Patient CT Data Research Articles

Proton dose deposition matrix prediction using multi-source feature driven deep learning approach

Proton dose deposition results are influenced by various factors, such as irradiation angle, beamlet energy and other parameters. The calculation of the proton dose deposition matrix (DDM) can be highly complex but is crucial in intensity-modulated proton therapy (IMPT). In this work, we present a novel deep learning (DL) approach using multi-source features for proton DDM prediction. The DL5 proton DDM prediction method involves five input features containing beamlet geometry, dosimetry and treatment machine information like patient CT data, beamlet energy, distance from voxel to beamlet axis, distance from voxel to body surface, and pencil beam (PB) dose. The dose calculated by Monte Carlo (MC) method was used as the ground truth dose label. A total of 40 000 features, corresponding to 8000 beamlets, were obtained from head patient datasets and used for the training data. Additionally, seventeen head patients not included in the training process were utilized as testing cases. The DL5 method demonstrates high proton beamlet dose prediction accuracy, with an average determination coefficient R 2 of 0.93 when compared to the MC dose. Accurate beamlet dose estimation can be achieved in as little as 1.5 milliseconds for an individual proton beamlet. For IMPT plan dose comparisons to the dose calculated by the MC method, the DL5 method exhibited gamma pass rates of γ(2 mm, 2%) and γ(3 mm, 3%) ranging from 98.15% to 99.89% and 98.80% to 99.98%, respectively, across all 17 testing cases. On average, the DL5 method increased the gamma pass rates to γ(2 mm, 2%) from 82.97% to 99.23% and to γ(3 mm, 3%) from 85.27% to 99.75% when compared with the PB method. The proposed DL5 model enables rapid and precise dose calculation in IMPT plan, which has the potential to significantly enhance the efficiency and quality of proton radiation therapy.

Finite element models with automatic computed tomography bone segmentation for failure load computation

Bone segmentation is an important step to perform biomechanical failure load simulations on in-vivo CT data of patients with bone metastasis, as it is a mandatory operation to obtain meshes needed for numerical simulations. Segmentation can be a tedious and time consuming task when done manually, and expert segmentations are subject to intra- and inter-operator variability. Deep learning methods are increasingly employed to automatically carry out image segmentation tasks. These networks usually need to be trained on a large image dataset along with the manual segmentations to maximize generalization to new images, but it is not always possible to have access to a multitude of CT-scans with the associated ground truth. It then becomes necessary to use training techniques to make the best use of the limited available data. In this paper, we propose a dedicated pipeline of preprocessing, deep learning based segmentation method and post-processing for in-vivo human femurs and vertebrae segmentation from CT-scans volumes. We experimented with three U-Net architectures and showed that out-of-the-box models enable automatic and high-quality volume segmentation if carefully trained. We compared the failure load simulation results obtained on femurs and vertebrae using either automatic or manual segmentations and studied the sensitivity of the simulations on small variations of the automatic segmentation. The failure loads obtained using automatic segmentations were comparable to those obtained using manual expert segmentations for all the femurs and vertebrae tested, demonstrating the effectiveness of the automated segmentation approach for failure load simulations.

Predicting radiation-induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy.

Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. Radiation-induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT-induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections. RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT. This Python-based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10-fold cross validation. Our algorithm predicted post-treatment ALC with an average error of cells/L with 89% of the patients having a prediction error below 0.5×109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post-treatment ALC<0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38-s end-to-end prediction time of post treatment ALC. Our model performed well in predicting RIIS in patients treated using lung SBRT. With near-real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.

Typical values statistical analysis for adult chest and abdomen-pelvis CT examinations

