Using cone-beam CT for appropriate nostril selection in nasotracheal intubation

OBJECTIVES

Nasotracheal intubation is a standard blind procedure associated with various complications. The selection of the appropriate nostril is crucial to preventing most of these complications. The present study aimed to evaluate the predictive ability of cone-beam CT (CBCT) images to select the correct nostril for nasotracheal intubation.

METHODS

The study encompassed 60 patients who underwent maxillofacial surgery with nasotracheal intubation under general anaesthesia. While the anaesthetist made the appropriate nostril selection clinically according to a simple occlusion test and spatula test, the radiologist made the selection after analysing various CBCT findings such as the angle and direction of nasal septum deviation (NSD), minimum bone distance along the intubation path, and the presence of inferior turbinate hypertrophy. The appropriateness of these choices made blindly at different times was evaluated using descriptive statistics, chi-squared test, and independent samples t-test.

RESULTS

The study found that 83.3% of the suggested nostril intubations were successful. We also observed that intubation duration was longer when inferior turbinate hypertrophy was present (P = .031). However, there was no statistical relationship between the presence of epistaxis and septal deviation (P = .395). Nonetheless, in 64.3% of cases with epistaxis, the intubated nostril and the septum deviation direction were the same.

CONCLUSIONS

Pre-operative evaluations using CBCT can aid anaesthetists for septum deviation and turbinate hypertrophy, as both can impact intubation success rates and duration.

Automatic classification and segmentation of multiclass jaw lesions in cone-beam CT using deep learning

OBJECTIVES

To develop and validate a modified deep learning (DL) model based on nnU-Net for classifying and segmenting five-class jaw lesions using cone-beam CT (CBCT).

METHODS

A total of 368 CBCT scans (37 168 slices) were used to train a multi-class segmentation model. The data underwent manual annotation by two oral and maxillofacial surgeons (OMSs) to serve as ground truth. Sensitivity, specificity, precision, F1-score, and accuracy were used to evaluate the classification ability of the model and doctors, with or without artificial intelligence assistance. The dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and segmentation time were used to evaluate the segmentation effect of the model.

RESULTS

The model achieved the dual task of classifying and segmenting jaw lesions in CBCT. For classification, the sensitivity, specificity, precision, and accuracy of the model were 0.871, 0.974, 0.874, and 0.891, respectively, surpassing oral and maxillofacial radiologists (OMFRs) and OMSs, approaching the specialist. With the model's assistance, the classification performance of OMFRs and OMSs improved, particularly for odontogenic keratocyst (OKC) and ameloblastoma (AM), with F1-score improvements ranging from 6.2% to 12.7%. For segmentation, the DSC was 87.2% and the ASSD was 1.359 mm. The model's average segmentation time was 40 ± 9.9 s, contrasting with 25 ± 7.2 min for OMSs.

CONCLUSIONS

The proposed DL model accurately and efficiently classified and segmented five classes of jaw lesions using CBCT. In addition, it could assist doctors in improving classification accuracy and segmentation efficiency, particularly in distinguishing confusing lesions (eg, AM and OKC).

In vitro accuracy of ultra-low dose cone-beam CT for detection of proximal caries

OBJECTIVES

This study aimed to assess the accuracy of ultra-low dose (ULD) cone-beam CT (CBCT) for detection of proximal caries.

METHODS

This in vitro study evaluated 104 molar and premolar teeth. The teeth were mounted in dry skulls and underwent CBCT with 4 protocols of high-resolution (HR), normal (NORM), ULD-HR, and ULD-NORM; 78 CBCT images were scored by 3 observers for the presence and penetration depth of caries twice with a 2-week interval using a 5-point Likert scale. The teeth were then sectioned and observed under a stereomicroscope (gold standard). The 4 protocols were compared with each other and with the gold standard. The receiver operating characteristic curve was drawn, and the area under the curve (AUC) was calculated and compared by the Chi-square test (alpha = .05).

RESULTS

The interobserver agreement ranged from 0.5233 to 0.6034 for ULD-NORM, 0.5380 to 0.6279 for NORM, 0.5856 to 0.6300 for ULD-HR, and 0.6614 to 0.7707 for HR images. The intra-observer agreement ranged from 0.6027 to 0.8812 for ULD-HR, 0.7083 to 0.7556 for HR, 0.6076 to 0.9452 for ULD-NORM, and 0.7012 to 0.9221 for NORM images. Comparison of AUC revealed no significant difference between NORM and ULD-NORM (P > .05), or HR and ULD-HR (P > .05). The highest AUC belonged to HR (0.8529) and the lowest to NORM (0.7774).

