Computed Tomography Image Reconstruction Research Articles

SDGAN: A novel spatial deformable generative adversarial network for low-dose CT image reconstruction

X-ray computed tomography (CT) is one of the most commonly used medical imaging technologies for the evaluation of many diseases. Full-dose imaging for CT ensures the image quality but usually raises concerns about the potential health risks of radiation exposure, especially for cancer patients. The conflict between reducing the radiation exposure and remaining diagnostic performance can be addressed effectively via reconstructing the low-dose CT (L-CT) to the high-quality full-dose CT (F-CT) ones. In this paper, we propose a Spatial Deformable Generative Adversarial Network (SDGAN) to achieve efficient L-CT reconstruction and analysis. SDGAN consists of three modules: the conformer-based generator, the dual-scale discriminator and the spatial deformable fusion module (SDFM). A sequence of consecutive L-CT slices is first fed into the conformer-based generator with the dual-scale discriminator to generate F-CT images. These estimated F-CT images are then fed into SDFM, which fully explores the inter- and intra-slice spatial information, to synthesize the final F-CT images with high quality. Experimental results show that the proposed SDGAN achieves the state-of-the-art performances on commonly used metrics and satisfies the reconstruction needs for clinical standards.

Sinogram domain metal artifact correction of CT via deep learning

PurposeMetal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in image quality can hinder subsequent clinical diagnosis and treatment planning. Beam hardening artifacts are often manifested as severe strip artifacts in the image domain, affecting the overall quality of the reconstructed CT image. In the sinogram domain, metal is typically located in specific areas, and image processing in these regions can preserve image information in other areas, making the model more robust. To address this issue, we propose a region-based correction of beam hardening artifacts in the sinogram domain using deep learning. MethodsWe present a model composed of three modules: (a) a Sinogram Metal Segmentation Network (Seg-Net), (b) a Sinogram Enhancement Network (Sino-Net), and (c) a Fusion Module. The model starts by using the Attention U-Net network to segment the metal regions in the sinogram. The segmented metal regions are then interpolated to obtain a sinogram image free of metal. The Sino-Net is then applied to compensate for the loss of organizational and artifact information in the metal regions. The corrected metal sinogram and the interpolated metal-free sinogram are then used to reconstruct the metal CT and metal-free CT images, respectively. Finally, the Fusion Module combines the two CT images to produce the result. ResultsOur proposed method shows strong performance in both qualitative and quantitative evaluations. The peak signal-to-noise ratio (PSNR) of the CT image before and after correction was 18.22 and 30.32, respectively. The structural similarity index measure (SSIM) improved from 0.75 to 0.99, and the weighted peak signal-to-noise ratio (WPSNR) increased from 21.69 to 35.68. ConclusionsOur proposed method demonstrates the reliability of high-accuracy correction of beam hardening artifacts.

RICT: Rotating image computed tomography with a one-to-one reversible image rotation algorithm.

The Mueller, Siddon and Joseph weighting algorithms are frequently used for projection and back-projection, which are relatively complicated when they are implemented in computer code. This study aims to reduce the actual complexity of the projection and back-projection. First, we neglect the exact shape of the pixel, so that its shadow is a rectangle projecting precisely to a detector bin, which implies that all the pixel weights are exactly 1 for each ray through them, otherwise are exactly 0. Next, a one-to-one reversible image rotation algorithm (RIRA) is proposed to compute the projection and back-projection, where two one-to-one mapping lists namely, U and V, are used to store the coordinates of a rotated pixel and its corresponding new coordinates, respectively. For each 2D projection, the projection is simply the column sum in each orientation according to the lists U and V. For each 2D back-projection, it is simply to arrange the projection to the corresponding column element according to the lists U and V. Thus, there is no need for an interpolation in the projection and back-projection. Last, a rotating image computed tomography (RICT) based on RIRA is proposed to reconstruct the image. Experiments show the RICT reconstructs a good image that is close to the result of filtered back-projection (FBP) method according to the RMSE, PSNR and MSSIM values. What's more, our weight, projection and back-projection are much easier to be implemented in computer code than the FBP method. This study demonstrates that the RIRA method has potential to be used to simplify many computed tomography image reconstruction algorithms.

Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.

Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension. In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity. Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s. To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.

Convergence Results for Primal-Dual Algorithms in the Presence of Adjoint Mismatch

Most optimization problems arising in imaging science involve high-dimensional linear operators and their adjoints. In the implementations of these operators, changes may be introduced for various practical considerations (e.g., memory limitation, computational cost, convergence speed), leading to an adjoint mismatch. This occurs for the X-ray tomographic inverse problems found in computed tomography (CT), where a surrogate operator often replaces the adjoint of the measurement operator (called the projector). The resulting adjoint mismatch can jeopardize the convergence properties of iterative schemes used for image recovery. In this paper, we study the theoretical behavior of a panel of primal-dual proximal algorithms, which rely on forward-backward-(forward) splitting schemes when an adjoint mismatch occurs. We analyze these algorithms by focusing on the resolution of possibly nonsmooth convex penalized minimization problems in an infinite-dimensional setting. Using tools from fixed point theory, we show that they can solve monotone inclusions beyond minimization problems. Such findings indicate that these algorithms can be seen as a generalization of classical primal-dual formulations. The applicability of our findings is also demonstrated through two numerical experiments in the context of CT image reconstruction.

Prompt gamma ray detection and imaging for boron neutron capture therapy using CdTe detector and novel detector shield - Monte Carlo study.

For boron neutron capture therapy (BNCT), the improvements in patient dosimetry will require information about the spatial variation of 10 B concentration in the tumor and critical organs. A non-invasive approach, based on the detection of prompt gamma (PG) rays from the BNC reaction, may be well-suited to obtain such information. The detectability of the BNC PG rays has been shown experimentally utilizing energy-resolving cadmium telluride (CdTe) detectors. However, the feasibility of this approach under the clinically relevant conditions of BNCT is currently unknown. The present work aimed to investigate the aforementioned feasibility by performing Monte Carlo (MC) simulations under the phantom irradiation geometry relevant to accelerator-based BNCT (a-BNCT). Especially, this investigation focused on demonstrating the enhanced detection of the BNC PG rays using a novel neutron shield for CdTe detectors. Upon demonstrating the efficacy of the proposed detector shield, the BNC PG ray-based quantitative imaging of clinically relevant concentrations of 10 B was also demonstrated. The Geant4 MC simulation toolkit was used to model the phantom irradiation by an epithermal neutron beam as well as the detection of the BNC PG rays from the phantom by CdTe detectors with and without the proposed gadolinium (Gd)-based detector shield. It was also used to model the BNC PG ray-based quantitative imaging of 10 B concentrations under a-BNCT scenarios. Each model included a 20cm-diameter/24cm-height cylindrical PMMA phantom containing 10 B inserts at various concentrations. Arrays of CdTe crystals of 5×5×1 mm3 each (up to 120 in the case of a ring detector) were modeled for acquiring the BNC PG ray signals and quantitative imaging. According to the MC simulations, thermalized neutrons from the phantom were found to reach the CdTe detector and captured by Cd and Te, resulting in the gamma ray background noise that directly interfered with the BNC PG ray signal. The proposed Gd-based detector shield was found to be highly effective in shielding thermal neutrons from the phantom, thereby reducing the unwanted gamma ray background noise. Owing to this shield, the detection of as low as seven parts-per-million (ppm) of 10 B within the phantom of clinically relevant size was possible using 20 billion incident neutron histories. Furthermore, quantitative imaging of 10 B distributed at low concentration (down to 50ppm) within the phantom was demonstrated using computed tomography (CT) simulations with 16 billion incident neutron histories per angular projection. The 10 B detection limit (7.5ppm) was also estimated using the reconstructed CT image. Both 10 B detection limits determined from this investigation are deemed clinically relevant for BNCT. The proposed Gd-based detector shield played an essential role for achieving the currently reported 10 B detection limits. Overall, the present MC simulation work demonstrated highly sensitive BNC PG ray detection and imaging under a-BNCT scenarios using CdTe detectors coupled with a novel detector shield.

