating soft tissues in CBCT images.

Residents' journal review

This article compares the treatment outcome between two patients when en-masse intrusion and retraction of the maxillary anterior teeth were performed with mini-implants placed at different sites. Both the patients had a skeletal Class II malocclusion with Angle's Class I molar relation, Class I canine relation, proclination of upper and lower anterior teeth. In the first case, intrusion and retraction was done with conventional friction mechanics for space closure and an intrusive force from a mini-implant placed in the midline. In the second case, intrusion and retraction were done with the application of force from the anterior attachments to mini-implants placed between the second premolars and first permanent molar bilaterally. There was intrusion of the maxillary anterior teeth with both mechanics. However, the evaluation of treatment outcome at the end of space closure showed mesial movement of the maxillary first permanent molar in the first case. In the second case, distal movement and intrusion of the maxillary first permanent molar and decrease in the lower anterior facial height was noted. The mechanics in the first case can be applied for intrusion and retraction of maxillary anterior teeth when anchorage requirement is not critical. The mechanics described in the second case can be used for intrusion and retraction of maxillary anterior teeth in cases with a very high anchorage need and will be beneficial in patients with vertical growth pattern.

PurposeThe target detectability of cone beam computed tomography (CBCT) performed in image‐guided radiation therapy (IGRT) was investigated to achieve sufficient image quality for patient positioning over a course of treatment session while maintaining radiation exposure from CBCT imaging as low as reasonably achievable (ALARA).MethodsBody CBCT scans operated in half‐fan mode were acquired with three different protocols: CBCTlowD, CBCTmidD, and CBCThighD, which resulted in weighted CT dose index (CTDIw) of 0.36, 1.43, and 2.78 cGy, respectively. An electron density phantom that is 18 cm in diameter was covered by four layers of 2.5‐cm‐thick bolus to simulate patients of different body sizes. Multivariate analysis was used to examine the impact of body size, radiation exposure, and tissue type on the target detectability of CBCT imaging, quantified as contrast‐to‐noise ratio (CNR).ResultsCBCTmidD allows sufficient target detection of adipose, breast, muscle, liver in a background of water for normal‐weight adults with cross‐sectional diameter less than 28 cm, while CBCThighD is suitable for adult patients with larger body sizes or body mass index over 25 kg/m2. Once the cross‐sectional diameter of adult patients is larger than 35 cm, the CTDIw of CBCT scans should be higher than 2.78 cGy to achieve required CNR. As for pediatric and adolescent patients with cross‐sectional diameter less than 25 cm, CBCTlowD is able to produce images with sufficient target detection.ConclusionThe target detectability of soft tissues in default CBCT scans may not be sufficient for overweight or obese adults. Contrary, pediatric and adolescent patients would receive unnecessarily high radiation exposure from default CBCT scans. Therefore, the selection of acquisition parameters for CBCT scans optimized according to patient body size was proposed to ensure sufficient image quality for daily patient positioning in radiation therapy while achieving the ALARA principle.

ObjectivesThe study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.MethodsIn 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.ResultsF1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.ConclusionsModels based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

Background: Cone beam computed tomography (CBCT) is a three-dimensional radiographic imaging technique that is commonly being used in diagnosis and treatment planning in various fields of dentistry. Incidental findings on CBCT images are frequently reported in the literature and are important to assess before treatment planning. Aims: To record types and prevalence of incidental findings in the maxillofacial region, identified on CBCT scans and described in radiologist's consultation reports. Settings and Design: A total of 147 CBCT scan reports on 59 males and 88 females between June 2007 and February 2012 at University of California, San Francisco, were analyzed retrospectively. Methods and Material: 147 patient reports by Board certified oral radiologists on CBCT scans taken with the Hitachi CB MercuRay. The incidental findings were categorized and analysed using descriptive statistics. Statistical Analysis Used: Logistic regression analysis was used to compare the rate of temporomandibular joint (TMJ) pathologies between females and males. Results: The overall rate of incidental findings was 40.1%. The age range of patients was from 8 years to 80 years. The highest rate of incidental findings was in the sinus region (51.7%), followed by dento-alveolar region (34.01%), TMJ region (26.53%), osseous region (15.64%), calcification of ligaments, pineal gland, and carotid artery (12.92%), dental anomalies (10.88%), nasal region (8.84%), and airway region (5.44%). According to logistic regression analysis, females were 2.58 times more likely to exhibit TMJ pathology compared to males ( P value = 0.02). Conclusions: CBCT scans are beneficial in revealing incidental abnormalities in the head and neck region outside the scope of interest. Careful review of the entire CBCT image is essential to avoid under- or overestimation of potential complications in providing comprehensive health care.