CONCLUSIONS

Considering the significant reduction in radiation dose in ULD CBCT and its acceptable diagnostic accuracy for detection of proximal caries, this protocol may be used for detection of proximal carious lesions and assessment of their depth.

An artificial intelligence grading system of apical periodontitis in cone-beam computed tomography data

OBJECTIVES

In order to assist junior doctors in better diagnosing apical periodontitis (AP), an artificial intelligence AP grading system was developed based on deep learning (DL) and its reliability and accuracy were evaluated.

METHODS

One hundred and twenty cone-beam computed tomography (CBCT) images were selected to construct a classification dataset with four categories, which were divided by CBCT periapical index (CBCTPAI), including normal periapical tissue, CBCTPAI 1-2, CBCTPAI 3-5, and young permanent teeth. Three classic algorithms (ResNet50/101/152) as well as one self-invented algorithm (PAINet) were compared with each other. PAINet were also compared with two recent Transformer-based models and three attention models. Their performance was evaluated by accuracy, precision, recall, balanced F score (F1-score), and the area under the macro-average receiver operating curve (AUC). Reliability was evaluated by Cohen's kappa to compare the consistency of model predicted labels with expert opinions.

RESULTS

PAINet performed best among the four algorithms. The accuracy, precision, recall, F1-score, and AUC on the test set were 0.9333, 0.9415, 0.9333, 0.9336, and 0.9972, respectively. Cohen's kappa was 0.911, which represented almost perfect consistency.

CONCLUSIONS

PAINet can accurately distinguish between normal periapical tissues, CBCTPAI 1-2, CBCTPAI 3-5, and young permanent teeth. Its results were highly consistent with expert opinions. It can help junior doctors diagnose and score AP, reducing the burden. It can also be promoted in areas where experts are lacking to provide professional diagnostic opinions.

Dedicated Breast CT: Getting Ready for Prime Time

Dedicated breast CT is an imaging modality that provides true 3D imaging of the breast with many advantages over current conventional breast imaging modalities. The addition of intravascular contrast increases the sensitivity of breast CT substantially. As such, there are immediate potential applications in the clinical workflow. These include using breast CT to replace much of the traditional diagnostic workup when faced with indeterminate breast lesions. Contrast-enhanced breast CT may be appropriate as a supplemental screening tool for women at high risk of breast cancer, similar to breast MRI. In addition, emerging studies are demonstrating the utility of breast CT in neoadjuvant chemotherapy tumor response monitoring as well as planning for surgical treatment options. While short exam times and fully 3D imaging in a noncompressed position are advantages of this modality, limited coverage of chest wall/axilla due to prone positioning and use of ionizing radiation are drawbacks. To date, several studies have reported on the performance characteristics of this promising modality.

Comparison of quantitative radiomorphometric predictors of healthy and MRONJ-affected bone using panoramic radiography and cone-beam CT

OBJECTIVES

To determine the most distinctive quantitative radiomorphometric parameter(s) for the detection of MRONJ-affected bone changes in panoramic radiography (PR) and cone-beam CT (CBCT).

METHODS

PR and sagittal CBCT slices of 24 MRONJ patients and 22 healthy controls were used for the measurements of mandibular cortical thickness (MCT), fractal dimension (FD), lacunarity, mean gray value (MGV), bone area fraction (BA/TA), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N). MCT was measured in the mental foramen region. While FD and lacunarity were measured on mandibular trabecular and cortical regions-of-interest (ROIs), the remaining parameters were measured on trabecular ROIs. The independent samples t-test was used to compare the measurements between the MRONJ and control groups for both imaging modalities (P = .05).

RESULTS

MCT was the only parameter that differentiated MRONJ-affected bone in both PR and CBCT (P < .05). None of the remaining parameters revealed any difference for MRONJ-affected bone in CBCT (P > .05). FD, lacunarity, MGV, BA/TA, and Tb.Sp could distinguish MRONJ-affected trabecular bone in PR (P < .05). The correspondent ROI for both imaging methods that was reliable for detecting MRONJ-affected bone was the trabecular bone distal to the mental foramen above the inferior alveolar canal (ROI-3).

CONCLUSIONS

MCT is a reliable parameter for the discrimination of MRONJ-affected bone in both PR and CBCT images. PR may be used to detect MRONJ-affected trabecular bone using FD, lacunarity, MGV, BA/TA, and Tb.Sp measurements as well.

Diagnostic Accuracy of Cone-Beam CT for Acute Intracranial Hemorrhage: A Systematic Review and Meta-Analysis

OBJECTIVE

Our purpose was to synthesize evidence in the literature to determine the diagnostic accuracy of cone-beam CT (CBCT) for the detection of intracranial hemorrhage (ICH) and hemorrhage types, including intraparenchymal hemorrhage (IPH), subarachnoid hemorrhage (SAH), and intraventricular hemorrhage (IVH).