X-ray energy spectrum estimation based on a virtual computed tomography system

This paper presents a method for estimating the x-ray energy spectrum for computed tomography (CT) in the diagnostic energy range from the reconstructed CT image itself. To this end, a virtual CT system was developed, and datasets, including CT images for the Gammex phantom labeled by the corresponding energy spectra, were generated. Using these datasets, an artificial neural network (ANN) model was trained to reproduce the energy spectrum from the CT values in the Gammex inserts. In the actual application, an aluminum-based bow-tie filter was used in the virtual CT system, and an ANN model with a bow-tie filter was also developed. Both ANN models without/with a bow-tie filter can estimate the x-ray spectrum within the agreement, which is defined as one minus the absolute error, of more than 80% on average. The agreement increases as the tube voltage increases. The estimation was occasionally inaccurate when the amount of noise on the CT image was considerable. Image quality with a signal-to-noise ratio of more than 10 for the basis material of the Gammex phantom was required to predict the spectrum accurately. Based on the experimental data acquired from Activion16 (Canon Medical System, Japan), the ANN model with a bow-tie filter produced a reasonable energy spectrum by simultaneous optimization of the shape of the bow-tie filter. The present method requires a CT image for the Gammex phantom only, and no special setup, thus it is expected to be readily applied in clinical applications, such as beam hardening reduction, CT dose management, and material decomposition, all of which require exact information on the x-ray energy spectrum.

Total-body low-dose CT image denoising using a prior knowledge transfer technique with a contrastive regularization mechanism.

Reducing the radiation exposure experienced by patients in total-body computed tomography (CT) imaging has attracted extensive attention in the medical imaging community. A low radiation dose may result in increased noise and artifacts that greatly affect the subsequent clinical diagnosis. To obtain high-quality total-body low-dose CT (LDCT) images, previous deep learning-based research works developed various network architectures. However, most of these methods only employ normal-dose CT (NDCT) images as ground truths to guide the training process of the constructed denoising network. As a result of this simple restriction, the reconstructed images tend to lose favorable image details and easily generate oversmoothed textures. This study explores how to better utilize the information contained in the feature spaces of NDCT images to guide the LDCT image reconstruction process and achieve high-qualityresults. We propose a novel intratask knowledge transfer (KT) method that leverages the knowledge distilled from NDCT images as an auxiliary component of the LDCT image reconstruction process. Our proposed architecture is named the teacher-student consistency network (TSC-Net), which consists of teacher and student networks with identical architectures. By employing the designed KT loss, the student network is encouraged to emulate the teacher network in the representation space and gain robust prior content. In addition, to further exploit the information contained in CT scans, a contrastive regularization mechanism (CRM) built upon contrastive learning is introduced. The CRM aims to minimize and maximize the L2 distances from the predicted CT images to the NDCT samples and to the LDCT samples in the latent space, respectively. Moreover, based on attention and the deformable convolution approach, we design a dynamic enhancement module (DEM) to improve the network capability to transform input informationflows. By conducting ablation studies, we prove the effectiveness of the proposed KT loss, CRM, and DEM. Extensive experimental results demonstrate that the TSC-Net outperforms the state-of-the-art methods in both quantitative and qualitative evaluations. Additionally, the excellent results obtained for clinical readings also prove that our proposed method can reconstruct high-quality CT images for clinicalapplications. Based on the experimental results and clinical readings, the TSC-Net has better performance than other approaches. In our future work, we may explore the reconstruction of LDCT images by fusing the positron emission tomography (PET) and CT modalities to further improve the visual quality of the reconstructed CTimages.

Thoracic cavity remodeling and pulmonary function change after chest wall resection.

