3D Computed Tomography Images Research Articles

LIT-Former: Linking In-Plane and Through-Plane Transformers for Simultaneous CT Image Denoising and Deblurring.

This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perform denoising due to low-dose and deblurring for super-resolution separately. Up to date, little work was done for simultaneous in-plane denoising and through-plane deblurring, which is important to obtain high-quality 3D CT images with lower radiation and faster imaging speed. For this task, a straightforward method is to directly train an end-to-end 3D network. However, it demands much more training data and expensive computational costs. Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks. LIT-Former has two novel designs: efficient multi-head self-attention modules (eMSM) and efficient convolutional feed-forward networks (eCFN). First, eMSM integrates in-plane 2D self-attention and through-plane 1D self-attention to efficiently capture global interactions of 3D self-attention, the core unit of transformer networks. Second, eCFN integrates 2D convolution and 1D convolution to extract local information of 3D convolution in the same fashion. As a result, the proposed LIT-Former synergizes these two sub-tasks, significantly reducing the computational complexity as compared to 3D counterparts and enabling rapid convergence. Extensive experimental results on simulated and clinical datasets demonstrate superior performance over state-of-the-art models. The source code is made available at https://github.com/hao1635/LIT-Former.

In situ 3D X-ray imaging of water distribution in each layer of a membrane electrode assembly of a polymer electrolyte fuel cell.

In situ 3D computed tomography imaging with statistical analysis successfully revealed the water accumulation and drainage characteristics in the stacked gas diffusion layers (GDLs) and membrane electrode assembly (MEA) of a polymer electrolyte fuel cell. Efficient water drainage at the interface between the cathode GDL and MEA was confirmed upon supplying oxygen to the cathode.

Comparison of deep inspiration breath hold and free breathing intensity modulated proton therapy of locally advanced lung cancer

Background and purposeFor locally advanced non-small cell lung cancer (LA-NSCLC), intensity-modulated proton therapy (IMPT) can reduce organ at risk (OAR) doses compared to intensity-modulated radiotherapy (IMRT). Deep inspiration breath hold (DIBH) reduces OAR doses compared to free breathing (FB) in IMRT. In IMPT, differences in dose distributions and robustness between DIBH and FB are unclear. In this study, we compare DIBH to FB in IMPT, and IMPT to IMRT. Materials and methodsFortyone LA-NSCLC patients were prospectively included. 4D computed tomography images (4DCTs) and DIBH CTs were acquired for treatment planning and during weeks 1 and 3 of treatment. A new system for automated robust planning was developed and used to generate a FB and a DIBH IMPT plan for each patient. Plans were compared in terms of dose-volume parameters and normal tissue complication probabilities (NTCPs). Dose recalculations on repeat CTs were used to compare inter-fraction plan robustness. ResultsIn IMPT, DIBH reduced median lungs Dmean from 9.3 Gy(RBE) to 8.0 Gy(RBE) compared to FB, and radiation pneumonitis NTCP from 10.9 % to 9.4 % (p < 0.001). Inter-fraction plan robustness for DIBH and FB was similar. Median NTCPs for radiation pneumonitis and mortality were around 9 percentage points lower with IMPT than IMRT (p < 0.001). These differences were much larger than between FB and DIBH within each modality. ConclusionDIBH IMPT resulted in reduced lung dose and radiation pneumonitis NTCP compared to FB IMPT. Inter-fraction robustness was comparable. OAR doses were far lower in IMPT than IMRT.

Split Bregman quantum noise removal algorithm for 3D reconstruction of neutron computed tomography image

The low intensity of the neutron source for neutron computed tomography (CT) results in a long acquisition time for a single projection, which causes the neutron projection data to contain a large amount of quantum noise. Quantum noise will degrade the quality of neutron CT reconstruction images. Therefore, an efficient quantum noise removal algorithm must be used in CT reconstruction. In this paper, an efficient quantum noise removal algorithm for neutron CT 3D image reconstruction is proposed by analysing classical image processing algorithms and quantum image processing algorithms, which employs the maximum likelihood expectation maximization to reconstruct the image and split Bregman algorithm to solve for the total variation (MLEM-SBTV). Experimental results show that MLEM-SBTV performs well in removing quantum noise and reconstructing the detailed structure of images.