BackgroundTo investigate the feasibility of contouring the planning risk organ volume (PRV) for the heart, and to determine the probability of evaluating radiation dose to the heart using kilovoltage-cone beam computed tomography (kV-CBCT) in early-stage non-small-cell lung cancer (NSCLC) patients, who received stereotactic body radiotherapy (SBRT).Materials and methodsSeventeen NSCLC patients who received SBRT (5Gy/f × 10f dose) were enrolled and subjected to CBCT and CT imaging analyses to plan treatment. Sequential planning CBCT images of individual patient’s hearts were analyzed for reproducibility of heart contouring and volume. Comparative analyses were made between the planning CT- and CBCT-detected heart margins and dose-volume indices for treatment.ResultsThe heart volume from planning CT images was significantly smaller than that from CBCT scans (p < 0.05), and the volumes based on the different series of CBCT images were similar (p > 0.05).The overlap of the heart region on the same anatomical section between the first series of CBCT scans and other scans reached 0.985 ± 0.020 without statistically significant differences (p > 0.05). The mean margins of the heart from planning CT and CBCT scans were 10.5 ± 2.8 mm in the left direction, 5.9 ± 2.8 mm in the right direction, 2.2 ± 1.6 mm in the direction of the head, 3.3 ± 2.2 mm in the direction of the foot, 6.7 ± 1.1 mm in the anterior direction, and 4.5 mm ± 2.5 mm in the posterior direction. All relative and absolute dose-volume indices obtained from CBCT images were significantly larger than those from planning CT scans (p < 0.05), with the exception of the volume in the 5Gy region.ConclusionThe PRV of heart contouring based on kV-CBCT is feasible with good reproducibility. More accurate and objective dose-volume indices may be obtained for NSCLC patients by using kV-CBCT, instead of CT, to plan SBRT.

Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.

Residents’ journal review

Purpose: The introduction of cone‐beam CT (CBCT) based IGRT has greatly improved our knowledge of patient anatomy during treatment. However, the calculation of dose distributions based on CBCT images is problematic, due to poor consistency of density calibration and image artefacts. A method for correcting CBCT images for use in dose calculations has been developed and implemented at our institution. Method and Materials: Post processing of the Elekta Synergy CBCT scans was performed to remove artefacts and spurious density variations. In the algorithm the pre‐treatment CT scan was used as a reference for the density of regions in the CBCT image, providing a ‘shading map’ of the differences between the two. Smoothing of the shading map was performed to remove high frequency content. Testing of the correction algorithm compared density values for corresponding tissue types, certified that non‐artefact density changes are maintained, and compared dose calculations based on corrected CBCT scans to pre‐treatment CT calculations. Results: CBCT based dose calculations have been developed for prostate &amp; lung treatment sites. For both sites the densities within the corrected CBCT scan were closer to those in the pre‐treatment CT scan. In the example of prostate patients CT numbers in regions of fat and muscle tissue in the corrected CBCT were within 1% of the values in the planning CT, as opposed to 10–20% different for the un‐corrected CBCT. The improvement in the density within the CBCT scan improves the dosimetric quality of the scan, providing a means of more automated treatment planning based on CBCT scans. Conclusion: A method for correcting CBCT scans for re‐calculation of delivered dose has been developed and validated. The enhanced CBCT scans greatly improve the agreement between CBCT calculated doses and the expectation based on the planning CT.