3D CT Images Research Articles

Uncertainty-aware image classification on 3D CT lung

Early detection is crucial for lung cancer to prolong the patient’s survival. Existing model architectures used in such systems have shown promising results. However, they lack reliability and robustness in their predictions and the models are typically evaluated on a single dataset, making them overconfident when a new class is present. With the existence of uncertainty, uncertain images can be referred to medical experts for a second opinion. Thus, we propose an uncertainty-aware framework that includes three phases: data preprocessing and model selection and evaluation, uncertainty quantification (UQ), and uncertainty measurement and data referral for the classification of benign and malignant nodules using 3D CT images. To quantify the uncertainty, we employed three approaches; Monte Carlo Dropout (MCD), Deep Ensemble (DE), and Ensemble Monte Carlo Dropout (EMCD). We evaluated eight different deep learning models consisting of ResNet, DenseNet, and the Inception network family, all of which achieved average F1 scores above 0.832, and the highest average value of 0.845 was obtained using InceptionResNetV2. Furthermore, incorporating the UQ demonstrated significant improvement in the overall model performance. Upon evaluation of the uncertainty estimate, MCD outperforms the other UQ models except for the metric, URecall, where DE and EMCD excel, implying that they are better at identifying incorrect predictions with higher uncertainty levels, which is vital in the medical field. Finally, we show that using a threshold for data referral can greatly improve the performance further, increasing the accuracy up to 0.959.

End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotyping of grapevine trunk internal structure

Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and artificial intelligence-based image processing that allowed a non-destructive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine (Vitis vinifera L.). Vineyard’s sustainability is threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key-measurements in evaluating vines’ sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of tissue quality and plant health across plant species.

High three-dimensional accuracy of component placement and lower limb alignment using a robotic arm-assisted system and gap-balancing instrument in total knee arthroplasty.

It was hypothesized that robotic arm-assisted total knee arthroplasty (RA-TKA) using additionally a gap-balancing instrumentation will show high accuracy in executing the planning in femoral and tibial component placement throughout the range of knee motion (ROM) during TKA surgery. Prospectively collected data were analysed for patients undergoing RA-TKA. A cruciate retaining cemented design was implanted using the MAKO® robotic system. Lower limb alignment at 0°, 30°, 45°, 60° and 90° of flexion was recorded at the beginning of surgery and finally after implantation of the components. A ligament tensioner was inserted after tibial precut to measure the extension and flexion gap, and final component placement was planned based on 3D CT images. Femoral and tibial component placement was measured in all three planes. A total of 104 patients were included (mean age 69.4 ± 9 years; 44 male, 60 female). The difference in component placement after planning and final implantation showed less valgus of 0.7° ± 1.4° (p < 0.001), less external rotation of 0.6° ± 1.9° (p = 0.001) and less flexion of 0.9° ± 1.8° (p < 0.001) for the femoral component. The tibial component was placed in more varus of 0.2° ± 0.9° (p = 0.056) and more posterior slope of 0.5° ± 0.9° (p < 0.001). The lower limb alignment in extension was 4.4° ± 5.2° of varus of the native knee and changed to 1.2° ± 1.9° of varus after TKA (p < 0.01). Robotic-assisted TKA helps to achieve the target of alignment and component placement very close to the planning. It allows optimal component placement of off-the-shelf implants respecting patient's specific anatomy. Level II.

Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques.

Aorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms and occlusive disease. In such cases, image segmentation is prerequisite for applying diagnostic algorithms, which in turn allow the prediction of possible complications and enable risk assessment, which is crucial in saving lives. The aim of this paper is to present a novel fully automatic 3D segmentation method, which combines basic image processing techniques and more advanced machine learning algorithms, for detecting and modelling the aorta in 3D CT imaging data. An initial intensity threshold-based segmentation procedure is followed by a classification-based segmentation approach, based on a Markov Random Field network. The result of the proposed two-stage segmentation process is modelled and visualized. The proposed methodology was applied to 16 3D CT data sets and the extracted aortic segments were reconstructed as 3D models. The performance of segmentation was evaluated both qualitatively and quantitatively against other commonly used segmentation techniques, in terms of the accuracy achieved, compared to the actual aorta, which was defined manually by experts. The proposed methodology achieved superior segmentation performance, compared to all compared segmentation techniques, in terms of the accuracy of the extracted 3D aortic model. Therefore, the proposed segmentation scheme could be used in clinical practice, such as in treatment planning and assessment, as it can speed up the evaluation of the medical imaging data, which is commonly a lengthy and tedious process.