3D Digital Modeling of Dental Casts from Their 3D CT Images with Scatter and Beam-Hardening Correction.

Dental 3D modeling plays a pivotal role in digital dentistry, offering precise tools for treatment planning, implant placement, and prosthesis customization. Traditional methods rely on physical plaster casts, which pose challenges in storage, accessibility, and accuracy, fueling interest in digitization using 3D computed tomography (CT) imaging. We introduce a method that can reduce both artifacts simultaneously. To validate the proposed method, we carried out CT scan experiments using plaster dental casts created from dental impressions. After the artifact correction, the CT image quality was greatly improved in terms of image uniformity, contrast-to-noise ratio (CNR), and edge sharpness. We examined the correction effects on the accuracy of the 3D models generated from the CT images. As referenced to the 3D models derived from the optical scan data, the root mean square (RMS) errors were reduced by 8.8~71.7% for three dental casts of different sizes and shapes. Our method offers a solution to challenges posed by artifacts in CT scanning of plaster dental casts, leading to enhanced 3D model accuracy. This advancement holds promise for dental professionals seeking precise digital modeling for diverse applications in dentistry.

Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma

BackgroundAt present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality.MethodsA total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D computed tomography images and structure contours as input. Automatic IMRT treatment planning was achieved by using its generated dose with slight tightening for further plan fine-tuning. Lastly, the plan quality was compared between automatic plans and clinical plans.ResultsThe average time for automatic plan generation was less than 4 min, including fluence maps prediction with a python script and automated plan tuning with a C# script. Compared with clinical plans, automatic plans showed better conformity and homogeneity for planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the dosimetric metrics for most organs at risk (OARs) were ameliorated in the automatic plan, especially Dmax of the brainstem and spinal cord, and Dmean of the left and right parotid glands significantly decreased (P < 0.05).ConclusionWe have successfully implemented an automatic IMRT plan generation method for patients with NPC. This method shows high planning efficiency and comparable or superior plan quality than clinical plans. The qualitative results before and after the plan fine-tuning indicates that further optimization using dose objectives generated by predicted fluence maps is crucial to obtain high-quality automatic plans.

Anatomical studies on the PES region of Zebu cattle (Bos Taurus indicus) with special references to 3D computed tomography imaging technique

The 3D render volume reconstruction CT (3D-RVCT) produced detailed images of the PES region, determining its relationships with the surrounding structures. Despite extensive research in veterinary studies on the PES through gross anatomy and CT, there is a lack of studies on the PES of zebu cattle. The study aimed to analyze the PES of Zebu cattle using gross cross-sectional, radiographic, CT, and morphometric methods, with the use of 3D-RVCT to provide anatomical guidance for surgeons and students. The study was performed on sixteen PES regions to provide hard and soft tissues in CT images. Three are five tarsal bones and two large fused (III and IV) metatarsal bones that were completely fused except for their distal extremities, which were divided distally by the intertrochlear notch. The cortical thickness of the metatarsal bone was equal on both sides. The bony septum divided the medullary cavity between the two fused large metatarsal bones in the proximal distal half only and disappeared in the middle part. The reconstruction showed similar sizes in the right and left limbs, confirming the pes bones. The radiographic and CT images could be used as a normal reference for the interpretation of some clinical diseases in the PES. The 3D CT reconstruction of the pes bones was described by various CT oblique dorsal and plantar views. The study focuses on diagnosing PES disorders using CT imaging, improving medical interventions, improving Zebu cattle health outcomes, and empowering students to contribute to veterinary medicine research and advancements.