METHODS

We performed a meta-analysis following the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. Our protocol was registered with International Prospective Register of Systematic Reviews (PROSPERO-CRD42021261915). Systematic searches were last performed on April 30, 2024, in EMBASE, PubMed, Web-of-Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature databases. Inclusion criteria were (1) studies reporting diagnostic metrics of CBCT for ICH and (2) studies using a reference standard to determine ICH. Exclusion criteria were (1) case reports, abstracts, reviews and (2) studies without patient-level data. Pooled estimates and 95% confidence intervals (CIs) were calculated for diagnostic odds ratios (DORs), sensitivity, and specificity using random-effects and common-effects models. Mixed methods appraisal tool was used to evaluate risk of bias.

RESULTS

Seven studies were included in the meta-analysis yielding 466 patients. Mean or median age ranged from 54 to 75 years. Female patients represented 51.4% (222 of 432) in reported studies. Multidetector CT was the reference standard in all studies. DOR, pooled sensitivity, and pooled specificity for ICH were 5.28 (95% CI: 4.11-6.46), 0.88 (95% CI: 0.79-0.97), and 0.99 (95% CI: 0.98-1.0). Pooled sensitivity for IPH, SAH, and IVH was 0.98 (95% CI: 0.95-1.0), 0.82 (95% CI: 0.57-1.0), and 0.78 (95% CI: 0.55-1.0). Pooled specificity for IPH, SAH, and IVH was 0.99 (95% CI: 0.98-1.0), 0.99 (95% CI: 0.97-1.0), and 1.0 (95% CI: 0.98-1.0).

DISCUSSION

CBCT had moderate DOR and high pooled specificity for ICH and hemorrhage types. However, pooled sensitivity varied by hemorrhage type, with the highest sensitivity for IPH, followed by SAH and IVH.

Cone-beam CT with a noncircular (sine-on-sphere) orbit: imaging performance of a clinical system for image-guided interventions

Purpose

We aim to compare the imaging performance of a cone-beam CT (CBCT) imaging system with noncircular scan protocols (sine-on-sphere) to a conventional circular orbit.

Approach

A biplane C-arm system (ARTIS Icono; Siemens Healthineers) capable of circular and noncircular CBCT acquisition was used, with the latter orbit (sine-on-sphere, "Sine Spin") executing a sinusoidal motion with ± 10    deg tilt amplitude over the half-scan orbit. A test phantom was used for the characterization of image uniformity, noise, noise-power spectrum (NPS), spatial resolution [modulation transfer function (MTF) in axial and oblique directions], and cone-beam artifacts. Findings were interpreted using an anthropomorphic head phantom with respect to pertinent tasks in skull base neurosurgery.

Results

The noncircular scan protocol exhibited several advantages associated with improved 3D sampling-evident in the NPS as filling of the null cone about thef z spatial frequency axis and reduction of cone-beam artifacts. The region of support at the longitudinal extrema was reduced from 16 to ∼ 12    cm at a radial distance of 6.5 cm. Circular and noncircular orbits exhibited nearly identical image uniformity and quantum noise, demonstrating cupping of - 16.7 % and overall noise of ∼ 27    HU . Although both the radially averaged axial MTF (f x , y ) and 45 deg oblique MTF (f x , y , z ) were ∼ 20 % lower for the noncircular orbit compared with the circular orbit at the default full reconstruction field of view (FOV), there was no difference in spatial resolution for the medium reconstruction FOV (smaller voxel size). Differences in the perceptual image quality for the anthropomorphic phantom reinforced the objective, quantitative findings, including reduced beam-hardening and cone-beam artifacts about structures of interest in the skull base.

Conclusions

Image quality differences between circular and noncircular CBCT orbits were quantitatively evaluated on a clinical system in the context of neurosurgery. The primary performance advantage for the noncircular orbit was the improved sampling and elimination of cone-beam artifacts.

Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT

OBJECTIVES

Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance.

METHODS

We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests. A total of 348 features were selected to train six ML models for differential diagnosis by a 5-fold cross-validation. We then compared the performance of ML-based diagnoses to those of radiologists.

RESULTS

Among the six ML models, XGBoost was effective in distinguishing AME and OKC in CBCT images, with its classification performance outperforming the other models. The mean precision, recall, accuracy, F1-score, and area under the curve (AUC) were 0.900, 0.807, 0.843, 0.841, and 0.872, respectively. Compared to the diagnostics by radiologists, ML-based radiomic diagnostics performed better.