Chest wall resection (CWR) is an essential procedure for treating malignancies and infectious conditions of the chest wall. However, there are few studies on the pulmonary function and changes in thoracic cavity volume (TCV) related to CWR. This study aims to investigate the effects of CWR on long-term changes in TCV and pulmonary function. Data of patients who underwent CWR between 2001 and 2021 were retrospectively reviewed. Patients who underwent single rib or lung resection rather than wedge resection were excluded. TCV (liter) was defined as the sum of the right and left TCVs (RCV and LCV) and was measured using computed tomography image reconstruction software. Changes in pulmonary function and TCV 1 year postoperatively were analyzed. A total of 45 patients were included. The number of resected ribs was 2 in 16 (35.6%) and ≥3 in 29 (64.4%) patients. Thirty patients underwent reconstruction. Long-term post-CWR decreased in forced vital capacity (FVC) (-7.9%, P=0.004) and forced expiratory volume in 1 second (FEV1) (-7.0%, P=0.002) were significant. There was no significant decrease in FEV1/FVC ratio (-3.0%, P=0.06), diffusing capacity of the lung for carbon monoxide (DLCO) (-5.9%, P=0.18) and TCV (-3.1%, P=0.10). There was no correlation between changes in TCV and decreases in FVC (r=0.12, P=0.56) or FEV1 (r=0.15, P=0.45). After right-side CWR (n=27), RCV (-7.8%, P=0.01) decreased significantly, whereas LCV (+2.1%, P=0.58) did not. The left-side CWR exhibited an identical pattern. (LCV: -8.5%, P=0.004; RCV: +1.3%, P=0.85). In the ≥3 rib-resection group, FVC (-9.5%, P=0.02), FEV1 (-7.9%, P=0.02) and TCV (-6.4%, P=0.04) decreased significantly. No significant changes were noted in the 2 rib-resection group. There were no significant differences in the changes in pulmonary function nor TCV between the reconstruction and no-reconstruction groups. The long-term decrease in pulmonary function after CWR was significant, especially after ≥3-rib resection.

Sialography: a pictorial review.

Non-tumour inflammatory and obstructive salivary gland pathologies such as sialadenitis, sialolithiasis, sialadenosis, ductal strictures, etc. require precise radiological evaluation and mapping of salivary gland ductal system for better treatment outcome. Conventional sialography is considered as a useful and reliable technique in evaluation of salivary glands especially intrinsic and acquired abnormalities involving the ductal system and is useful for detection of non-radiopaque sialoliths which are invisible on routine plain radiographs. Primarily sialography is used as a diagnostic tool, additionally it plays an important therapeutic role as salivary gland lavage in cases of recurrent salivary gland infections and in obstructive salivary gland disorders by helping in clearance of mucous plugs or small sialoliths within the ducts. Recently, diagnostic performance of computed tomography (CT) sialography is being explored and has been reported to have high sensitivity in detection of small sialoliths and allows differentiation of sialoliths from other calcifications in glandular ductal system. Multiplanar three dimensional (3D) reconstructed CT images have been reported to play a key role in determination of anatomical location or extent of salivary gland disease without superimposition or distortion of structures. This review aims to discuss the disease specific applications of sialography and CT Sialography in particular for visualization of salivary gland disorders.

Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging.

Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction.

Stature estimation by semi-automatic measurements of 3D CT images of the femur

Stature estimation is one of the most basic and important methods of personal identification. The long bones of the limbs provide the most accurate stature estimation, with the femur being one of the most useful. In all the previously reported methods of stature estimation using computed tomography (CT) images of the femur, laborious manual measurement was necessary. A semi-automatic bone measuring method can simplify this process, so we firstly reported a stature estimation process using semi-automatic bone measurement software equipped with artificial intelligence. Multiple measurements of femurs of adult Japanese cadavers were performed using automatic three-dimensional reconstructed CT images of femurs. After manually setting four points on the femur, an automatic measurement was acquired. The relationships between stature and five femoral measurements, with acceptable intraobserver and interobserver errors, were analyzed with single regression analysis using the standard error of the estimate (SEE) and the coefficient of determination (R2). The maximum length of the femur (MLF) provided the lowest SEE and the highest R2; the SEE and R2 in all cadavers, males and females, respectively, were 3.913 cm (R2 = 0.842), 3.664 cm (R2 = 0.705), and 3.456 cm (R2 = 0.686) for MLF on the right femur, and 3.837 cm (R2 = 0.848), 3.667 cm (R2 = 0.705), and 3.384 cm (R2 = 0.699) for MLF on the left femur. These results were non-inferior to those of previous reports regarding stature estimation using the MLF. Stature estimation with this simple and time-saving method would be useful in forensic medical practice.