Evaluation of glenoid morphology and bony Bankart lesion in shoulders with traumatic anterior instability using zero echo time magnetic resonance imaging

BackgroundPreoperative computed tomography (CT) evaluation of bone morphometry aids in determining treatment strategies for shoulder instability. The use of zero echo time (ZTE) sequence in magnetic resonance imaging (MRI), a new bone cortex imaging technique, may help reduce radiation exposure and medical costs. Therefore, this study aimed to evaluate the glenoid morphology and detect the presence of bony Bankart lesion using ZTE MRI in shoulders with anterior instability and compare its diagnostic accuracy with that of CT. MethodsThirty-six patients (36 shoulders) with anterior instability who underwent preoperative CT and MRI examinations between April 2019 and October 2021 were retrospectively analyzed. The percentages of glenoid bone defects on three-dimensional (3D) CT and ZTE images were determined, and the correlation between these percentages was evaluated. The number of cases with bony Bankart lesion on CT and two types of ZTE (3D and CT-like) images was determined, and the diagnostic accuracy of ZTE for detecting bony Bankart lesion was assessed, with CT as the gold standard. Patients with bony Bankart lesion on CT images were divided into two groups based on whether the lesion was detectable on 3D ZTE or CT-like images. The longer diameters of bony Bankart lesion were compared between the groups. ResultsThe median percentage of glenoid bone loss was 12.1% (range, 1.3%–45.9%) and 12.3% (range, 0%–46.6%) on 3D CT and 3D ZTE images, respectively. The Spearman’s rank correlation coefficient was 0.89. Bony Bankart lesion was detected in 18, 13, and eight shoulders of the 36 patients on CT, 3D ZTE, and CT-like images, respectively. The overall diagnostic accuracy of the CT-like and 3D ZTE images for detecting bony Bankart lesion was 86.1% and 72.2%, respectively. A significant difference was observed between the groups with and without bony Bankart lesion on CT-like images in terms of the long diameter of the bone fragments on CT (p < 0.01). ConclusionsZTE MRI demonstrated high reproducibility for the evaluation of glenoid bone defect in shoulders with anterior instability. Although no significant difference in the measurement was observed compared with that on CT, the ability of ZTE MRI to delineate bone Bankart lesion remains limited.

Effect of corrosion pit distribution of rebar on pore, and crack characteristics in concrete

This research paper delves into the impact of corrosion-induced changes, particularly pit distribution, on pore structures and crack propagation in concrete subjected to galvanostatic accelerated corrosion. Two samples with different pit configurations were investigated—one with larger and spread pits, the other with smaller and localized ones. Various characterizations methods were applied including X-ray Computed Tomography (CT), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectrometry (EDS), and Raman spectroscopy. The 3D CT images reveals dynamic changes in pore volumes, pit expansion, emergence of new pores at cement-aggregate interfaces, and distinctive crack formation patterns. Image analysis accurately quantifies corrosion products, highlighting their dependence on pit presence and characteristic. The sample with larger pit volume experiences exclusive initial formation of iron hydroxides, while small pit volume contains both iron oxides and hydroxides simultaneously. Raman spectroscopy shows the dynamic interplay of iron oxides and hydroxides, marking an evolution from oxides to hydroxides within the cement paste. Crack formation analysis reveals distinct patterns in samples with high pit dispersion, including cracks originating from pores near reinforcing bar pits, larger cracks diffusing corrosion products outward, and zipper cracks emerging from distant interconnected pores. Conversely, low pit content sample exhibits a different crack pattern with varying origins and narrower apertures. The temporal evolution of cracks in this sample appears simultaneous, possibly linked to ongoing corrosion processes. Elemental mapping indicates elevated corrosion product volumes within and around cracks, possibly due to water migration into the concrete matrix. These insights deepen our understanding of concrete deterioration mechanisms, guiding strategies to enhance structural integrity and durability in critical infrastructure.

Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism.

This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions.