CONCLUSIONS

Radiomic-based ML algorithms allow CBCT images of AME and OKC to be distinguished accurately, facilitating the preoperative differential diagnosis of AME and OKC.

ADVANCES IN KNOWLEDGE

ML and radiomic approaches with high-resolution CBCT images provide new insights into the differential diagnosis of AME and OKC.

Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

Objective.Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is highly desired for improved imaging efficiency, dose reduction, and better mechanical clearance. LA-CBCT reconstruction, however, suffers from severe under-sampling artifacts, making it a highly ill-posed inverse problem. Diffusion models can generate data/images by reversing a data-noising process through learned data distributions; and can be incorporated as a denoiser/regularizer in LA-CBCT reconstruction. In this study, we developed a diffusion model-based framework, prior frequency-guided diffusion model (PFGDM), for robust and structure-preserving LA-CBCT reconstruction.Approach.PFGDM uses a conditioned diffusion model as a regularizer for LA-CBCT reconstruction, and the condition is based on high-frequency information extracted from patient-specific prior CT scans which provides a strong anatomical prior for LA-CBCT reconstruction. Specifically, we developed two variants of PFGDM (PFGDM-A and PFGDM-B) with different conditioning schemes. PFGDM-A applies the high-frequency CT information condition until a pre-optimized iteration step, and drops it afterwards to enable both similar and differing CT/CBCT anatomies to be reconstructed. PFGDM-B, on the other hand, continuously applies the prior CT information condition in every reconstruction step, while with a decaying mechanism, to gradually phase out the reconstruction guidance from the prior CT scans. The two variants of PFGDM were tested and compared with current available LA-CBCT reconstruction solutions, via metrics including peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).Main results.PFGDM outperformed all traditional and diffusion model-based methods. The mean(s.d.) PSNR/SSIM were 27.97(3.10)/0.949(0.027), 26.63(2.79)/0.937(0.029), and 23.81(2.25)/0.896(0.036) for PFGDM-A, and 28.20(1.28)/0.954(0.011), 26.68(1.04)/0.941(0.014), and 23.72(1.19)/0.894(0.034) for PFGDM-B, based on 120°, 90°, and 30° orthogonal-view scan angles respectively. In contrast, the PSNR/SSIM was 19.61(2.47)/0.807(0.048) for 30° for DiffusionMBIR, a diffusion-based method without prior CT conditioning.Significance. PFGDM reconstructs high-quality LA-CBCTs under very-limited gantry angles, allowing faster and more flexible CBCT scans with dose reductions.

XTransCT: ultra-fast volumetric CT reconstruction using two orthogonal x-ray projections for image-guided radiation therapy via a transformer network

Objective.The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy.Approach.Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which require extensive x-ray projections and lead to high radiation doses and equipment constraints.Main results.The proposed XTransCT algorithm demonstrated superior performance in terms of image quality, structural accuracy, and generalizability across different datasets, including a hospital set of 50 patients, the large-scale public LIDC-IDRI dataset, and the LNDb dataset for cross-validation. Notably, the algorithm achieved an approximately 300% improvement in reconstruction speed, with a rate of 44 ms per 3D image reconstruction compared to former 3D convolution-based methods.Significance.The XTransCT architecture has the potential to impact clinical practice by providing high-quality CT images faster and with substantially reduced radiation exposure for patients. The model's generalizability suggests it has the potential applicable in various healthcare settings.

Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography

BACKGROUND

Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches.

PURPOSE

We investigate the feasibility of BME assessment using kV-switching DE CBCT with a comprehensive CBCT artifact correction framework and a two-stage projection- and image-domain three-material decomposition algorithm.

METHODS

DE CBCT projections of quantitative BME phantoms (water containers 100-165 mm in size with inserts presenting various degrees of edema) and an animal cadaver model of BME were acquired on a CBCT test bench emulating the standard wrist imaging configuration of a Multitom Rax twin robotic x-ray system. The slow kV-switching scan protocol involved a 60 kV low energy (LE) beam and a 120 kV high energy (HE) beam switched every 0.5° over a 200° angular span. The DE CBCT data preprocessing and artifact correction framework consisted of (i) projection interpolation onto matched LE and HE projections views, (ii) lag and glare deconvolutions, and (iii) efficient Monte Carlo (MC)-based scatter correction. Virtual non-calcium (VNCa) images for BME detection were then generated by projection-domain decomposition into an Aluminium (Al) and polyethylene basis set (to remove beam hardening) followed by three-material image-domain decomposition into water, Ca, and fat. Feasibility of BME detection was quantified in terms of VNCa image contrast and receiver operating characteristic (ROC) curves. Robustness to object size, position in the field of view (FOV) and beam collimation (varied 20-160 mm) was investigated.