Sacroiliitis diagnosis based on interpretable features and multi-task learning

Objective. Sacroiliitis is an early pathological manifestation of ankylosing spondylitis (AS), and a positive sacroiliitis test on imaging may help clinical practitioners diagnose AS early. Deep learning based automatic diagnosis algorithms can deliver grading findings for sacroiliitis, however, it requires a large amount of data with precise labels to train the model and lacks grading features visualization. In this paper, we aimed to propose a radiomics and deep learning based deep feature visualization positive diagnosis algorithm for sacroiliitis on CT scans. Visualization of grading features can enhance clinical interpretability with visual grading features, which assist doctors in diagnosis and treatment more effectively. Approach. The region of interest (ROI) is identified by segmenting the sacroiliac joint (SIJ) 3D CT images using a combination of the U-net model and certain statistical approaches. Then, in addition to extracting spatial and frequency domain features from ROI according to the radiographic manifestations of sacroiliitis, the radiomics features have also been integrated into the proposed encoder module to obtain a powerful encoder and extract features effectively. Finally, a multi-task learning technique and five-class labels are utilized to help with performing positive tests to reduce discrepancies in the evaluation of several radiologists. Main results. On our private dataset, proposed methods have obtained an accuracy rate of 87.3%, which is 9.8% higher than the baseline and consistent with assessments made by qualified medical professionals. Significance. The results of the ablation experiment and interpreting analysis demonstrated that the proposed methods are applied in automatic CT scan sacroiliitis diagnosis due to their excellently interpretable and portable advantages.

Six-Part Classification of Femoral Intertrochanteric Fractures: A Classification Method to Improve the Diagnosis Rate of Unstable Fractures.

Three-dimensional (3D)-CT data is currently insufficient for classifying femoral trochanter fractures. Fracture classification based on fracture stability analysis is helpful to evaluate the prognosis of patients after internal fixation. Currently, there is a lack of fracture classification methods based on 3D-CT images and fracture stability analysis. The aim of this study was to propose a new six-part classification method for intertrochanteric fractures of femur based on 3D-CT images and fracture stability analysis to improve the diagnosis rate of unstable fractures. From January 2009 to December 2019, 320 patients receiving intramedullary nail surgery for femoral intertrochanteric fractures at Chengdu University's Affiliated Hospital were studied retrospectively. AO and six-part classifications were undertaken according to the 3D-CT image data of the patients, and the stability rates of two classifications were compared. According to the six-part classification stability criteria, the patients were divided into a stable and an unstable fracture group. The perioperative and follow-up indicators of the two groups were statistically analyzed, and the six-part classification's inter-observer and internal reliability was examined. There were 107 men and 213 females women the 320 patients, with an average age of 79.32 ± 11.26 years and an osteoporosis rate of 55.63% (178/320). The fracture stability rate of 39.69% (127/320) was studied using a six-part classification method. The AO classification fracture stability rate was 42.50% (136/320), with no significant difference (χ2 = 0.523, p = 0.470 > 0.05). There is no statistically significant difference between the two classification techniques in the examination of fracture stability (McNemer difference test p = 0.306 > 0.05; Kappa consistency test p < 0.001). According to the six-part classification, fracture stability and instability group were divided into two groups. The following indicators were compared between the two groups: The surgery time (p = 0.280), fracture reduction quality (p = 0.062); function independent measurement (p = 0.075); timed up and go test (TUG) (p = 0.191), and Parker-Palmer score (p = 0.146). Were as compared according to the six-part classification of stable and unstable fracture groups. Perioperative blood loss (p < 0.001), the Harris score excellent and good rate (p = 0.043), fracture healing time (p < 0.001), and the entire weight-bearing duration (p = 0.002) were statistically significant. The difference in femoral head height (FHH) (p = 0.046), the change in femoral neck shaft angle (p = 0.003), the change in medial cephalic nail length (p = 0.033), and the change in tip-apex distance (TAD) (p = 0.002) were statistically significant compared to the relevant markers of imaging stability. Fracture stability had a substantial influence on Harris ratings at 3, 6, and 12 months following surgery, according to repeated measures analysis of variance (F(1,126) = 32.604, p < 0.001). The effect of time on the Harris score was similarly significant (F(1.893,238.508) = 202.771, p < 0.001). The observer intra-observer inter-group correlation coefficient (ICC) value was 0.941 > 0.75, the inter-observer ICC value was 0.921 > 0.75, and the intra-observer and inter-observer reliability were both good. The six-part classification of femoral intertrochanteric fractures based on 3D-CT images has broader guiding relevance for femoral intertrochanteric fracture stability analysis. Clinicians will find this classification simpler and more consistent than the AO classification.