CNN-based fully automatic mitral valve extraction using CT images and existence probability maps

Objective. Accurate extraction of mitral valve shape from clinical tomographic images acquired in patients has proven useful for planning surgical and interventional mitral valve treatments. However, manual extraction of the mitral valve shape is laborious, and the existing automatic extraction methods have not been sufficiently accurate. In this paper, we propose a fully automated method of extracting mitral valve shape from computed tomography (CT) images for the all phases of the cardiac cycle. Approach. This method extracts the mitral valve shape based on DenseNet using both the original CT image and the existence probability maps of the mitral valve area inferred by U-Net as input. A total of 1585 CT images from 204 patients with various cardiac diseases including mitral regurgitation were collected and manually annotated for mitral valve region. The proposed method was trained and evaluated by 10-fold cross validation using the collected data and was compared with the method without the existence probability maps. Main results. The mean error of shape extraction error in the proposed method is 0.88 mm, which is an improvement of 0.32 mm compared with the method without the existence probability maps. Significance. We present a novel fully automatic mitral valve extraction method from input to output for all phases of 4D CT images. We suggest that the accuracy of mitral valve shape extraction is improved by using existence probability maps.

Cardiac computed tomography angiography for assessment of endothelial insufficiency of left atrial appendage disc-like occluder.

This study was performed to explore the diagnostic value of cardiac computed tomography angiography (CCTA) for endothelial insufficiency (EIS) of a left atrial appendage (LAA) disc-like occluder. Fifty-nine patients with nonvalvular atrial fibrillation who underwent placement of an LAA disc-like occluder (LAmbre; Lifetech Scientific) in our hospital were retrospectively analyzed. Patients who were found to have contrast agent entering the LAA at the 3-month postoperative CCTA examination underwent Hounsfield unit (HU) measurement of the LAA and construction of a three-dimensional(3D) model of the device for preliminary discernment between peri-device leakage (PDL) and EIS. These patients were then further examined by transesophageal echocardiography (TEE) to check for concordance with the computed tomography (CT) findings. According to the CT and TEE results, all patients were divided into the PDL group, total endothelialization group, and EIS group. The endothelial conditions and other implantation-related results were also tracked at the 6-month follow-up. All 59 patients underwent successful implantation of the LAmbre LAA closure device with no severe adverse events during the procedure. Thirty-five patients were found to have contrast agent entering the LAA at the 3-month postoperative CCTA follow-up. Based on the CT HU measurement and the 3D construction analysis results, these 35 patients were divided into the PDL group (19 patients) and the EIS group (16 patients). In the PDL group, the contrast agent infiltrated from the shoulder along the periphery of the occluder on two-dimensional (2D)CT images, and the 3D model showed a gap between the LAA and the device cover. However, the CCTA images of the other 16 patients in the EIS group showed that the contrast agent in the occluder on the 2D CTA images and 3D construction model confirmed the absence of a gap between the LAA and the device cover. TEE confirmed all of the CT results. The 6-month follow-up results showed that 14 of 19 patients in the EIS group achieved total endothelialization, whereas this number in the PDL group was only five of 19 patients. CCTA can replace TEE for examination of the endothelialization status, and patients with EIS have a higher chance of endothelialization than patients with PDL.

Usefulness of an intraoperative flipped monitor in laparoscopic surgery with situs inversus totalis: a case report of laparoscopic-assisted ileocecal resection

BackgroundSitus inversus totalis (SIT) is a rare congenital condition that involves complete transposition (right to left reversal) of the visceral organs. Laparoscopic surgery can be challenging because of the mirror-image anatomy. We describe a surgical innovation in laparoscopic surgery for SIT.Case presentationA 41-year-old man with SIT was diagnosed with an appendiceal tumor and underwent laparoscopic-assisted ileocecal resection. Preoperatively, we evaluated anatomical variations using 3D-computed tomography and simulated mirror images by watching flipped videos of patients with normal anatomy undergoing similar operations. During the operation, port placement and the surgeons’ standing positions were reversed. Additionally, two monitors were placed at the patient’s head, with one monitor showing original images, and the other showing flipped images that looked the same as the normal anatomy. We checked the range of the mobilized region and important anatomical structures by watching the flipped monitor as needed. The patient’s postoperative course was uneventful.ConclusionsDue to the complexities of laparoscopic surgery for SIT, preoperative preparation and surgical innovation are necessary for safe surgery. Several suggestions have been made to understand anatomical anomalies and improve operability; however, surgeons must focus on the mirror-image anatomy throughout the operation. Therefore, the use of intraoperative flipped monitor will be helpful for surgeons in reducing the risk of anatomical misidentification.