The use of DRLs has been extensively documented in the literature as a tool for protocol optimization across different x-ray imaging modalities in different countries. It recognizes the importance of developing and validating methods capable of correlating DRL quantities (CTDIvol, DLP and SSDE) with the technical parameters employed in CT studies. Such correlations must be supported by robust statistical methodologies in order to ensure the adoption of adequate optimization decisions. The aim of this work was to apply the Generalized Additive Model (GAM) statistical analysis in adult non-contrast chest and abdomen-pelvis CT typical values. These patient cohorts were statistically evaluated to identify correlations with key-parameters associated to the demographic patient information and machine dependent data, taking into account patients’ effective diameters, d, and body mass indexes (BMI). GAM was implemented considering each anatomical region in order to correlate the log-transformed DRL quantities (DRLq's) as outcomes given different key predictors related to image acquisition and patient characteristics. A total of 956 CT patient data were collected in this retrospective single-center study. Demographic variables demonstrate that age is not or it is just weakly-correlated to the DRLq's resulting from chest procedures, but it is strongly correlated when considering abdomen-pelvis examinations. Gender is correlated to the DRLq's for chest examinations adopting d as a key predictor but it is only correlated with DLP adopting the BMI as a key predictor. The level of accuracy provided by the GAM was adequate for interpreting the large fluctuations of the DRLq's, technical parameters and demographic data observed for the studied patient cohorts. Our results reflect the importance of a comprehensive statistical evaluation of typical values. The domain of this technique is important to different CT imaging chain stakeholders and its application can be a key tool for decision-making process to effective optimization strategies.

Three-dimensional measurements of symmetry for the mandibular ramus

BackgroundThe study examines a sample of patients presenting for viscerocranial computer tomography that does not display any apparent signs of asymmetry, assesses the three-dimensional congruency of the mandibular ramus, and focuses on differences in age and gender. MethodsThis cross-sectional cohort study screened viscerocranial CT data of patients without deformation or developmental anomalies. Segmentations were obtained from the left and right sides and superimposed according to the best-fit alignment. Comparisons were made to evaluate three-dimensional congruency and compared between subgroups according to age and gender. ResultsTwo hundred and sixty-eight patients were screened, and one hundred patients met the inclusion criteria. There were no statistical differences between the left and right sides of the mandibular ramus. Also, there were no differences between the subgroups. The overall root mean square was 0.75 ± 0.15 mm, and the mean absolute distance from the mean was 0.54 ± 0.10 mm. ConclusionThe mean difference was less than one millimetre, far below the two-millimetre distance described in the literature that defines relative symmetry. Our study population displays a high degree of three-dimensional congruency.Our findings help to understand that there is sufficient three-dimensional congruency of the mandibular ramus, thus contributing to facilitating CAD-CAM-based procedures based on symmetry for this specific anatomic structure.

Decompressive Bone Flap Replacement (Decompressive Cranioplasty): A Novel Technique for Intracranial Hypertension-Initial Experience and Outcome.

Intracranial hypertension is a life-threatening condition that can be treated by decompressive craniectomy (DC), which involves removing a part of the skull and intracranial lesions. However, DC has many complications and requires a second surgery to repair the skull. Decompressive bone flap replacement (DBFR) or decompressive cranioplasty is a novel technique that replaces the bone flap with a titanium mesh, providing both decompression and skull integrity. The materials and methods of DBFR are described in detail. A three-dimensional titanium mesh is fabricated based on the computed tomography (CT) data of previous DC patients. An appropriate titanium mesh is selected based on the preoperative and intraoperative assessments. After removing the intracranial lesions, the titanium mesh is fixed over the bone window. We successfully performed DBFR in three emergent cases. The postoperative CT scan showed adequate decompression in all cases. No reoperation for skull repair was needed, and there were no surgical complications. The cosmetic outcome was excellent. There were no relevant complications in the operative area. DBFR may be a safe and effective alternative to DC in a specific subgroup of patients, in whom complete removal of the bone flap is feasible. DBFR can reduce intracranial pressure while maintaining the integrity of the skull cavity, eliminating the need for additional surgery for skull repair. Possible improvements for DBFR in the future are suggested, such as using a greater curvature of the titanium mesh or a modified DBFR with a hinged titanium mesh.

Assessment of pulmonary fibrosis severity at autopsy in patients after COVID-19: comparison with quantitative CT scan data in the acute phase of the disease