A formula for instability-related bone loss: estimating glenoid width and redefining bare spot

PurposeThe aim of the study reveals a new intuitive method for preoperatively assessing defect ratio in glenoid deficiency based on the native glenoid width and the bare spot.MethodsA linear relationship, i.e. the rh formula, between the native glenoid width (2r) and height (h) was revealed by a cadaver cohort (n = 204). To validate the reliability of the rh formula, 280 3D-CT images of intact glenoids were recruited. To evaluate the accuracy of rh formula in estimating glenoid defect, the 65 anterior–inferior defect models were artificially established based on the 3D-CT images of intact glenoids. Moreover, a clinically common anterior–posterior (AP) method was compared with the rh formula, to verify the technical superiority of rh formula.ResultsThe regression analysis indicated a linear relationship between the width and height of intact glenoid: 2r = 0.768 × h − 1.222 mm (R2 = 0.820, p < 0.001). An excellent reliability was found between the formula prediction and model width (ICC = 0.911, p = 0.266). An excellent agreement was found between the predicted values and model parameters (glenoid width, ICCrh = 0.967, prh = 0.778; defect ratio, prh = 0.572, ICCrh = 0.997). And, it is of higher accuracy compared to the AP method (glenoid width, ICCAP = 0.933, pAP = 0.001; defect ratio, ICCAP = 0.911, pAP = 0.033).ConclusionApplying the cadaver-based formula on 3D-CT scans accurately predicts native glenoid width and redefines bare spot for preoperatively determining glenoid bone loss.

Influence of Respiratory Motion on Dose Distribution in Gastric Mucosa-associated Lymphoid Tissue Lymphoma Radiotherapy.

The present study investigated the effect of respiratory motion on planned radiotherapy (RT) dose for gastric mucosa-associated lymphoid tissue (MALT) lymphoma using four-dimensional dose (4D-dose) accumulation. 4D-computed tomography (4D-CT) images of 10 patients with gastric MALT lymphomas were divided into 10 respiratory phases. Further, the 3D-dose was calculated using 3D conformal RT (3D-CRT) and volumetric modulated arc therapy (VMAT) plans based on the average intensity projection (AIP) images. Then, both plans were recalculated according to each phase image. Moreover, the dose distributions in each phase were transferred to the AIP images using deformable image registration. The 4D-dose distribution was calculated by summing the doses of each phase, and it was compared with the dosimetric parameters of the 3D-dose distribution. For 3D-CRT, the D95 and D99 of the 4D-dose in the planning target volume (PTV) were significantly lower than those of the 3D-dose, with mean differences of 0.2 (p=0.009) and 0.1 Gy (p=0.021), respectively. There were no significant differences in the other PTV and organ-at-risk dosimetric parameters of 3D-CRT or in any dosimetric parameters of VMAT between the 3D- and 4D-dose distributions. The effect of respiratory motion on the planned 3D-CRT and VMAT dose distributions for gastric MALT lymphoma is minimal and clinically negligible.

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks.

For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.

Automatic Vertebral Rotation Angle Measurement of 3D Vertebrae Based on an Improved Transformer Network.

The measurement of vertebral rotation angles serves as a crucial parameter in spinal assessments, particularly in understanding conditions such as idiopathic scoliosis. Historically, these angles were calculated from 2D CT images. However, such 2D techniques fail to comprehensively capture the intricate three-dimensional deformities inherent in spinal curvatures. To overcome the limitations of manual measurements and 2D imaging, we introduce an entirely automated approach for quantifying vertebral rotation angles using a three-dimensional vertebral model. Our method involves refining a point cloud segmentation network based on a transformer architecture. This enhanced network segments the three-dimensional vertebral point cloud, allowing for accurate measurement of vertebral rotation angles. In contrast to conventional network methodologies, our approach exhibits notable improvements in segmenting vertebral datasets. To validate our approach, we compare our automated measurements with angles derived from prevalent manual labeling techniques. The analysis, conducted through Bland-Altman plots and the corresponding intraclass correlation coefficient results, indicates significant agreement between our automated measurement method and manual measurements. The observed high intraclass correlation coefficients (ranging from 0.980 to 0.993) further underscore the reliability of our automated measurement process. Consequently, our proposed method demonstrates substantial potential for clinical applications, showcasing its capacity to provide accurate and efficient vertebral rotation angle measurements.