End-to-end Prediction of EGFR Mutation Status with Denseformer.

Accurate genotyping of the epidermal growth factor receptor (EGFR) is critical for the treatment planning of lung adenocarcinoma. Currently, clinical identification of EGFR genotyping highly relies on biopsy and sequence testing which is invasive and complicated. Recent advancements in the integration of computed tomography (CT) imagery with deep learning techniques have yielded a non-invasive and straightforward way for identifying EGFR profiles. However, there are still many limitations for further exploration: 1) most of these methods still require physicians to annotate tumor boundaries, which are time-consuming and prone to subjective errors; 2) most of the existing methods are simply borrowed from computer vision field which does not sufficiently exploit the multi-level features for final prediction. To solve these problems, we propose a Denseformer framework to identify EGFR mutation status in a real end-to-end fashion directly from 3D lung CT images. Specifically, we take the 3D whole-lung CT images as the input of the neural network model without manually labeling the lung nodules. This is inspired by the medical report that the mutational status of EGFR is associated not only with the local tumor nodules but also with the microenvironment surrounded by the whole lung. Besides, we design a novel Denseformer network to fully explore the distinctive information across the different level features. The Denseformer is a novel network architecture that combines the advantages of both convolutional neural network (CNN) and Transformer. Denseformer directly learns from the 3D whole-lung CT images, which preserves the spatial location information in the CT images. To further improve the model performance, we designed a combined Transformer module. This module employs the Transformer Encoder to globally integrate the information of different levels and layers and use them as the basis for the final prediction. The proposed model has been tested on a lung adenocarcinoma dataset collected at the Affiliated Hospital of Zunyi Medical University. Extensive experiments demonstrated the proposed method can effectively extract meaningful features from 3D CT images to make accurate predictions. Compared with other state-of-the-art methods, Denseformer achieves the best performance among current methods using deep learning to predict EGFR mutation status based on a single modality of CT images.

Sex estimation from the hyoid bone measurements in an adult Eastern Turkish population using 3D CT images, discriminant function analysis, support vector machines, and artificial neural networks☆

The hyoid bone is one of the bones in the human body that shows sexual dimorphism. The anthropological and anthropometric characteristics that determine sexual dimorphism are influenced by demographic differences. The aim of this study was to investigate the rate of sexual dimorphism of the hyoid bone in the adult Eastern Turkish population from the examination of the 3D computed tomography images of 240 patients, using discriminant function analysis (DFA), support vector machines (SVM), and artificial neural networks (ANN). These evaluations were based on eight hyoid measurements that have been frequently used in previous CT studies. The results showed that all eight measurements were higher in males than in females (p = 0.000). It was determined that sex could be estimated accurately at up to 93.3 % using DFA, 93.8 % using SVM and 95.4 % using ANN. The maximum accuracy rate achieved to 94.2 % in males using SVM, and 95.8 % in females using ANN.These high rates of sexual dimorphism found using DFA, SVM, and ANN in this study indicate that characteristics of the hyoid bone can be utilized to determine sex in the Eastern Turkish population.