INTRODUCTION: After the COVID-19 pandemic, there is increasing evidence that many patients show fibrous changes in lung tissue accompanied by functional lung disorders. Objective data on the histopathogenesis of such changes is still insufficient. Prospective studies are required to fully assess the consequences of these clinical manifestations.OBJECTIVE: Evaluation of the capabilities of digital processing of histological preparations of lung tissue and their comparison with quantitative CT data of lung patients in the acute phase of COVID-19.MATERIALS AND METHODS: The study included data from patients after COVID-19 (7 women and 3 men aged 47 to 93 years) who died after the acute phase of COVID-19 from extrapulmonary causes. The control group included data from 7 people (5 women and 2 men aged 35 to 93 years) who died shortly after hospitalization from extrapulmonary causes (myocardial infarction or acute cerebral stroke), with no signs of lung diseases, including autopsy results. Digital processing of histological preparations of lung tissue obtained during autopsy was carried out, and their comparison with the results of quantitative semi-automatic processing of CT data.Statistics. Beta regression (mgcv library) was used. The model was characterized by a pseudodetermination coefficient R2. The association was considered statistically significant at p&lt;0.05.RESULTS: A reliable dependence of the severity of fibrous changes in histological samples on the results of quantitative analysis of CT images of patients in the acute period of COVID-19 was demonstrated.DISCUSSION: Extrapolation of lung autopsy data through quantitative CT assessment is one of the ways to pre-diagnose and identify groups of patients who require specific treatment of post-COVID-19 pulmonary fibrosis.CONCLUSION. Computerized digital processing of histological images made it possible to correctly compare the histopathological examination data with the CT picture in COVID-19, which could potentially have a prognostic value in the search for more effective treatment strategies.

Scoring system for unstable spinal gunshot wounds: the study protocol

To present a research protocol for development of new scoring system for unstable spinal gunshot wounds based on inter-expert agreement assessment. To create a new tactical classification, we will distinguish and analyze clinical and CT data of patients with thoracolumbar spinal gunshot wounds. The Delphi method will be used to collaborate between several surgeons. A three-stage study will result a questionnaire (for 30 clinical cases). We will develop tactical scoring system and analyze statistical data (kappa). Various classifications have been developed for closed spinal injuries. These systems describe the nature of injury and allow one to develop tactical decisions for further actions. Another mechanism of injuries following gunshot wounds does not allow the classification of closed injuries to be adequately applied in some cases. Indeed, spinal structures follow either direct passage of a wounding projectile through the spine or transferring the energy of this projectile in contrast to classical compression, distraction and rotational-translation mechanisms typical for closed trauma.

Design of 3D-printed prostheses for reconstruction of periacetabular bone tumors using topology optimization.

Background: Prostheses for the reconstruction of periacetabular bone tumors are prone to instigate stress shielding. The purpose of this study is to design 3D-printed prostheses with topology optimization (TO) for the reconstruction of periacetabular bone tumors and to add porous structures to reduce stress shielding and facilitate integration between prostheses and host bone. Methods: Utilizing patient CT data, we constructed a finite element analysis (FEA) model. Subsequent phases encompassed carrying out TO on the designated area, utilizing the solid isotropic material penalization model (SIMP), and this optimized removal area was replaced with a porous structure. Further analyses included preoperative FEA simulations to comparatively evaluate parameters, including maximum stress, stress distribution, strain energy density (SED), and the relative micromotion of prostheses before and after TO. Furthermore, FEA based on patients' postoperative CT data was conducted again to assess the potential risk of stress shielding subsequent to implantation. Ultimately, preliminary follow-up findings from two patients were documented. Results: In both prostheses, the SED before and after TO increased by 143.61% (from 0.10322 to 0.25145mJ/mm3) and 35.050% (from 0.30964 to 0.41817mJ/mm3) respectively, showing significant differences (p < 0.001). The peak stress in the Type II prosthesis decreased by 10.494% (from 77.227 to 69.123MPa), while there was no significant change in peak stress for the Type I prosthesis. There were no significant changes in stress distribution or the proportion of regions with micromotion less than 28μm before and after TO for either prosthesis. Postoperative FEA verified results showed that the stress in the pelvis and prostheses remained at relatively low levels. The results of follow-up showed that the patients had successful osseointegration and their MSTS scores at the 12thmonth after surgery were both 100%. Conclusion: These two types of 3D-printed porous prostheses using TO for periacetabular bone tumor reconstruction offer advantages over traditional prostheses by reducing stress shielding and promoting osseointegration, while maintaining the original stiffness of the prosthesis. Furthermore, in vivo experiments show that these prostheses meet the requirements for daily activities of patients. This study provides a valuable reference for the design of future periacetabular bone tumor reconstruction prostheses.