RESULTS

The MC-based scatter correction delivered > 69% reduction of cupping artifacts for moderate to wide collimations (> 80 mm beam width), which was essential to achieve accurate DE material decomposition. In a forearm-sized object, a 20% increase in water concentration (edema) of a trabecular bone-mimicking mixture presented as ∼15 HU VNCa contrast using 80-160 mm beam collimations. The variability with respect to object position in the FOV was modest (< 15% coefficient of variation). The areas under the ROC curve were > 0.9. A femur-sized object presented a somewhat more challenging task, resulting in increased sensitivity to object positioning at 160 mm collimation. In animal cadaver specimens, areas of VNCa enhancement consistent with BME were observed in DE CBCT images in regions of MRI-confirmed edema.

CONCLUSION

Our results indicate that the proposed artifact correction and material decomposition pipeline can overcome the challenges of scatter and limited spectral separation to achieve relatively accurate and sensitive BME detection in DE CBCT. This study provides an important baseline for clinical translation of musculoskeletal DE CBCT to quantitative, point-of-care bone health assessment.

Chinese expert consensus on cone-beam CT-guided diagnosis, localization and treatment for pulmonary nodules

Cone-beam computed tomography (CBCT) system can provide real-time 3D images and fluoroscopy images of the region of interest during the operation. Some systems can even offer augmented fluoroscopy and puncture guidance. The use of CBCT for interventional pulmonary procedures has grown significantly in recent years, and numerous clinical studies have confirmed the technology's efficacy and safety in the diagnosis, localization, and treatment of pulmonary nodules. In order to optimize and standardize the technical specifications of CBCT and guide its application in clinical practice, the consensus statement has been organized and written in a collaborative effort by the Professional Committee on Interventional Pulmonology of China Association for Promotion of Health Science and Technology.

Angiography with cone-beam CT versus contrast-enhanced MRI for living donor transplant imaging: Is MRI enough?

The purpose of this study is to compare the subjective and objective quality and confidence between conventional angiography with cone-beam computed tomography (CBCT) and magnetic resonance imaging (MRI) for the preoperative evaluation of potential donors for living donor liver transplant. Seventeen patients undergoing preoperative donor evaluation for living donor liver transplantation that underwent angiography with CBCT and contrast-enhanced MRI for evaluation of hepatic vascular anatomy were included in the study. Four attending radiologists interpreted anonymized, randomized angiography with CBCT images and MRIs, rating the diagnostic quality and confidence of their interpretation (on a 3-point scale) for each element, as well as clinically relevant measurements. Overall, the readers rated the quality of angiography with CBCT to be higher than that of MRI (median [interquartile range] = 3 (2, 3) vs. 2 (1-3), p < 0.001) across all patients. Readers of angiography with CBCT had more confidence in their interpretations as an average of all elements evaluated than the MRI readers (3 (3) vs. 3 (2, 3), p < 0.001). When the same reader interpreted both MRI and CBCT, the right hepatic artery diameter (3.8 mm ± 0.72 mm vs. 4.5 mm ± 1.2 mm, p < 0.005) and proper hepatic artery diameter (4.43 mm ± 0.98 mm vs. 5.4 mm ± 1.05 mm, p < 0.003) were significantly different between MRI and CBCT. There was poor interrater reliability for determining segment IV arterial supply for both modalities (κ < 0.2). Angiography with CBCT provides higher subjective diagnostic quality and greater radiologist confidence than MRI. The difference in measurements between CBCT and MRI when the same reader reads both studies suggests CBCT adds additional information over MRI evaluation alone.

A study on the radiomic correlation between CBCT and pCT scans based on modified 3D-RUnet image segmentation

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.

Comparison of intraoperative cone-beam CT versus preoperative fan-beam CT for navigated spine surgery: a prospective randomized study

OBJECTIVE

This prospective randomized study aimed to investigate the accuracy, radiation exposure, and surgical workflow optimization of a novel intraoperative spinal navigation system using preoperative fan-beam (FB) CT versus the classic intraoperative cone-beam (CB) CT in patients undergoing minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF).

METHODS

In this two-arm, single-center, randomized study, the authors evaluated the safety and clinical outcomes of a novel navigation system for pedicle screw placement in spine surgery.

RESULTS

The accuracy of pedicle screw placement in the experimental group (FB group) was 94.38%, while it was 94.55% in the control group (CB group). Notably, the intraoperative radiation exposure to patients in the FB CT group (mean 0.361 ± 0.261 mSv) was significantly lower than that in the CB CT group (mean 6.526 ± 13.591 mSv) (p < 0.0001). Furthermore, the intraoperative preparation time for screw placement in the FB group (mean 10.6 ± 5.62 minutes) was significantly lower than that in the CB group (mean 17.6 ± 5.59 minutes) (p = 0.0004). No significant differences were observed for blood loss during surgery, total radiation exposure to surgeons, mean time for inserting a single pedicle screw, revision surgery rate, patients' reported outcomes, and length of postoperative hospital stay between the two groups. Significant differences were observed for intraoperative radiation exposure to patients and the preparation time for pedicle screw placement.

