3D Computed Tomography Research Articles

Evaluation of Recovery Efficacy of Inspiratory Muscle Training After Lobectomy Based on Computed Tomography 3D Reconstruction.

Progressive resistance inspiratory muscle training is the principle of inspiratory air-flow resistance loading training to restore diaphragm function, increase alveolar compliance, and further improve respiratory function. However, there is a lack of research on the effectiveness of progressive resistance inspiratory training in post-lobectomy rehabilitation and the accurate assessment of lung volumes. In this study, 79 subjects diagnosed with lung cancer and undergoing thoracoscopic lobectomy were retrospectively analyzed. The subjects were divided into a control group (n = 40) and an observation group (n =39) according to the different training modalities. The control group received conventional respiratory training. The observation group received progressive resistance inspiratory muscle training based on conventional breathing training. The primary outcome indicators were the following: lung function and lung volume. The secondary outcome indicators were the following: the number of postoperative hospital days, duration of drain retention, and incidence of postoperative pulmonary complications. Baseline data on age, sex, body mass index, smoking history, education level, underlying disease, type of pathology, lung cancer stages, surgical site, preoperative lung volume, and preoperative lung function were not statistically different between the 2 groups (P > .05). The subjects in the observation group had median (interquartile range [IQR]) lung volumes at 1 month after surgery (3.22 [3.12-3.37] L vs 3.14 [2.95-3.24] L; P = .031), median (IQR) FEV1 (2.11 [1.96-2.21] L vs 2.01 [1.81-2.12] L; P = .031), and mean ± SD peak expiratory flow (5.07 ± 0.62 L/s vs 4.66 ± 0.64 L/s; P = .005) were higher than those in the control group. The median (IQR) postoperative hospital stays (5 [4-5] d vs 5 [4-6] d; P = .030) and the median (IQR) chest drain retention times were shorter in the observation group versus the control group (74 [72-96] h vs 96 [84-96] h; P = .02). There was no significant difference in the incidence of postoperative atelectasis (5.1% vs 10.0%; P = .41) and pneumonia (7.7% vs 12.5%; P = .48). Progressive resistance inspiratory muscle training was effective in improving lung volume and lung function, and in reducing the length of hospital stay and chest drain closure time after lobectomy.

Melon and rostral muscle morphology of Gervais' beaked whale (Mesoplodon europaeus): Alternating patterns of bilateral asymmetry.

Toothed whales utilize specialized nasal structures such as the lipid-rich melon to produce sound and propagate it into the aquatic environment. Very little nasal morphology of mesoplodont beaked whales has been described in the literature, and the anatomy of the melon and associated musculature of Gervais' beaked whale (Mesoplodon europaeus) remains undescribed. Heads of three (n = 3) Gervais' beaked whales were examined in detail via dissection as well as computed tomography (CT) and magnetic resonance imaging (MRI). Two additional Gervais' beaked whale individuals (n = 2) were studied via archived CT and MRI scans. Representative transverse dissection sections of the melon were processed for polarized light imaging to verify the presence of tendons inserting into the melon tissue. Three-dimensional (3D) CT reconstructions of the melon, rostral muscles, and associated structures were performed to assess morphology and spatial relationships. In all individuals, the melon's main body demonstrated a bilaterally asymmetrical, curvilinear geometry. This curvilinear shape was defined by a pattern of alternating asymmetry in the medial rostral muscles that projected into the melon's tissue. In transverse polarized light imaging, a network of tendons originating from these asymmetrical rostral muscle projections was observed permeating the melon's lipid tissue. This curvilinear melon morphology and associated asymmetrical musculature suggest a means of lengthening the lipid pathway within a relatively short dimensional footprint. In addition, the species-specific arrangement of muscular projections suggests complex fine-tuning of the melon's geometry during echolocation. Further studies may lend additional insight into the function of this unusual melon morphology.