Analysis of Degenerative and Isthmic Lumbar Spondylolisthesis from the Difference of Pelvic Parameters and the Degree of Degeneration through Imaging Data.

In previous studies, many imaging analyses have been conducted to explore the changes in the intervertebral disc degeneration (DD), facet joint osteoarthritis (FJOA), L4 inclination angle (L4IA), pelvis-related parameters, lumbar lordosis (LL), and paravertebral muscle (PVM) in the occurrence and development of degenerative spinal diseases via measuring the X-ray, CT, and MRI data of clinical patients. However, few studies have quantitatively investigated the pelvic parameters and the degree of spine degeneration in patients with degenerative lumbar spondylolisthesis (DLS) and isthmic lumbar spondylolisthesis (ILS). This study discusses the changes in the imaging parameters of DLS, ILS, and a control group; explores the correlation between different measurement parameters; and discusses their risk factors. We evaluated 164 patients with single L4-L5 grade 1 level degenerative lumbar spondylolisthesis (DLS group), 161 patients with single L4-L5 grade 1 level isthmic lumbar spondylolisthesis (ILS group), and 164 patients with non-specific back pain (control group). The grades of DD and FJOA as well as the percentage of the fat infiltration area (%FIA) of multifidus muscle (MM) at the L4-L5 level were measured via CT and MRI. Lumbar lordosis (LL), pelvic incidence (PI), pelvic tilt (PT), the L4 inclination angle (L4IA), and sacral slope (SS) were measured via X-ray film, and the differences among the DLS group, ILS group, and control group were analyzed. Furthermore, the risk factors related to the incidences of the DLS and ILS groups were discussed. First, the pelvis-related parameters of DLS and ILS patients were 51.91 ± 12.23 and 53.28 ± 11.12, respectively, while those of the control group were 40.13 ± 8.72 (p1 < 0.001, p2 < 0.001). Lumbar lordosis (LL) in DLS patients (39.34 ± 8.57) was significantly lower than in the control group (44.40 ± 11.79, p < 0.001). On the contrary, lumbar lordosis (LL) in the ILS group (55.16 ± 12.31) was significantly higher than in the control group (44.40 ± 11.79, p < 0.001). Secondly, the three groups of patients were characterized by significant variations in the L4 inclination angle (L4IA), disc degeneration (DD), facet joint osteoarthritis (FJOA), pelvis-related parameters, and paravertebral muscle (PVM) (p < 0.05). Finally, logistic regression suggests that the L4IA, FJOA, and PT may be risk factors for the occurrence of DLS, and the occurrence of ILS is correlated with the L4IA, FJOA, DD, PT, and LL. Compared with the control group, there are changes in pelvic parameters, the L4IA, LL, DD, FJOA, and PVM in DLS and ILS patients, and the degree is different. The parameters within the same group are related to each other, and DLS and ILS have different risk factors. The mechanical stability of the spine is affected by the parameter and angle changes, which may be of great significance for explaining the cause of spondylolisthesis, evaluating the health of the lumbar spine, and guiding the lifestyles of patients.

Sensitivity and specificity of secondary dose calculation for head and neck treatment plans.

Secondary dose calculation (SDC) with an independent algorithm is one option to perform plan-specific quality assurance (QA). While measurement-based QA can potentially detect errors in plan delivery, the dose values are typically only compared to calculations on homogeneous phantom geometries instead of patient CT data. We analyzed the sensitivity and specificity of an SDC software by purposely introducing different errors and determined thresholds for optimal decisions. Thirty head and neck VMAT plans and 30 modifications of those plans, including errors related to density and beam modelling, were recalculated using RadCalc with a Monte Carlo algorithm. Decision thresholds were obtained by receiver operating characteristics (ROC) analysis. For comparison, measurement-based QA using the ArcCHECK phantom was carried out and evaluated in the same way. Despite optimized decision thresholds, none of the systems was able to reliably detect all errors. ArcCHECK analysis using a 2%/2mm criterion with a threshold of 91.1% had an area under the curve (AUC) of 0.80. Evaluating differences in recalculated and planned DVH parameter of the target structures in RadCalc with a 2% threshold an AUC of 0.86 was achieved. Out-of-field deviations could be attributed to weaknesses in the beam model. Secondary dose calculation with RadCalc is an alternative to established measurement-based phantom QA. Different tools catch different errors; therefore, a combination of approaches should be preferred.