Accuracy and reproducibility of two-dimensional computed tomography-based positioning of femoral component rotational alignment in preoperative planning for total knee arthroplasty

BackgroundPoor rotation of the femoral component in total knee arthroplasty (TKA) can result in various postoperative complications, underscoring the critical importance of preoperative planning.PurposeTo improve the accuracy of femoral component positioning during TKA, this study compared the accuracy and repeatability of different two-dimensional (2D) computed tomography (CT) measurement methods for measuring the posterior condylar angle (PCA) in preoperative TKA planning.MethodsA retrospective analysis was conducted on 75 patients (150 knees) who underwent bilateral lower extremity computed tomography angiography (CTA) at Fuyang People's Hospital from January 2021 to July 2021. Three different methods were used to measure the PCA based on 2D CT images (axial CT slices) and three-dimensional(3D) models (femoral models reconstructed from CT data) in this study. Method 1: Single-plane 2D CT measurement, measuring PCA in the most obvious single-plane CT slice of the surgical transepicondylar axis (sTEA); Method 2: multi-plane 2D CT measurement, identifying and locating anatomical landmarks in multiple 2D CT slices and measuring PCA; Method 3: 3D model measurement, measuring PCA in the reconstructed femur 3D model. Compare the differences in PCA measurements between the three measurement methods. A positive PCA measurement was recorded when the sTEA was externally rotated relative to the posterior condylar line (PCL). Any difference exceeding 3° between the PCA measurement in the 2D CT and the PCA reference value in the 3D model was classified as an outlier. The intraclass correlation coefficient (ICC) and Bland–Altman method were utilized to assess the intra- and inter-observer reproducibility of the three measurement methods.ResultsThe PCA measurement in the single-plane 2D CT was 1.91 ± 1.94°, with a measurement error of − 1.22 ± 1.32° and 12.7% of outlier values. In the multi-plane 2D CT, the PCA measurement was 2.96 ± 1.68°, with a measurement error of -0.15 ± 0.91° and 6.0% of outlier values. The PCA measurement in the 3D model was 3.12 ± 1.69°. The PCA measurement in single-plane 2D CT was notably smaller than that in multi-plane 2D CT and 3D models, with no significant difference between the latter two. The multi-plane 2D CT showed significantly lower measurement error and outlier values than the single-plane 2D CT. All three PCA measurement methods exhibited high reproducibility (ICC: 0.93 ~ 0.97).ConclusionsUsing of multi-plane 2D CT for measuring PCA in preoperative planning of TKA has high reproducibility and accuracy, with fewer outlier values. We recommend preoperative measurement of PCA using muti-plane 2D CT to improve the accuracy of positioning the femoral component rotational alignment during surgery.

3D image scanning of gravel soil using in-situ X-ray computed tomography

A typical ground investigation for characterizing geotechnical properties of soil requires sampling soils to test in a laboratory. Laboratory X-ray computed tomography (CT) has been used to non-destructively observe soils and characterize their properties using image processing, numerical analysis, or three-dimensional (3D) printing techniques based on scanned images; however, if it becomes possible to scan the soils in the ground, it may enable the characterization without sampling them. In this study, an in-situ X-ray CT scanning system comprising a drilling machine with an integrated CT scanner was developed. A model test was conducted on gravel soil to verify if the equipment can drill and scan the soil underground. Moreover, image processing was performed on acquired 3D CT images to verify the image quality; the particle morphology (particle size and shape characteristics) was compared with the results obtained for projected particles captured in a two-dimensional (2D) manner by a digital camera. The equipment successfully drilled to a target depth of 800 mm, and the soil was scanned at depths of 700, 750, and 800 mm. Image processing results showed a reasonable agreement between the 3D and 2D particle morphology images, and confirmed the feasibility of the in-situ X-ray CT scanning system.

Dosiomics and radiomics improve the prediction of post-radiotherapy neutrophil-lymphocyte ratio in locally advanced non-small cell lung cancer.