CONCLUSIONS

The preoperative FB CT-based intraoperative spinal navigation system demonstrated comparable accuracy and safety when compared with the intraoperative CB CT-based system. Moreover, the FB CT-based system had a shorter time for screw placement and reduced intraoperative radiation exposure to patients. These findings support the potential benefits of adopting this novel navigation system to enhance surgical precision and reduce radiation-related risks in MIS-TLIF procedures.

Paediatric CBCT protocols for image-guided radiotherapy; outcome of a survey across SIOP Europe affiliated countries and literature review

BACKGROUND

Implementation of daily cone-beam CT (CBCT) into clinical practice in paediatric image-guided radiotherapy (IGRT) lags behind compared to adults. Surveys report wide variation in practice for paediatric IGRT and technical information remains unreported. In this study we report on technical settings from applied paediatric CBCT protocols and review the literature for paediatric CBCT protocols.

METHODS

From September to October 2022, a survey was conducted among 246 SIOPE-affiliated centres across 35 countries. The survey consisted of 3 parts: 1) baseline information; technical CBCT exposure settings and patient set-up procedure for 2) brain/head, and 3) abdomen. Descriptive statistics was used to summarise current practice. The literature was reviewed systematically with two reviewers obtaining consensus RESULTS: The literature search revealed 22 papers concerning paediatric CBCT protocols. Seven papers focused on dose-optimisation. Responses from 50/246 centres in 25/35 countries were collected: 44/50 treated with photons and 10/50 with protons. In total, 48 brain/head and 53 abdominal protocols were reported. 42/50 centres used kV-CBCT for brain/head and 35/50 for abdomen; daily CBCT was used for brain/head = 28/48 (58%) and abdomen = 33/53 62%. Greater consistency was seen in brain/head protocols (dose range 0.32 - 67.7 mGy) compared to abdominal (dose range 0.27 - 119.7 mGy).

CONCLUSION

Although daily CBCT is now widely used in paediatric IGRT, our survey demonstrates a wide range of technical settings, suggesting an unmet need to optimise paediatric IGRT protocols. This is in accordance with the literature. However, there are only few paediatric optimisation studies suggesting that dose reduction is possible while maintaining image quality.

Volumetric feature points integration with bio-structure-informed guidance for deformable multi-modal CT image registration

Objective.Medical image registration represents a fundamental challenge in medical image processing. Specifically, CT-CBCT registration has significant implications in the context of image-guided radiation therapy (IGRT). However, traditional iterative methods often require considerable computational time. Deep learning based methods, especially when dealing with low contrast organs, are frequently entangled in local optimal solutions.Approach.To address these limitations, we introduce a registration method based on volumetric feature points integration with bio-structure-informed guidance. Surface point cloud is generated from segmentation labels during the training stage, with both the surface-registered point pairs and voxel feature point pairs co-guiding the training process, thereby achieving higher registration accuracy.Main results.Our findings have been validated on paired CT-CBCT datasets. In comparison with other deep learning registration methods, our approach has improved the precision by 6%, reaching a state-of-the-art status.Significance.The integration of voxel feature points and bio-structure feature points to guide the training of the medical image registration network has achieved promising results. This provides a meaningful direction for further research in medical image registration and IGRT.

Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network

BACKGROUND

The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-dependent as well as system-specific.

PURPOSE

In this study, we propose a dual-domain network for reducing the bowtie-filter-induced artifacts in CBCT images.

METHODS

In the projection domain, the network compensates for the filter-induced beam-hardening that are highly related to the eclipse artifacts. The output of the projection-domain network was used for image reconstruction and the reconstructed images were fed into the image-domain network. In the image domain, the network further reduces the remaining cupping artifacts that are associated with the scatter. A single image-domain-only network was also implemented for comparison.

RESULTS

The proposed approach successfully enhanced soft-tissue contrast with much-reduced image artifacts. In the numerical study, the proposed method decreased perceptual loss and root-mean-square-error (RMSE) of the images by 84.5% and 84.9%, respectively, and increased the structure similarity index measure (SSIM) by 0.26 compared to the original input images on average. In the experimental study, the proposed method decreased perceptual loss and RMSE of the images by 87.2% and 92.1%, respectively, and increased SSIM by 0.58 compared to the original input images on average.