Method for CAD/CAM-based production of patient-specific anatomical bone surrogates from polyurethane rigid foams of different densities for various clinical applications

Abstract Physical models of bony areas are used in the medical environment for training, patient information or for various examinations. These are idealized average models which, however, do not reflect the individuality of a respective patient. Furthermore, the mechanical properties of the bony structure with cortical bone and cancellous bone are not considered in most models. A few manufacturers, such as SawBone, offer models and material blocks that approximate the properties of the bone. However, these are not patientspecific either. This paper presents a proposal for a method to create patient-specific physical bone models with CAD/CAM technologies that approximate shape and mechanical properties. The models are made of two different rigid polyurethane foams for cortical bone and cancellous bone. The methodical development includes the elaboration of a framework starting from the patient's CT data, through design derivation, to fabrication. Furthermore, a possible process for fabrication in the form of a demonstrator and its application in sawing tests will be demonstrated.

Simulation of consequences of using nonideal detectors during beam data commissioning measurements.

Beam data commissioning is a core task of radiotherapy physicists. Despite multiple detectors available, a feasible measurement program compromises between detector properties and time constraints. Therefore, it is important to understand how nonideal measurement data propagates into patient dose calculation. We simulated the effects of realistic errors, due to beam commissioning with presumably nonoptimal detectors, on the resulting patient dose distributions. Additionally, the detectability of such beam commissioning errors during patient plan quality assurance (QA) was evaluated. A clinically used beam model was re-commissioned introducing changes to depth dose curves, output factors, profiles or combinations of those. Seventeen altered beam models with incremental changes of the modelling parameters were created to analyze dose changes on simplified anatomical phantoms. Additionally, fourteen altered models incorporate changes in the order of signal differences reported for typically used detectors. Eighteen treatment plans of different types were recalculated on patient CT data sets using the altered beam models. For the majority of clinical plans, dose distributions in the target volume recalculated on the patient computed tomography data were similar between the original and the modified beam models, yielding global 2%/2mm gamma pass rates above 98.9%. Larger changes were observed for certain combinations of beam modelling errors and anatomical sites, most extreme for output factor changes in a small target volume plan with a pass rate of 80.6%. Modelling an enlarged penumbra as if measured with a 0.125cm3 ion chamber had the largest effect on the dose distribution (average pass rate of 96.5%, lowest 85.4%). On different QA phantom geometries, dose distributions between calculations with modified and unmodified models typically changed too little to be detected in actual measurements. While the simulated errors during beam modelling had little effect on most plans, in some cases changes were considerable. High-quality penumbra and small field output factor should be a main focus of commissioning measurements. Detecting modelling issues using standard patient QA phantoms is unlikely. Verification of a beam model should be performed especially for plans with high modulation and in different depths or geometries representing the variety of situations expected clinically.

Proposal of Modified Lung-RADS in Assessing Pulmonary Nodules of Patients with Previous Malignancies: A Primary Study

Background: In addition to the diameters of pulmonary nodules, the number and morphology of blood vessels in pure ground-glass nodules (pGGNs) were closely related to the occurrence of lung cancer. Moreover, the benign and malignant signs of nodules were also valuable for the identification of nodules. Based on these two points, we tried to revise Lung-RADS 2022 and proposed our Modified Lung-RADS. The aim of the study was to verify the diagnostic performance of Modified Lung-RADS for pulmonary solid nodules (SNs) and pure ground-glass nodules (pGGNs) in patients with previous malignancies. Methods: The chest CT and clinical data of patients with prior cancer who underwent pulmonary nodulectomies from 1 January 2018 to 30 November 2021 were enrolled according to inclusion and exclusion criteria. A total of 240 patients with 293 pulmonary nodules were included in this study. In contrast with the original version, the risk classification of pGGNs based on the GGN–vascular relationships (GVRs), and the SNs without burrs and with benign signs, could be downgraded to category 2. The sensitivity, specificity, and agreement rate of the original Lung-RADS 2022 and Modified Lung-RADS for pGGNs and SNs were calculated and compared. Results: Compared with the original version, the sensitivity and agreement rate of the Modified version for pGGNs increased from 0 and 23.33% to 97.10% and 92.22%, respectively, while the specificity decreased from 100% to 76.19%. As regards SNs, the specificity and agreement rate of the Modified version increased from 44.44% to 75.00% (p &lt; 0.05) and 88.67% to 94.09% (p = 0.052), respectively, while the sensitivity was unchanged (98.20%). Conclusions: In general, the diagnostic efficiency of Modified Lung-RADS was superior to that of the original version, and Modified Lung-RADS could be a preliminary attempt to improve Lung-RADS 2022.