The Reliability of Measurements for Tibial Torsion: A Comparison of CT, MRI, Biplanar Radiography, and 3D Reconstructions With and Without Standardized Measurement Training

Background: Accurate and reliable assessment of tibial torsion is important for the identification and treatment of tibial rotation malalignment; however, the ideal rotational measurement modality and technique are controversial. This study compares rotational measurements between computed tomography (CT), magnetic resonance imaging (MRI), biplanar erect radiograph (BER) reconstructions, and three-dimensional (3D) reconstructed CT before and after standardized training of observers to evaluate the reliability of each assessment method.Methods: Eight adult cadaveric specimens underwent CT, MRI, and BER imaging. Tibial torsion was measured by three independent observers (one resident and two experienced orthopaedic surgeons) both before and after standardized measurement instruction. Reliability for inter-observer measurement was evaluated using the Intraclass Correlation Coefficient (ICC). Measurement values for CT, MRI, and BER reconstructions were compared to 3D CT reconstructions analyzed using custom software to align and measure tibial torsion (used as the reference standard).Results: Before training, there was poor inter-observer reliability for CT (ICC=0.492, p=0.014) and moderate interobserver reliability for MRI (ICC=0.633, p=0.002). There was no inter-method reliability between 3D CT and MRI for two of the three surgeons and moderate to good reliability between 3D CT and CT. After training, the interobserver reliability for CT improved to 0.536 and the inter-observer reliability for MRI improved to 0.701. The BER measurements (no observer involvement) had moderate reliability compared to the 3D CT reconstructions (ICC=0.69, p=0.026). Measurement error was 4 degrees for CT pre-training and 7 degrees post-training, and 7 degrees for MRI pre-training and 8 degrees post-training.Conclusions: A standardized training regimen for MRI measurements improved both inter-observer and intraobserver reliability. Inter-method reliability between CT, MRI, and BER compared to reference 3D CT reconstructions demonstrated that all imaging modalities are a valid means to measure tibial rotation but differ in reliability from moderate to good. When assessing tibial torsional deformities, it is important to consider these variations from true rotation and feel comfortable using them for pre-operative planning purposes.Level of Evidence: N/A for a cadaveric imaging study Key Concepts•A standardized training regimen for MRI measurements of tibial torsion significantly improved inter-observer and intra-observer reliability.•CT, MRI, and BER measurements are valid imaging modalities compared to 3D CT reference standards for measuring tibial rotation.•Established manual measurements of tibial torsion from MRI may be improved with adequate training.•Measurement error of up to 8 degrees can be seen from true tibial torsion during the imaging assessment.

Imaging Findings in Pediatric Accessory Cranial Sutures using 3D CT Reconstruction: Fracture or Rudimentary Suture.

Objective:Although accessory sutures are considered to be relatively rare, the consequences of a missed diagnosis are profound. Distinguishing between accessory sutures and cranial fractures can be difficult, especially in cases of suspected non-accidental trauma. High-resolution imaging is the best way to discern between two- and three-dimensional computerized tomography (3D CT) is considered the preferred method for evaluation. The goal of this study was to determine the impact of 3D CT scans in distinguishing between accessory sutures and cranial fractures in suspected child abuse cases in a rural community and the importance of early detection in such cases, as well as call attention to the consequences of initial misinterpretation. Materials and methods:The researchers conducted a retrospective chart review of all pediatric patients diagnosed with cranial fractures (265 in total) at University Medical Center between May 30, 2016, and May 30, 2021. Initial computed tomography (CT) scans and subsequent 3D CT scans were evaluated for each patient that fit the inclusion criteria, 13 in total. Patients were then categorized into two groups based on the final diagnosis on the radiology report: accessory cranial suture or cranial fracture. Once these patients were identified, the etiology and structural components of each were evaluated, and the key differences were highlighted. Results:Our results showed that, of the 11 cases of suspected non-accidental trauma, six were finally diagnosed with accessory sutures with the use of 3D CT scans, and of those six, four were diagnosed with cranial fractures from the initial CT scan report due to a similar presentation and asymmetric nature. Conclusion:Discerning between fracture and accessory suture is essential in evaluating pediatric patients presenting with signs ofcranial fracture due to the increased risk of misinterpretation that can lead to severe legal consequences considering that cranial suture variants may mimic intentional injury and be mistaken for child abuse, causing significant distress for patients and their families.