An automatic segmentation algorithm for conglutinated bone fragments in 3D CT images of lower limb fractures based on morphology

An automatic segmentation algorithm for conglutinated bone fragments in 3D CT images of lower limb fractures based on morphology

Diagnosis and treatment of neonatal machete syndrome

Aim: To provide suggestions for clinical diagnosis, treatment and eugenics through the retrospective analysis of a case of neonatal machete syndrome.Methods: The clinical data of a newborn with machete syndrome admitted to the pediatrics department of Baogang Hospital of Inner Mongolia were analyzed, and suggestions of eugenics were provided based on the literatures.Results: The child, male, natural conception, G2P2, gestational age 38+4 weeks, was born by cesarean section (at am 09:24 on November 13th, 2021). He was hospitalized in our department for 9 days and given NCPAP positive pressure ventilation to give oxygen and anti-infection symptomatic treatment. The dyspnea of the child was not significantly alleviated, and the heart murmurs became increasingly obvious. The child was transferred to the neonatology Department of the Seventh Medical Center of the PLA General Hospital for further treatment on 9 days after birth. After admission, piperacillin and tazobactam were treated with anti-infection, high-frequency oscillation mode of invasive ventilator, limited fluid volume and other symptomatic treatments. Fiberbronchoscopy showed atelectasis of right lung and tracheomalacia of left lung. Echocardiography showed partial anomalous pulmonary venous drainage, atrial septal defect (secondary foramina), inferior vena cava dilatation, pulmonary hypertension, initial coronary artery dilatation, and patent ductus arteriosus. CT 3D imaging of pulmonary veins showed: Congenital heart disease: partial anomalous pulmonary venous drainage (subcardial), consideration of "machete syndrome," atrial septal defect, consideration of pulmonary hypertension, right pulmonary artery stenosis, right lower lung receiving abdominal aorta blood supply, right lung tissue dysplasia, and widening of inferior vena cava pulmonary veins. Therefore, the comprehensive diagnosis was "machete syndrome." Considering the high surgical risk of the child, conservative symptomatic treatment was recommended, and the family requested discharge after 22 days of treatment. The patient died at home at 3 months follow-up after discharge.Conclusions: Machete syndrome is a rare congenital property of abnormal partial or complete pulmonary venous drainage from the right or left lung to the inferior vena cava. With an incidence of approximately 2 in 100,000 live births, the syndrome is commonly associated with right lung dysplasia, pulmonary sequestration, persistent left superior vena cava, and right tilt of the heart. In this case, the child had these syndromes after birth, since the primary hospital did not recognize the disease, the child was clearly diagnosed in the higher hospital. Children with machete syndrome can be treated with surgery in the early neonatal period, but the indication of surgical treatment is still controversial, and the need for surgery is evaluated according to the severity of the patient's symptoms and pulmonary circulation blood flow.

Predicting primate tongue morphology based on geometrical skull matching. A first step towards an application on fossil hominins.

As part of a long-term research project aiming at generating a biomechanical model of a fossil human tongue from a carefully designed 3D Finite Element mesh of a living human tongue, we present a computer-based method that optimally registers 3D CT images of the head and neck of the living human into similar images of another primate. We quantitatively evaluate the method on a baboon. The method generates a geometric deformation field which is used to build up a 3D Finite Element mesh of the baboon tongue. In order to assess the method's ability to generate a realistic tongue from bony structure information alone, as would be the case for fossil humans, its performance is evaluated and compared under two conditions in which different anatomical information is available: (1) combined information from soft-tissue and bony structures; (2) information from bony structures alone. An Uncertainty Quantification method is used to evaluate the sensitivity of the transformation to two crucial parameters, namely the resolution of the transformation grid and the weight of a smoothness constraint applied to the transformation, and to determine the best possible meshes. In both conditions the baboon tongue morphology is realistically predicted, evidencing that bony structures alone provide enough relevant information to generate soft tissue.

Prediction of Pharyngeal 3D Volume Using 2D Lateral Area Measurements During Swallowing.