Metal artifact reduction and clinical verification in oral and maxillofacial region based on deep learning

Objective: To construct a kind of neural network for eliminating the metal artifacts in CT images by training the generative adversarial networks (GAN) model, so as to provide reference for clinical practice. Methods: The CT data of patients treated in the Department of Radiology, West China Hospital of Stomatology, Sichuan University from January 2017 to June 2022 were collected. A total of 1 000 cases of artifact-free CT data and 620 cases of metal artifact CT data were obtained, including 5 types of metal restorative materials, namely, fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies. Four hundred metal artifact CT data and 1 000 artifact-free CT data were utilized for simulation synthesis, and 1 000 pairs of simulated artifacts and metal images and simulated metal images (200 pairs of each type) were constructed. Under the condition that the data of the five metal artifacts were equal, the entire data set was randomly (computer random) divided into a training set (800 pairs) and a test set (200 pairs). The former was used to train the GAN model, and the latter was used to evaluate the performance of the GAN model. The test set was evaluated quantitatively and the quantitative indexes were root-mean-square error (RMSE) and structural similarity index measure (SSIM). The trained GAN model was employed to eliminate the metal artifacts from the CT data of the remaining 220 clinical cases of metal artifact CT data, and the elimination results were evaluated by two senior attending doctors using the modified LiKert scale. Results: The RMSE values for artifact elimination of fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies in test set were 0.018±0.004, 0.023±0.007, 0.015±0.003, 0.019±0.004, 0.024±0.008, respectively (F=1.29, P=0.274). The SSIM values were 0.963±0.023, 0.961±0.023, 0.965±0.013, 0.958±0.022, 0.957±0.026, respectively (F=2.22, P=0.069). The intra-group correlation coefficient of 2 evaluators was 0.972. For 220 clinical cases, the overall score of the modified LiKert scale was (3.73±1.13), indicating a satisfactory performance. The scores of modified LiKert scale for fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies were (3.68±1.13), (3.67±1.16), (3.97±1.03), (3.83±1.14), (3.33±1.12), respectively (F=1.44, P=0.145). Conclusions: The metal artifact reduction GAN model constructed in this study can effectively remove the interference of metal artifacts and improve the image quality.

Three-dimensional computed tomographic angular measurements of the canine tibia using a bone-centered coordinate system.

Canine tibial alignment is determined by two-dimensional angular measurements, and tibial torsion is challenging. Aim of the study was the development and evaluation of a CT technique to measure canine tibial varus and torsion angles independent from positioning and truly three-dimensional. A bone-centered 3D cartesian coordinate system was introduced into the CT-scans of canine tibiae and aligned with the anatomical planes of the bone based on osseous reference points. Tibial torsion, and varus (or valgus) angles were calculated based on geometric definition of projection planes with VoXim® medical imaging software using 3D coordinates of the reference points. To test accuracy of the tibial torsion angle measurements, CT scans of a tibial torsion model were performed in 12 different hinge rotation setups ranging from the normal anatomical situation up to +/ 90° and compared to goniometer measurements. Independency of tibial positioning on the CT scanner table was evaluated in 20 normal canine tibiae that were scanned in a position parallel to the z-axis and two additional off-angle double oblique positions having 15° and 45° deviation in direction of the x- and y-axes. Angular measurements in oblique positions were compared with the normal parallel position by subtraction. Precision was tested using clinical CT scans of 34 canine patients with a clinical diagnosis of patellar luxation. Accuracy testing in the tibial torsional deformity model revealed a difference of 0.2° demonstrated by Passing-Bablok analysis and Bland-Altman-Plots. Testing for independency from tibial positioning resulted in mean differences less than 1.3°. Precision testing in clinical patients resulted in coefficients of variation for repeated measurements of 2.35% (intraobserver agreement) and 0.60% (interobserver agreement) for the tibial torsion angle, and 2.70% (intraobserver agreement) and 0.97% (interobserver agreement) for the tibial varus (or valgus) angle. The technique is lacking determination of bone deformities in the sagittal plane, and demonstration of accuracy in severe complex bone deformities in multiple planes.In conclusion, we developed a method to measure canine tibial torsional and varus or valgus deformities, that calculates in 3D space, and we demonstrated its accuracy in a torsional deformity model, and its precision in CT data of clinical patients.