The optimal tibial tunnel placement to maximize the graft bending angle in the transtibial posterior cruciate ligament reconstruction: a quantitative assessment in three-dimensional computed tomography model.

The graft bending angle created by the graft and the tibial tunnel has inevitably occurred during the transtibial posterior cruciate ligament (PCL) reconstruction. However, few studies quantitively analyzed this angle. This study aimed to (I) explore the optimal tibial tunnel placement to maximize the graft bending angle in the PCL reconstruction; (II) reveal the effect of the tibial tunnel placement on the graft bending angle. This was an in-vitro surgical simulation study based on the three-dimensional (3D) computed tomography (CT). A total of 55 patients who took CT scanning for knee injuries were selected (April 2020 to January 2022) from the local hospital database for review. The 3D knee models were established on the Mimics software based on the knees' CT data. Using the Rhinoceros software to simulate the transtibial PCL reconstruction on the 3D CT knee model. The anteromedial and anterolateral tibial tunnel approaches were simulated with different tibial tunnel angle. The graft bending angle and tibial tunnel length (TTL) with different tibial tunnel angles were quantitively analyzed. The graft bending angle in anterolateral approach with a 50° tibial tunnel angle was significantly greater than it in anteromedial approach with a 60° tibial tunnel angle (P<0.001). There was no difference of the graft bending angle between the anterolateral approach with a 40° tibial tunnel angle and the anteromedial approach with a 60° tibial tunnel angle (P>0.05). The graft bending angle showed a strong correlation with the tibial tunnel angle (for anteromedial approach: r=0.759, P<0.001; for anterolateral approach: r=0.702, P<0.001). The best-fit equation to calculate the graft bending angle based on the tibial tunnel angle was Y = 0.89*X + 59.05 in anteromedial tibial tunnel approach (r2=0.576), and was Y = 0.78*X + 80.21 anterolateral tibial tunnel approach (r2=0.493). The graft bending angle and TTL will significantly increase as the tibial tunnel angle becomes greater. Maximizing the tibial tunnel angle (50° tibial tunnel angle) in the anterolateral approach could provide the greatest graft bending angle in the PCL reconstruction. No matter how the tibial tunnel angle is changed in the anteromedial approach, using anterolateral approach might reduce the killer turn effect more effectively than using anteromedial approach.

Does preoperative 3D CT planning helps in predicting the component size determination and alignment in automatic robotic total knee arthroplasty (RA-TKA)

PurposeImage-based Robotic Total knee Arthroplasty (RA-TKA)was developed with the purpose of enhancing the accuracy in determining the component sizes preoperatively and helping surgeons in minimizing errors and improve patient outcomes. The research aims to find the reliability of robotic-assisted TKR based on images in determining the correct component sizes using preoperative three-dimensional (3D) computer tomography. MethodAfter ethical approval, we conducted a prospective study from March 2022 to December 2022. A total of 100 knees underwent image-based RA-TKA having grade 4 Osteoarthritis knee (Kellegren Lawrence classification). A single senior surgeon performed on all patients. Postoperative implant sizes and fit were assessed by five radiographic markers by an independent observer. ResultsIn our study, we found the mean age was (64.96 ± 7.3) years, with female to male ratio of 43:22. The preoperative 3D CT accuracy is 100% for femoral component sizing and 97% for the tibial component. There was a statistically significant improvement in varus deformity from preoperative 7.370 ± 3.70° to 1.24 0 ± 0.910° after surgery., p = 0.001. Improvement in flexion deformity correction was from preoperative 6.50 ± 6.30 to postoperative 1.640 ± 1.770, p = 0.001. ConclusionOur study concludes that the use of pre-operative 3D CT helps in predicting the component sizes, minimizes surgical time, and enhances implant position accuracy, as well as improves postoperative limb alignment in the coronal and sagittal planes.