To develop and validate a dosiomics and radiomics model based on three-dimensional (3D) dose distribution map and computed tomography (CT) images for the prediction of the post-radiotherapy (post-RT) neutrophil-to-lymphocyte ratio (NLR). This work retrospectively collected 242 locally advanced non-small cell lung cancer (LA-NSCLC) patients who were treated with definitive radiotherapy from 2012 to 2016. The NLR collected one month after the completion of RT was defined as the primary outcome. Clinical characteristics and two-dimensional dosimetric factors calculated from the dose-volume histogram (DVH) were included. A total of 4165 dosiomics and radiomics features were extracted from the 3D dose maps and CT images within five different anatomical regions of interest (ROIs), respectively. Then, a three-step feature selection method was proposed to progressively filter features from coarse to fine: (i) model-based ranking according to individual feature's performance, (ii) maximum relevance and minimum redundancy (mRMR), (iii) select from model based on feature importance calculated with an ensemble of several decision trees. The selected feature subsets were utilized to develop the prediction model with GBDT. All patients were divided into a development set and an independent testing set (2:1). Five-fold cross-validation was applied to the development set for both feature selection and model training procedure. Finally, a fusion model combining dosiomics, radiomics and clinical features was constructed to further improve the prediction results. The area under receiver operating characteristic curve (ROC) were used to evaluate the modelperformance. The clinical-based and DVH-based models showed limited predictive power with AUCs of 0.632 (95% CI: 0.490-0.773) and 0.634 (95% CI: 0.497-0.771), respectively, in the independent testing set. The 9 feature-based dosiomics and 3 feature-based radiomics models showed improved AUCs of 0.738 (95% CI: 0.628-0.849) and 0.689 (95% CI: 0.566-0.813), respectively. The dosiomics & radiomics & clinical fusion model further improved the model's generalization ability with an AUC of 0.765 (95% CI: 0.656-0.874). Dosiomics and radiomics can benefit the prediction of post-RT NLR of LA-NSCLC patients. This can provide a reference for evaluating radiotherapy-relatedinflammation.

Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease.

To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT). We collected a data set of 650 nodules (316 acute and 334 chronic) from the CT scans of 110 patients with NTM-LD. The data set was divided into training, validation, and test sets in a ratio of 4:1:1. Bounding boxes were used to crop the 2D CT images down to the area of interest. A DCNN model was built using 11 convolutional layers and trained on these images. The performance of the model was evaluated on the hold-out test set and compared with that of 3 radiologists who independently reviewed the images. The DCNN model achieved an area under the receiver operating characteristic curve of 0.806 for differentiating acute and chronic NTM-LD nodules, corresponding to sensitivity, specificity, and accuracy of 76%, 68%, and 72%, respectively. The performance of the model was comparable to that of the 3 radiologists, who had area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of 0.693 to 0.771, 61% to 82%, 59% to 73%, and 60% to 73%, respectively. This study demonstrated the feasibility of using a DCNN model for the classification of the activity of NTM-LD nodules on chest CT. The model performance was comparable to that of radiologists. This approach can potentially and efficiently improve the diagnosis and management of NTM-LD.

Damage Analysis and Quality Control of Carbon-Reinforced Concrete Beams Based on In Situ Computed Tomography Tests

Carbon-reinforced concrete (CRC) is increasingly utilized in construction, due to its unique properties, such as corrosion resistance, high-tensile strength, and durability. Understanding its behavior under different loads is crucial to ensuring its safe and effective use in various construction applications. In this study, three-point bending tests were performed in combination with large-scale in situ computed tomography (CT). This paper presents the related three- and four-dimensional evaluation methods, with emphasis on crack width and quality control. The focus was on large CRC beams, with cross-sectional sizes of up to 80 mm by 160 mm. Such dimensions require extremely high energy during a CT scan. Therefore, a new experimental setup with energies of up to 8 MeV was used in this study. However, such high energies posed new challenges to the analysis methods. Therefore, two methods (digital volume correlation and grayscale profile analysis) for accurate crack width estimation were adapted and applied to the 3D reconstructions. In addition, a photogrammetric stereo image sequence was acquired and analyzed, using digital image correlation to cross-validate the results derived from the 3D crack width estimates. The 3D CT images also played a key role in the quality control measures, including the localization of the carbon-reinforcement and the assessment of porosity within the concrete structure.