This study evaluated the validity of pharyngeal 2D area measurements acquired from the lateral view for predicting the actual 3D volume in healthy adults during swallowing. Seventy-five healthy adults (39 females, 36 males; mean age 51.3years) were examined using 320-row area detector computed tomography (320-ADCT). All participants swallowed a 10mL honey-thick barium bolus upon command while seated in a 45° semi-reclining position. Multi-planar reconstruction images and dynamic 3D-CT images were obtained using Aquilion ONE software. Pharyngeal 2D area and 3D volume measurements were taken before swallowing and at the frame depicting maximum pharyngeal constriction. Pharyngeal volume before swallowing (PVhold) was accurately predicted by 2D area (R2 = 0.816). Adding height and sex to the model increased R2 to 0.836. Regarding pharyngeal volume during maximum constriction (PVmax), 2D area also exhibited acceptable predictive power (R2 = 0.777). However, analysis of statistical residuals and outliers revealed a greater tendency for prediction errors when there is less complete constriction of the pharynx as well as asymmetry in bolus flow or movement. Findings highlight the importance of routinely incorporating anterior-posterior views during VFSS exams. Future work is needed to determine clinical utility of pharyngeal volume measurements derived from 320-ADCT.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning

ObjectiveAim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). MethodsIn this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model β was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. ResultsA total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873–0.960) among all models [Model α: 0.709 (0.618–0.800); Model β: 0.749 (0.675–0.824); Model γ: 0.687 (0.592–0.782); MCTSI: 0.778 (0.698–0.857); RANSON: 0.642 (0.559–0.725); BISAP: 0.751 (0.668–0.833); SABP: 0.710 (0.621–0.798)]. ConclusionThe proposed multimodal model outperformed any single-modality models and traditional scoring systems.

Motion correction of wide-detector 4DCT images for cardiac resynchronization therapy planning

BackgroundLead placement at the latest mechanically activated left ventricle (LV) segments is strongly correlated with response to cardiac resynchronization therapy (CRT). We demonstrate the feasibility of a cardiac 4DCT motion correction algorithm (ResyncCT) in estimating LV mechanical activation for guiding lead placement in CRT. MethodsSubjects with full cardiac cycle 4DCT images acquired using a wide-detector CT scanner for CRT planning/upgrade were included. 4DCT images exhibited motion artifact-induced false-dyssynchrony, hindering LV mechanical activation time estimation. Motion-corrupted images were processed with ResyncCT to yield motion-corrected images. Time to onset of shortening (TOS) was estimated in each of 72 endocardial segments. A false-dyssynchrony index (FDI) was used to quantify the extent of motion artifacts in the uncorrected and the ResyncCT images. After motion correction, the change in classification of LV free-wall segments as optimal target sites for lead placement was investigated. ResultsTwenty subjects (70.7 ​± ​13.9 years, 6 female) were analyzed. Motion artifacts in the ResyncCT-processed images were significantly reduced (FDI: 28.9 ​± ​9.3 ​% vs 47.0 ​± ​6.0 ​%, p ​< ​0.001). In 10 (50 ​%) subjects, ResyncCT motion correction yielded statistically different TOS estimates (p ​< ​0.05). Additionally, 43 ​% of LV free-wall segments were reclassified as optimal target sites for lead placement after motion correction. ConclusionsResyncCT significantly reduced motion artifacts in wide-detector cardiac 4DCT images, yielded statistically different time to onset of shortening estimates, and changed the location of optimal target sites for lead placement. These results highlight the potential utility of ResyncCT motion correction in CRT planning when using wide-detector 4DCT imaging.

Clinical trainee performance on task-based AR/VR-guided surgical simulation is correlated with their 3D image spatial reasoning scores.

This paper describes a methodology for the assessment of training simulator-based computer-assisted intervention skills on an AR/VR-guided procedure making use of CT axial slice views for a neurosurgical procedure: external ventricular drain (EVD) placement. The task requires that trainees scroll through a stack of axial slices and form a mental representation of the anatomical structures in order to subsequently target the ventricles to insert an EVD. The process of observing the 2D CT image slices in order to build a mental representation of the 3D anatomical structures is the skill being taught, along with the cognitive control of the subsequent targeting, by planned motor actions, of the EVD tip to the ventricular system to drain cerebrospinal fluid (CSF). Convergence is established towards the validity of this assessment methodology by examining two objective measures of spatial reasoning, along with one subjective expert ranking methodology, and comparing these to AR/VR guidance. These measures have two components: the speed and accuracy of the targeting, which are used to derive the performance metric. Results of these correlations are presented for a population of PGY1 residents attending the Canadian Neurosurgical "Rookie Bootcamp" in2019.