The accuracy of an accelerometer-based portable navigation system for total hip arthroplasty using 3D CT measurement in the supine position: a prospective multicenter study

PurposeThis multicenter prospective study aimed to evaluate the accuracy of the cup alignment of an accelerometer-based portable navigation system (AP navigation) for a total hip arthroplasty (THA) in spine position. MethodsThis study prospectively enrolled 324 THAs using supine position in 9 hospitals. An AP navigation was used for cup fixation and the intraoperative navigation data (cup alignment, pelvic tilt, and pelvic rotation) were recorded. Computed tomography (CT) of the pelvis was performed 2 weeks post-operatively and cup alignment was measured on 3D-CT images. ResultThe accuracy (absolute difference in cup alignment between the intraoperative navigation record and postoperative 3D-CT measurements) was 3 ​± ​3° (mean ​± ​standard deviation) for cup inclination and 3 ​± ​3° for cup anteversion. The pelvis tilted anteriorly in 148 hips (46%) and posteriorly in 162 hips (50%), and did not tilt in 14 hips (4%). The pelvis rotated toward the operating side in 179 hips (55%), toward the contralateral side in 112 hips (35%), and did not rotate in 33 hips (10%). Multiple regression analysis showed that the patients’ characteristics (sex, body mass index, diagnosis, Crowe classification, laterality, intraoperative pelvic tilt, intraoperative pelvic rotation, and cup size) and the surgical factors (approach, preparation time, number of screws, and institution) did not affect the accuracy of the navigation system. ConclusionsThis is the first prospective multicenter study of this navigation system. The accuracy was not affected by patient characteristics, surgical factors, or substantial pelvic movement during cup fixation.

On Krylov methods for large-scale CBCT reconstruction.

Krylov subspace methods are a powerful family of iterative solvers for linear systems of equations, which are commonly used for inverse problems due to their intrinsic regularization properties. Moreover, these methods are naturally suited to solve large-scale problems, as they only require matrix-vector products with the system matrix (and its adjoint) to compute approximate solutions, and they display a very fast convergence. Even if this class of methods has been widely researched and studied in the numerical linear algebra community, its use in applied medical physics and applied engineering is still very limited. e.g. in realistic large-scale computed tomography (CT) problems, and more specifically in cone beam CT (CBCT). This work attempts to breach this gap by providing a general framework for the most relevant Krylov subspace methods applied to 3D CT problems, including the most well-known Krylov solvers for non-square systems (CGLS, LSQR, LSMR), possibly in combination with Tikhonov regularization, and methods that incorporate total variation regularization. This is provided within an open source framework: the tomographic iterative GPU-based reconstruction toolbox, with the idea of promoting accessibility and reproducibility of the results for the algorithms presented. Finally, numerical results in synthetic and real-world 3D CT applications (medical CBCT andμ-CT datasets) are provided to showcase and compare the different Krylov subspace methods presented in the paper, as well as their suitability for different kinds of problems.

Stereoscopic calibration for augmented reality visualization in microscopic surgery.

Middle and inner ear procedures target hearing loss, infections, and tumors of the temporal bone and lateral skull base. Despite the advances in surgical techniques, these procedures remain challenging due to limited haptic and visual feedback. Augmented reality (AR) may improve operative safety by allowing the 3D visualization of anatomical structures from preoperative computed tomography (CT) scans on real intraoperative microscope video feed. The purpose of this work was to develop a real-time CT-augmented stereo microscope system using camera calibration and electromagnetic (EM) tracking. A 3D printed and electromagnetically tracked calibration board was used to compute the intrinsic and extrinsic parameters of the surgical stereo microscope. These parameters were used to establish a transformation between the EM tracker coordinate system and the stereo microscope image space such that any tracked 3D point can be projected onto the left and right images of the microscope video stream. This allowed the augmentation of the microscope feed of a 3D printed temporal bone with its corresponding CT-derived virtual model. Finally, the calibration board was also used for evaluating the accuracy of the calibration. We evaluated the accuracy of the system by calculating the registration error (RE) in 2D and 3D in a microsurgical laboratory setting. Our calibration workflow achieved a RE of 0.11 ± 0.06mm in 2D and 0.98 ± 0.13mm in 3D. In addition, we overlaid a 3D CT model on the microscope feed of a 3D resin printed model of a segmented temporal bone. The system exhibited small latency and good registration accuracy. We present the calibration of an electromagnetically tracked surgical stereo microscope for augmented reality visualization. The calibration method achieved accuracy within a range suitable for otologic procedures. The AR process introduces enhanced visualization of the surgical field while allowing depth perception.

Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm.

Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological Society of North America (RSNA) 2019 database, the region of interest (RoI) was segmented by employing Otsu's thresholding method. Then, feature extraction was performed utilizing Tamura features: directionality, contrast, coarseness, and Gradient Local Ternary Pattern (GLTP) descriptors to extract vectors from the segmented RoI regions. The extracted vectors were dimensionally reduced by proposing a modified genetic algorithm, where the infinite feature selection technique was incorporated with the conventional genetic algorithm to further reduce the redundancy within the regularized vectors. The selected optimal vectors were finally fed to the Bi-directional Long Short Term Memory (Bi-LSTM) network to classify intracranial hemorrhage sub-types, such as subdural, intraparenchymal, subarachnoid, epidural, and intraventricular. The experimental investigation demonstrated that the Bi-LSTM based modified genetic algorithm obtained 99.40% sensitivity, 99.80% accuracy, and 99.48% specificity, which are higher compared to the existing machine learning models: Naïve Bayes, Random Forest, Support Vector Machine (SVM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) network.

Accuracy Evaluation Trial of Mixed Reality-Guided Spinal Puncture Technology.

To evaluate the accuracy of mixed reality (MR)-guided visualization technology for spinal puncture (MRsp). MRsp involved the following three steps: 1. Lumbar spine computed tomography (CT) data were obtained to reconstruct virtual 3D images, which were imported into a HoloLens (2nd gen). 2. The patented MR system quickly recognized the spatial orientation and superimposed the virtual image over the real spine in the HoloLens. 3. The operator performed the spinal puncture with structural information provided by the virtual image. A posture fixation cushion was used to keep the subjects' lateral decubitus position consistent. 12 subjects were recruited to verify the setup error and the registration error. The setup error was calculated using the first two CT scans and measuring the displacement of two location markers. The projection points of the upper edge of the L3 spinous process (L3↑), the lower edge of the L3 spinous process (L3↓), and the lower edge of the L4 spinous process (L4↓) in the virtual image were positioned and marked on the skin as the registration markers. A third CT scan was performed to determine the registration error by measuring the displacement between the three registration markers and the corresponding real spinous process edges. The setup errors in the position of the cranial location marker between CT scans along the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) axes of the CT bed measured 0.09 ± 0.06 cm, 0.30 ± 0.28 cm, and 0.22 ± 0.12 cm, respectively, while those of the position of the caudal location marker measured 0.08 ± 0.06 cm, 0.29 ± 0.18 cm, and 0.18 ± 0.10 cm, respectively. The registration errors between the three registration markers and the subject's real L3↑, L3↓, and L4↓ were 0.11 ± 0.09 cm, 0.15 ± 0.13 cm, and 0.13 ± 0.10 cm, respectively, in the SI direction. This MR-guided visualization technology for spinal puncture can accurately and quickly superimpose the reconstructed 3D CT images over a real human spine.

Automated layer identification and segmentation of x‐ray computer tomography imaged PCBs

AbstractThe non‐destructive inspection of Printed Circuit Boards (PCBs) through r‐ray Computer Tomography (CT) is a recently developed method that offers several advantages over traditional inspection techniques. This method is non‐invasive, quick, and offers high resolution, leading to significant improvements in inspection and repair efficiency. Post‐image analysis is an important step in PCB inspection and has important practical significance for automatic positioning and determining the location of faults. Usually, the results of image segmentation are an important basis for PCB defect detection, and accurate segmentation results can effectively improve the efficiency and accuracy of PCB inspection and increase the level of automation. This paper discusses two innovative improvements for the automatic segmentation process: firstly determining which slices of an x‐ray CT 3D PCB stack belong to which layer on a physical PCB in an automatic, generic and completely unsupervised way, which is verified on a 4‐layer PCB; secondly proposing a level set‐based image segmentation algorithm for the problem of gray scale inhomogeneity present in PCB CT. Experimental results on real PCB CT images with high aliasing and artifacts show that the proposed model can obtain better performance than the popular active contour models.