CONCLUSIONS

We have proposed a deep-learning-based dual-domain framework to reduce the bowtie-filter artifacts and to increase the soft-tissue contrast in CBCT images. The performance of the proposed method has been successfully demonstrated in both numerical and experimental studies.

Setup Uncertainty of Pediatric Brain Tumor Patients Receiving Proton Therapy: A Prospective Study

This study quantifies setup uncertainty in brain tumor patients who received image-guided proton therapy. Patients analyzed include 165 children, adolescents, and young adults (median age at radiotherapy: 9 years (range: 10 months to 24 years); 80 anesthetized and 85 awake) enrolled in a single-institution prospective study from 2020 to 2023. Cone-beam computed tomography (CBCT) was performed daily to calculate and correct manual setup errors, once per course after setup correction to measure residual errors, and weekly after treatments to assess intrafractional motion. Orthogonal radiographs were acquired consecutively with CBCT for paired comparisons of 40 patients. Translational and rotational errors were converted from 6 degrees of freedom to a scalar by a statistical approach that considers the distance from the target to the isocenter. The 95th percentile of setup uncertainty was reduced by daily CBCT from 10 mm (manual positioning) to 1-1.5 mm (after correction) and increased to 2 mm by the end of fractional treatment. A larger variation existed between the roll corrections reported by radiographs vs. CBCT than for pitch and yaw, while there was no statistically significant difference in translational variation. A quantile mixed regression model showed that the 95th percentile of intrafractional motion was 0.40 mm lower for anesthetized patients (p=0.0016). Considering additional uncertainty in radiation-imaging isocentricity, the commonly used total plan robustness of 3 mm against positional uncertainty would be appropriate for our study cohort.

PDS-MAR: a fine-grained projection-domain segmentation-based metal artifact reduction method for intraoperative CBCT images with guidewires

Objective.Since the invention of modern Computed Tomography (CT) systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and limited-angle projection data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used.Approach.To solve this problem, we present a fine-grained projection-domain segmentation-based metal artifact reduction (MAR) method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain.Main results.The proposed method is tested on both digital phantom data and real scanned cone-beam computed tomography (CBCT) data. It achieves much-improved quantitative results in both metal segmentation and artifact reduction in our phantom study. The results on real scanned data also show the superiority of this method.Significance.The concept of projection-domain metal segmentation would advance MAR techniques in CBCT and has the potential to push forward the use of intraoperative CBCT in human-handed and robotic-assisted minimal invasive spine surgeries.

Self-supervised denoising of projection data for low-dose cone-beam CT

BACKGROUND

Convolutional neural networks (CNNs) have shown promising results in image denoising tasks. While most existing CNN-based methods depend on supervised learning by directly mapping noisy inputs to clean targets, high-quality references are often unavailable for interventional radiology such as cone-beam computed tomography (CBCT).

PURPOSE

In this paper, we propose a novel self-supervised learning method that reduces noise in projections acquired by ordinary CBCT scans.

METHODS

With a network that partially blinds input, we are able to train the denoising model by mapping the partially blinded projections to the original projections. Additionally, we incorporate noise-to-noise learning into the self-supervised learning by mapping the adjacent projections to the original projections. With standard image reconstruction methods such as FDK-type algorithms, we can reconstruct high-quality CBCT images from the projections denoised by our projection-domain denoising method.

RESULTS

In the head phantom study, we measure peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values of the proposed method along with the other denoising methods and uncorrected low-dose CBCT data for a quantitative comparison both in projection and image domains. The PSNR and SSIM values of our self-supervised denoising approach are 27.08 and 0.839, whereas those of uncorrected CBCT images are 15.68 and 0.103, respectively. In the retrospective study, we assess the quality of interventional patient CBCT images to evaluate the projection-domain and image-domain denoising methods. Both qualitative and quantitative results indicate that our approach can effectively produce high-quality CBCT images with low-dose projections in the absence of duplicate clean or noisy references.

CONCLUSIONS

Our self-supervised learning strategy is capable of restoring anatomical information while efficiently removing noise in CBCT projection data.

Radiation doses and accuracy of navigated pedicle screw placement in cervical and thoracic spine surgery: a comparison of sliding gantry CT and mobile cone-beam CT in a homogeneous cohort

OBJECTIVE

Multiple solutions for navigation-guided pedicle screw placement are currently available. Intraoperative imaging techniques are invaluable for spinal surgery, but often there is little attention paid to patient radiation exposure. This study aimed to compare the applied radiation doses of sliding gantry CT (SGCT)- and mobile cone-beam CT (CBCT)-based pedicle screw placement for spinal instrumentation.