Noise Characteristics Modeled Unsupervised Network for Robust CT Image Reconstruction.

Deep learning (DL)-based methods show great potential in computed tomography (CT) imaging field. The DL-based reconstruction methods are usually evaluated on the training and testing datasets which are obtained from the same distribution, i.e., the same CT scan protocol (i.e., the region setting, kVp, mAs, etc.). In this work, we focus on analyzing the robustness of the DL-based methods against protocol-specific distribution shifts (i.e., the training and testing datasets are from different region settings, different kVp settings, or different mAs settings, respectively). The results show that the DL-based reconstruction methods are sensitive to the protocol-specific perturbations which can be attributed to the noise distribution shift between the training and testing datasets. Based on these findings, we presented a low-dose CT reconstruction method using an unsupervised strategy with the consideration of noise distribution to address the issue of protocol-specific perturbations. Specifically, unpaired sinogram data is enrolled into the network training, which represents unique information for specific imaging protocol, and a Gaussian mixture model (GMM) is introduced to characterize the noise distribution in CT images. It can be termed as GMM based unsupervised CT reconstruction network (GMM-unNet) method. Moreover, an expectation-maximization algorithm is designed to optimize the presented GMM-unNet method. Extensive experiments are performed on three datasets from different scan protocols, which demonstrate that the presented GMM-unNet method outperforms the competing methods both qualitatively and quantitatively.

Influence of Lingual Tonsillar Volume in Patients with Obstructive Sleep Apnea.

This study aimed to evaluate the influence of lingual tonsil (LT) volume measured using a three-dimensional (3D) reconstruction volume rendering program on clinical parameters and polysomnography (PSG) results. A total of 100 patients who underwent PSG, computed tomography (CT), and allergy test from April 2016 to April 2020 were randomly selected. LT volume was measured using an imaging software program that enables 3D reconstruction of CT images. PSG parameters were analyzed by dividing the subjects into two groups according to LT volume (each 50 people). Based on the medial volume of 0.863 cm3, the upper half LT volume group and the lower half LT volume group were analyzed. Clinical factors such as body weight, neck circumference, body mass index (BMI), and age showed no difference between the two groups. Among PSG parameters, supine arousal index and non-rapid eye movement (NREM) arousal index were significantly higher in the upper half LT volume group (p = 0.012, 0.037). However, there was no significant difference in apnea-hypopnea index (AHI) between the upper and lower half LT volume groups (p = 0.749). Arousal snoring index and REM arousal index also showed no difference between the two groups. The prevalence of allergic rhinitis was not different in the two groups. High LT volume is associated with NREM arousal and arousal in the supine position, but it is not related to AHI.

Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study

PurposeThe most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution. MethodsTriple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich’s theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins. ResultsRoot mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20–33, 33–47, 47–58, 58–69, 69–80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image. ConclusionsA new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.

A Novel Spline Algorithm Applied to COVID-19 Computed Tomography Image Reconstruction

In the information age, image processing technologies play a vital role in the field of industrial engineering. In this article, a novel spline scheme called the hierarchical polishing splines algorithm is proposed, and it is applied to the field of computed tomography (CT) image reconstruction of coronavirus disease 2019 (COVID-19). The proposed algorithm defines a set of control lattices, wherein the density of lattice points in these control lattice ranges from coarse to fine in order, and the final applied function is produced by adding the functions derived from every control lattices. In order to demonstrate the performance of the proposed algorithm, some CT images from COVID-19 patients are selected. The experimental results show that the reconstructed COVID-19 CT images by using the proposed algorithm have good quality when compared with some widely used approaches. In addition, this article also applies the proposed algorithm to reconstruct the infected regions derived from COVID-19 CT images, and the results also show that the proposed algorithm is more efficient than others. Besides, the applicability of the proposed algorithm is discussed, wherein the COVID-19 severity is estimated based on the reconstructed COVID-19 CT images, and the applicability analysis shows that the use of the proposed algorithm to reconstruct COVID-19 CT images can help to achieve more accurate severity assessment of COVID-19 in a certain extent.