Quantification of damage evolution in stainless steel 316L based on 3D ex-situ X-ray CT and micro-damage model

Quantifying damage to improve the constitutive models that better account for damage prediction is of great importance in metal forming process. In this study, high-resolution micro-focus X-ray Computed tomography (CT) system, together with loading-unloading tensile test, were employed to quantify the 3D interactions of micro-voids evolution in the biocompatible alloy stainless steel 316L (SS316L). The specimen material was tensile deformed and interrupted at several strain intervals prior to failure, X-ray CT was then performed to quantify the damage evolution via the detected changing in size, number, distribution, and volume fraction of micro-voids (VFMV) after each specified strain increment. Thereby the nature of ductile damage, which involved the dynamic evolution mechanism of the nucleation, growth and coalescence of micro-voids, was revealed. Furthermore, an improved micromechanics-based damage (micro-damage) model considering large deformation condition was implemented into finite element (FE) package ABAQUS for the damage prediction. The damage of the specimen under tensile deformation with a wide range of stress states was predicted and a good agreement with experimental data was achieved in terms of damage distribution and evolution. It is concluded that the findings of this study not only reveal the dynamic evolution mechanism of the micro-voids in the damage process intuitively from the experimental point of view, but also provide more accurate material parameters for the theoretical damage modelling, so as to obtain more convincible damage prediction results in the metal forming process.

Single Modality vs. Multimodality: What Works Best for Lung Cancer Screening?

In a clinical context, physicians usually take into account information from more than one data modality when making decisions regarding cancer diagnosis and treatment planning. Artificial intelligence-based methods should mimic the clinical method and take into consideration different sources of data that allow a more comprehensive analysis of the patient and, as a consequence, a more accurate diagnosis. Lung cancer evaluation, in particular, can benefit from this approach since this pathology presents high mortality rates due to its late diagnosis. However, many related works make use of a single data source, namely imaging data. Therefore, this work aims to study the prediction of lung cancer when using more than one data modality. The National Lung Screening Trial dataset that contains data from different sources, specifically, computed tomography (CT) scans and clinical data, was used for the study, the development and comparison of single-modality and multimodality models, that may explore the predictive capability of these two types of data to their full potential. A ResNet18 network was trained to classify 3D CT nodule regions of interest (ROI), whereas a random forest algorithm was used to classify the clinical data, with the former achieving an area under the ROC curve (AUC) of 0.7897 and the latter 0.5241. Regarding the multimodality approaches, three strategies, based on intermediate and late fusion, were implemented to combine the information from the 3D CT nodule ROIs and the clinical data. From those, the best model—a fully connected layer that receives as input a combination of clinical data and deep imaging features, given by a ResNet18 inference model—presented an AUC of 0.8021. Lung cancer is a complex disease, characterized by a multitude of biological and physiological phenomena and influenced by multiple factors. It is thus imperative that the models are capable of responding to that need. The results obtained showed that the combination of different types may have the potential to produce more comprehensive analyses of the disease by the models.

P27: Initial Fitting Study of Left Atrial Assist Device in Heart Failure with Preserved Ejection Fraction: Virtual Assessment with 3-D Computed Tomography Images

Study: We are developing the left atrial assist device (LAAD) with specific features: 1) it is implantable in the mitral plane, and 2) it delivers blood from the left atrium to the left ventricle to mitigate high left atrial pressure of heart failure with preserved ejection fraction (HFpEF). Here we report the first results of a virtual fitting study using 3D heart images of patients with HFpEF. Methods: Virtual fitting using five computed tomography (CT) scan images of adult HFpEF patients was performed. 3D CT images of the chest in five cases of HFpEF (60.4 ± 9.8 years old, 107.6 ± 20.4 kg, two cases were male) were obtained and combined with a virtual LAAD in stereolithographic (STL) file format. Anatomical fit of the pump was evaluated by size and alignment per each case. For each of the five patients, the LAAD 3D-reconstructed image was virtually inserted into the mitral plane of the patients’ heart, and the pump position was finely adjusted. Scales of the pump and the hearts were calibrated to match each other. Results: All five patients’ chests were able to accommodate the LAAD (Fig. 1). With several cross sectional and multi-directional views per each case, feasibility in size and alignment were confirmed. The five patients’ hearts had typical HFpEF features, such as, expanded left atrium and narrow left ventricle, and there was enough space around the LAAD’s inlet in the left atrium, indicating that there was a low risk of wall suction. Also, the LAAD pump does not cause any obstruction of the left ventricular outflow tract. Conclusions: This initial study revealed the suitable range of LAAD implantation in various sizes of the HFpEF population. Further studies including more heart anatomies of HFpEF patients are required and planned.Figure 1. Combined 3-dimentional images of the left atrial assist device and a patient heart with heart failure with preserved ejection fraction. A: top view, B: backside view, C: side view, D: cross sectional view.