METHODS

The authors retrospectively analyzed 183 and 54 patients who underwent SGCT- or standard CBCT-based pedicle screw placement, respectively, for spinal instrumentation at their department between June 2019 and January 2020. SGCT uses an automated radiation dose adjustment.

RESULTS

Baseline characteristics, including the number of screws per patient and the number of instrumented levels, did not significantly differ between the two groups. Although the accuracy of screw placement according to Gertzbein-Robbins classification did not differ between the two groups, more screws had to be revised intraoperatively in the CBCT group (SGCT 2.7% vs CBCT 6.0%, p = 0.0036). Mean (± SD) radiation doses for the first (SGCT 484.0 ± 201.1 vs CBCT 687.4 ± 188.5 mGy*cm, p < 0.0001), second (SGCT 515.8 ± 216.3 vs CBCT 658.3 ± 220.1 mGy*cm, p < 0.0001), third (SGCT 531.3 ± 237.5 vs CBCT 641.6 ± 177.3 mGy*cm, p = 0.0140), and total (SGCT 1216.9 ± 699.3 vs CBCT 2000.3 ± 921.0 mGy*cm, p < 0.0001) scans were significantly lower for SGCT. This was also true for radiation doses per scanned level (SGCT 461.9 ± 429.3 vs CBCT 1004.1 ± 905.1 mGy*cm, p < 0.0001) and radiation doses per screw (SGCT 172.6 ± 110.1 vs CBCT 349.6 ± 273.4 mGy*cm, p < 0.0001).

CONCLUSIONS

The applied radiation doses were significantly lower using SGCT for navigated pedicle screw placement in spinal instrumentation. A modern CT scanner on a sliding gantry leads to lower radiation doses, especially through automated 3D radiation dose adjustment.

Three-dimensional carpal alignment: computer-aided CT analysis of carpal axes and normal ranges

Assessment of carpal alignment traditionally uses carpal bone axes measured on lateral radiographs. In this study, three-dimensional axes were defined for carpal bones using segmentation and numerical modelling of CT data of 121 neutrally positioned, asymptomatic wrists. The geometric axis was used for radius, scaphoid and capitate, whereas the axis based on a line perpendicular to the articular surface was used for the other carpal bones. Normal values of radiocarpal angles in the radial coordinate and the reliability of the computer-aided analysis are reported. The mean sagittal radiocarpal angles (positive in palmar direction) were as follows: scaphoid 58° (SD 10°), lunate 0° (SD 11°), triquetrum 12° (SD 8°), trapezium 17° (SD 8°), trapezoid -10° (SD 7°), capitate -17° (SD 9°) and hamate 2° (SD 7°). The mean coronal radiocarpal angles (positive in ulnar direction) were -42° (SD 9°), -20° (SD 4°), -49° (SD 4°), -32° (SD 6°), -16° (SD 5°), 2° (SD 7°) and 8° (SD 6°), respectively. The intra-observer reliability of the measurements was excellent (mean intraclass correlations coefficient 0.98). This study provides guidelines on how to measure and quantify carpal alignment three-dimensionally, and a database for the normal values. Together, these may be useful when analysing various wrist pathologies and kinematics of the wrist.

CBCT-Based Dose Monitoring and Adaptive Planning Triggers in Head and Neck PBS Proton Therapy

PURPOSE

To investigate the feasibility of using cone-beam computed tomography (CBCT)-derived synthetic CTs to monitor the daily dose and trigger a plan review for adaptive proton therapy (APT) in head and neck cancer (HNC) patients.

METHODS

For 84 HNC patients treated with proton pencil-beam scanning (PBS), same-day CBCT and verification CT (vfCT) pairs were retrospectively collected. The ground truth CT (gtCT) was created by deforming the vfCT to the same-day CBCT, and it was then used as a dosimetric baseline and for establishing plan review trigger recommendations. Two different synthetic CT algorithms were tested; the corrected CBCT (corrCBCT) was created using an iterative image correction method and the virtual CT (virtCT) was created by deforming the planning CT to the CBCT, followed by a low-density masking process. Clinical treatment plans were recalculated on the image sets for evaluation.

RESULTS

Plan review trigger criteria for adaptive therapy were established after closely reviewing the cohort data. Compared to the vfCT, the corrCBCT and virtCT reliably produced dosimetric data more similar to the gtCT. The average discrepancy in D99 for high-risk clinical target volumes (CTV) was 1.1%, 0.7%, and 0.4% and for standard-risk CTVs was 1.8%, 0.5%, and 0.5% for the vfCT, corrCBCT, and virtCT, respectively.

CONCLUSION

Streamlined APT has been achieved with the proposed plan review criteria and CBCT-based synthetic CT workflow.