Interstitial lung disease (ILD) after COVID-19: a new fibro-inflammatory disease?

Introduction. Nowadays post-COVID respiratory symptoms that could be associated with pulmonary fibrosis progression are of concern. Objective. To compare CT and SPECT data of patients with post-COVID pulmonary fibrosis, and to define whether the lung fibrosis progression could be predictable. Material and Methods. Changes in chest CT scan, microcirculation disorders (SPECT) and impaired lung function parameters (DLCO) were analyzed in 74 post-COVID patients with residual consequences of COVID-19. Results. A year or more after the disease, 17 % of patients had isolated ground-glass areas, 24 % of patients had ventilation mosaics and air traps, most patients had compaction of the interlobular interstitial tissue of a short UIP type (67 %); consolidation zones (38 %); zones of pulmonary fibrosis of different lengths (57 %); discoid atelectasis (39 %); bronchiectasis (26 %), pulmonary hypertension (PH) (36 %). Significant decrease of the diffusion capacity and great microcirculation disorders accompanied by more than 50 % perfusion lack were detected. We demonstrated that significant radiological and functional effects of viral pneumonia were likely to be associated with post-viral interstitial lung disease. Conclusions. 1. Complete X-ray examination with lung diffusion capacity determination can contribute to optimal dispensary observation of post-COVID patients. 2. Microcirculation disorder greater than 50 % of the norm is a predictor of the lung parenchyma changes and can contribute to the prediction of long-term effects of the disease. 3. Complete radiation monitoring is required for patients over 60 years of age; post-COVID patients having severe form of the disease; patients having respiratory complaints for more than a year, regardless of the severity of COVID-19.

Contralateral bridge fixation of freehand minimally invasive pedicle screws combined with unilateral MIS-TLIF vs. open TLIF in the treatment of multi-segmental lumbar degenerative diseases: A five years retrospective study and finite element analysis

To evaluate the efficacy, safety, feasibility and biomechanical stability of contralateral bridge fixation of freehand minimally invasive pedicle screws (Freehand MIPS) combined with unilateral minimally invasive surgery-transforaminal lumbar interbody fusion (MIS-TLIF) (smile-face surgery) and open TLIF for the treatment of multi-segmental lumbar degenerative diseases (LDDs). From January 2013 to January 2016, clinical data of multi-segmental (2- or 3-level) LDDs receiving smile-face surgery or open TLIF were retrospectively collected and analyzed. The back and leg pain VAS and ODI were used to assess clinical outcomes preoperatively and postoperatively. The MacNab criteria were used to evaluate the satisfaction of patient. The disc height (DH), lumbar lordosis (LL) and segmental lordosis angle (SLA) were measured before and after surgery. We used patient's CT data to establish the finite element model of smile-face surgery and open TLIF, and analyze biomechanical stability of two methods. Smile-face surgery group showed shorter operation time, shorter incision, less blood loss, shorter hospital stay than open TLIF (P < 0.05). The back VAS in smile-face surgery group was significantly lower than that in open TLIF immediately and 3 months after surgery, and no significant difference was observed 1 year, 2 years and 5 years after surgery. There was no significant difference in the leg pain VAS and ODI between both groups after surgery. No significant difference was observed between two groups in the DH, LL and SLA. At 5-year follow-up, grade I or II fusion was achieved in 99.00% (100/101) segments of smile-face surgery group and 97.67% (84/86) segments of open TLIF group according to Bridwell system. The complication rate of open TLIF was higher than that of smile-face surgery (24.32% vs. 0%, P < 0.01). After verification, the established finite element model can accurately simulate the biological structure of lumbar spine and there was no significant difference in biomechanical stability between two methods. Smile-face surgery has some advantages over open TLIF including smaller aggression, less blood loss, and lower cost, indicating that it is a good choice of treatment for multi-segmental LDDs. Both methods can achieve good biomechanical stability.