CT Images Segmentation Using a Deep Learning-Based Approach for Preoperative Projection of Human Organ Model Using Augmented Reality Technology

Over the last decades, facing the blooming growth of technological progress, interest in digital devices such as computed tomography (CT) as well as magnetic resource imaging which emerged in the 1970s has continued to grow. Such medical data can be invested in numerous visual recognition applications. In this context, these data may be segmented to generate a precise 3D representation of an organ that may be visualized and manipulated to aid surgeons during surgical interventions. Notably, the segmentation process is performed manually through the use of image processing software. Within this framework, multiple outstanding approaches were elaborated. However, the latter proved to be inefficient and required human intervention to opt for the segmentation area appropriately. Over the last few years, automatic methods which are based on deep learning approaches have outperformed the state-of-the-art segmentation approaches due to the use of the relying on Convolutional Neural Networks. In this paper, a segmentation of preoperative patients CT scans based on deep learning architecture was carried out to determine the target organ’s shape. As a result, the segmented 2D CT images are used to generate the patient-specific biomechanical 3D model. To assess the efficiency and reliability of the proposed approach, the 3DIRCADb dataset was invested. The segmentation results were obtained through the implementation of a U-net architecture with good accuracy.

Comparing bone shape models from deep learning processing of magnetic resonance imaging to computed tomography-based models

The purpose of this study was to develop a deep learning approach to automatically segment the scapular bone on magnetic resonance imaging (MRI) images and to compare the accuracy of these three-dimensional (3D) models with that of 3D computed tomography (CT). Fifty-five patients with high-resolution 3D fat-saturated T2 MRI were retrospectively identified. The underlying pathology included rotator cuff tendinopathy and tears, shoulder instability, and impingement. Two experienced musculoskeletal researchers manually segmented the scapular bone. Five cross-validation training and validation splits were generated to independently train two-dimensional (2D) and 3D models using a convolutional neural network approach. Model performance was evaluated using the Dice similarity coefficient (DSC). All models with DSC > 0.70 were ensembled and used for the test set, which consisted of four patients with matching high-resolution MRI and CT scans. Clinically relevant glenoid measurements, including glenoid height, width, and retroversion, were calculated for two of the patients. Paired t-tests and Wilcoxon signed-rank tests were used to compare the DSC of the models. The 2D and 3D models achieved a best DSC of 0.86 and 0.82, respectively, with no significant difference observed. Augmentation of imaging data significantly improved 3D but not 2D model performance. In comparing clinical measurements of 3D MRI and CT, there was a mean difference ranging from 1.29 mm to 3.46 mm and 0.05° to 7.47°. We have presented a fully automatic, deep learning-based strategy for extracting scapular shape from a high-resolution MRI scan. Further developments of this technology have the potential to allow for surgeons to obtain all clinically relevant information from MRI scans and reduce the need for multiple imaging studies for patients with shoulder pathology.