Quantitative anatomical analysis of lumbar interspaces based on 3D CT imaging: optimized segment selection for lumbar puncture in different age groups

BackgroundOptimal lumbar puncture segment selection remains controversial. This study aims to analyze anatomical differences among L3-4, L4-5, and L5-S1 segments across age groups and provide quantitative evidence for optimized selection.Methods80 cases of CT images were collected with patients aged 10–80 years old. Threedimensional models containing L3-S1 vertebrae, dural sac, and nerve roots were reconstructed. Computer simulation determined the optimal puncture angles for the L3-4, L4-5, and L5-S1 segments. The effective dural sac area (ALDS), traversing nerve root area (ATNR), and area of the lumbar inter-laminar space (ALILS) were measured. Puncture efficacy ratio (ALDS/ALILS) and nerve injury risk ratio (ATNR/ALILS) were calculated. Cases were divided into four groups: A (10–20 years), B (21–40 years), C (41–60 years), and D (61–80 years). Statistical analysis was performed using SPSS.Results1) ALDS was similar among segments; 2) ATNR was greatest at L5-S1; 3) ALILS was greatest at L5-S1; 4) Puncture efficacy ratio was highest at L3-4 and lowest at L5-S1; 5) Nerve injury risk was highest at L5-S1. In group D, L5-S1 ALDS was larger than L3-4 and L4-5. ALDS decreased after age 40. Age variations were minimal across parameters.ConclusionThe comprehensive analysis demonstrated L3-4 as the optimal first-choice segment for ages 10–60 years, conferring maximal efficacy and safety. L5-S1 can serve as an alternative option for ages 61–80 years when upper interspaces narrow. This study provides quantitative imaging evidence supporting age-specific, optimized lumbar puncture segment selection.

The utility of three-dimensional modeling and printing in pediatric surgical patient and family education: a systematic review

BackgroundThree-dimensional (3D) modeling and printing are increasingly being used in surgical settings. This technology has several applications including pre-operative surgical planning, inter-team communication, and patient education and counseling. The majority of research on 3D technology has focused on adult populations, where it has been found to be a useful tool for educating patients across various surgical specialties. There is a dearth, however, of research on the utility of 3D modeling and printing for patient and family education in pediatric populations. Our objective was to systematically review the current literature on how this modality is being utilized in pediatric surgical settings for patient and family education and counselling.MethodsWe conducted a systematic review in accordance with PRISMA and CASP guidelines. The MEDLINE, CINAHL, Embase, and Web of Science databases were searched from inception to October 21, 2023, with no restrictions on language or geographical location. Citation chaining was used to ensure relevant papers were included. Articles were doubly screened and data was extracted independently by two authors. In the case of disagreement, a third author was consulted. Any articles pertaining to 3D modeling and printing in pediatric surgical settings for patient and family education and counseling were included.ResultsSix articles met inclusion criteria and were used for qualitative analysis. Two involved questionnaires given to parents of children to assess their understanding of relevant anatomy, surgical procedure, and risks after viewing conventional CT images and again after viewing a 3D-printed model. One involved a quasi-experimental study to assess young patients’ pre-operative surgical understanding and anxiety after undergoing conventional teaching as compared to after viewing a 3D storybook. One involved questionnaires given to parents of children in control and study groups to assess the usefulness of 3D printed models compared to conventional CT images in their understanding of relevant anatomy and the surgical procedure. Another study looked at the usefulness of 3D printed models compared to 2D and 3D CT images in providing caregiver understanding during the pre-operative consent process. The last article involved studying the impact of using 3D printing to help patients understand their disease and participate in decision-making processes during surgical consultations. In all six studies, utilizing 3D technology improved transfer of information between surgical team members and their patients and families.ConclusionOur systematic review suggests that 3D modeling and printing is a useful tool for patient and family education and counselling in pediatric surgical populations. Given the very small number of published studies, further research is needed to better define the utility of this technology in pediatric settings.

Type and Fusion Identification by Age and Sex in Human Hyoid Bone Using 3D CT Images in a Turkish Sample

Type and Fusion Identification by Age and Sex in Human Hyoid Bone Using 3D CT Images in a Turkish Sample