Virtual reconstruction and clinical verification of maxillary defect based on deep learning

Objective: To construct a virtual reconstruction method including midspan maxillary defects and provide clinical reference by training a generative adversarial network (GAN) model. Methods: The CT data of middle-aged Han patients with oral diseases who visited the Department of Radiology, West China Hospital of Stomatology, Sichuan University from June 2015 to June 2022 were collected, where the CT data of 100 healthy maxilla and 15 maxillary defects (5 simple unilateral defects, 5 unilateral defects involving zygomatic bone, 5 midspan defects) were selected. Mimics was used to create spherical phantom and simulate bone defects around the healthy maxillas, including simple unilateral defects, unilateral defects involving zygomatic bone and midspan defects. The original image was set as the correct reference for the reconstruction: artificial defects paired with the correct reference were divided into training set (n=70), validation set (n=20) and test set (n=10), where the first two were used to train the GAN model, and the test set was used to evaluate the GAN performance. Data from 15 clinical defects were imported into the trained GAN model for reconstruction, with mirroring and GAN-based virtual reconstruction for unilateral clinical defects, and only the latter method was adopted for midspan defects. The reconstruction results were divided into mirror reconstruction group (n=10), unilateral defect GAN reconstruction group (n=10) and midspan defect GAN reconstruction group (n=5). The test set, mirror reconstruction group, and unilateral defect GAN reconstruction group were quantitatively evaluated, whose quantitative indicators were Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95), and the group results were subjected to one-way ANOVA and Tukey test. The test set, mirror reconstruction group, unilateral defect GAN reconstruction group and midspan defect GAN reconstruction group were qualitatively scored, and Kruskal-Wallis test and Bonferroni correction were used for the total score of each group. Results: The total differences in the test set, mirror reconstruction group, unilateral defect GAN reconstruction group DCS (0.891±0.049, 0.721±0.047, 0.778±0.057, respectively) and HD95 [(3.58±1.51), (5.19±1.38), (4.51±1.10) mm, respectively] were statistically significant (F=28.08, P<0.001; F=3.62, P=0.041); among them, the test set DSC was significantly larger than the mirror reconstruction group (P<0.05), and the test set HD95 was significantly less than the mirror reconstruction group (P<0.05). Overall difference in qualitative total scores [8 (1), 6 (2), 6 (2), and 4 (2) points, respectively] in the test set, mirror reconstruction group, unilateral defect GAN reconstruction group, and midspan defect GAN reconstruction group were statistical significance (H=18.13, P<0.001); pairwise comparison showed that the total score of the test set was significantly higher than that of the mirror reconstruction group (P<0.05). Conclusions: The virtual reconstruction method based on GAN proposed in this study has better virtual reconstruction effect of unilateral defect than mirror technique, and can also realize virtual reconstruction of maxillary midspan defect.

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

The first case of novel Coronavirus (COVID-19) was reported in December 2019 in Wuhan City, China and led to an international outbreak. This virus causes serious respiratory illness and affects several other organs of the body differently for different patient. Worldwide, several waves of this infection have been reported, and researchers/doctors are working hard to develop novel solutions for the COVID diagnosis. Imaging and vision-based techniques are widely explored for the prediction of COVID-19; however, COVID infection percentage estimation is under explored. In this work, we propose a novel framework for the estimation of COVID-19 infection percentage based on deep learning techniques. The proposed network utilizes the features from vision transformers and CNN (Convolutional Neural Networks), specifically EfficientNet-B7. The features of both are fused together for preparing an information-rich feature vector that contributes to a more precise estimation of infection percentage. We evaluate our model on the Per-COVID-19 dataset (Bougourzi et al., 2021b) which comprises labeled CT data of COVID-19 patients. For the evaluation of the model on this dataset, we employ the most widely-used slice-level metrics, i.e., Pearson correlation coefficient (PC), Mean absolute error (MAE), and Root mean square error (RMSE). The network outperforms the other state-of-the-art methods and achieves 0.9886±0.009, 1.23±0.378, and 3.12±1.56, PC, MAE, and RMSE, respectively, using a 5-fold cross-validation technique. In addition, the overall average difference in the actual and predicted infection percentage is observed to be <2%. In conclusion, the detailed experimental results reveal the robustness and efficiency of the proposed network.