Robust H-K Curvature Map Matching for Patient-to-CT Registration in Neurosurgical Navigation Systems

Image-to-patient registration is a coordinate system matching process between real patients and medical images to actively utilize medical images such as computed tomography (CT) during surgery. This paper mainly deals with a markerless method utilizing scan data of patients and 3D data from CT images. The 3D surface data of the patient are registered to CT data using computer-based optimization methods such as iterative closest point (ICP) algorithms. However, if a proper initial location is not set up, the conventional ICP algorithm has the disadvantages that it takes a long converging time and also suffers from the local minimum problem during the process. We propose an automatic and robust 3D data registration method that can accurately find a proper initial location for the ICP algorithm using curvature matching. The proposed method finds and extracts the matching area for 3D registration by converting 3D CT data and 3D scan data to 2D curvature images and by performing curvature matching between them. Curvature features have characteristics that are robust to translation, rotation, and even some deformation. The proposed image-to-patient registration is implemented with the precise 3D registration of the extracted partial 3D CT data and the patient’s scan data using the ICP algorithm.

A computer-aided tool for automatic volume estimation of hematoma using non-contrast brain CT scans

The computation of hematoma volume is the key parameter for treatment planning of Intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) imaging is routinely used for the diagnosis of ICH. Hence, the development of computer-aided tools for three-dimensional (3D) computed tomography (CT) image analysis is essential to estimate the gross volume of hematoma. We propose a methodology for automatic estimation of the hematoma volume from 3D CT volumes. Our approach integrates two different methods, multiple abstract splitting (MAS) and seeded region growing (SRG) to develop a unified hematoma detection pipeline from pre-processed CT volumes. The proposed methodology was tested on 80 cases. The volume was estimated from the delineated hematoma region, validated against the ground-truth volumes, and compared with those obtained from the conventional ABC/2 approach. We also compared our results with the U-Net model (supervised technique) to show the applicability of the proposed method. The volume calculated from manually segmented hematoma was considered the ground truth. The R 2 correlation coefficient between the volume obtained from the proposed algorithm and the ground truth is 0.86, which is equivalent to the R 2 value resulting from the comparison between the volume calculated by ABC/2 and the ground truth. The experimental results of the proposed unsupervised approach are comparable to the deep neural architecture (U-Net models). The average computation time was 132.76 ± 14 seconds. The proposed methodology provides a fast and automatic estimation of hematoma volume, which is similar to the baseline user-guided ABC/2 approach. Implementation of our method does not demand a high-end computational setup. Thus, recommended in clinical practice for computer-assistive volume estimation of hematoma from 3D CT volumes and can be implemented in a simple computer system.

Multimodal imaging assessment of mitral valve area after transcatheter mitral valve implantation: A three-dimensional echocardiographic and CT scan study

The assessment of mitral bioprosthetic area remains a technical challenge, and its calculation by the continuity equation using transthoracic echocardiography (TTE) may be invalidated in several situations, especially after transcatheter mitral valve implantation (TMVI). This study aims to develop a new technique to measure the mitral valve area after a TMVI using a multimodal three-dimensional echocardiographic and computed tomography (CT) approach. Among the patients hospitalized for TMVI at Bichat Hospital between June 2017 and February 2021, we selected 24 who had both a transesophageal echocardiography (TEE) and a cardiac CT after TMVI and before discharge. We performed two area measurements by planimetry: the geometric orifice area (GOA) corresponding to the area delimited by the edges of the open valve leaflets after alignment in the plane of the proximal coaptation point of the bioprosthesis leaflets; and the smallest valve area (SVA) defined by the smallest measurable area of the open prosthesis, at the free edges of the leaflets. In our cohort, the GOA was 1.73 ± 0.41 cm2 on 3D TEE and 1.75 ± 0.42 cm2 on CT. The SVA was 1.40 ± 0.31 cm2 on 3D TEE and 1.49 ± 0.37 cm2 on CT. There was a good agreement between the two techniques for GOA and SVA (ICC > 0.6, P < 0.01) and a good correlation between the two measurements on 3D TEE as well as on CT (Pearson 0.88 and 0.91 respectively, P < 0.001). Our work suggests a new approach to multimodal assessment of prosthetic mitral valve area after TMVI, using 3D TEE and CT scan. Further studies are needed to complete our results, especially to confirm the feasibility and reproducibility of the measurements. It would also be interesting to correlate these area measurements with the clinical prognosis of patients after TMVI. Agreement between 3D TEE and CT measurements (Fig. 1).