Dual Image Navigation to Secure Surgical Margins in Thoracoscopic Segmentectomy.

Video-assisted thoracoscopic surgery (VATS) segmentectomy is being increasingly used for the management of non-small cell lung cancer. For non-palpable lesions, surgeons frequently find difficulty in ensuring a sufficient surgical resection margin. The purpose of this study was to evaluate the role of intraoperative dual image navigation in combination with the infrared thoracoscopy with intravenous injection of indocyanine green (IRT-ICG) method and intraoperative computed tomography (CT) in detecting oncological margins. This study involved 34 consecutive patients who underwent both IRT-ICG and intraoperative CT-assisted thoracoscopic segmentectomy between October 2017 and July 2021. The intersegmental line on the visceral pleura was visualized using the IRT-ICG method. The intersegmental line was marked by clipping, and an intraoperative CT scan was performed under bilateral lung ventilation. Intraoperative CT or three-dimensional CT reconstruction images were used by surgeons to confirm the correct anatomic segmental border and to secure a sufficient resection margin. A well-defined intersegmental line was observed in 91.2% of patients. In eight cases, the surgeon needed to make some modifications to the resection line to secure a sufficient surgical margin. The mean surgical margin assessed on gross examination by the pathologist was 23.4±9.0 mm. Complete resection was achieved in all patients using this approach. Intraoperative dual image navigation combined with the IRT-ICG method and intraoperative CT scan enables surgeons to perform definitive VATS segmentectomy for non-palpable lesions.

Extension of the open-source TIGRE toolbox for proton imaging

Proton irradiation is a well-established method to treat deep-seated tumors in radio oncology. Usually, an X-ray computed tomography (CT) scan is used for treatment planning. Since proton therapy is based on the precise knowledge of the stopping power describing the energy loss of protons in the patient tissues, the Hounsfield units of the planning CT have to be converted. This conversion introduces range errors in the treatment plan, which could be reduced, if the stopping power values were extracted directly from an image obtained using protons instead of X-rays. Since protons are affected by multiple Coulomb scattering, reconstruction of the 3D stopping power map results in limited image quality if the curved proton path is not considered. This work presents a substantial code extension of the open-source toolbox TIGRE for proton CT (pCT) image reconstruction based on proton radiographs including a curved proton path estimate. The code extension and the reconstruction algorithms are GPU-based, allowing to achieve reconstruction results within minutes. The performance of the pCT code extension was tested with Monte Carlo simulated data using three phantoms (Catphan® high resolution and sensitometry modules and a CIRS patient phantom). In the simulations, ideal and non-ideal conditions for a pCT setup were assumed. The obtained mean absolute percentage error was found to be below 1% and up to 8 lp/cm could be resolved using an idealized setup. These findings demonstrate that the presented code extension to the TIGRE toolbox offers the possibility for other research groups to use a fast and accurate open-source pCT reconstruction.

Locating and Imaging Fiber Breaks in CFRP Using Guided Wave Tomography and Eddy Current Testing.

In this paper, guided Lamb wave tomography and eddy current testing (ECT) techniques were combined to locate and evaluate fiber breaks in carbon-fiber-reinforced plastic (CFRP) structures. Guided wave testing (GWT) and computed tomography (CT) imaging were employed to quickly locate fiber breaks in the CFRP plate. From B-scans performed along two different fiber orientations (0 and 90 degrees), parallel-beam projections of different features were extracted from the guided wave signals, using signal-processing techniques (such as wavelet and Hilbert transforms) and statistical functions (such as skewness and kurtosis). The parallel-beam projections of each individual feature were used as input in computed tomography imaging reconstruction to approximately estimate the location of fiber breaks. From the obtained reconstructed images, image-fusion techniques were applied to get complementary information from multiple source images into one single image. After locating the fiber breaks, C-scans were performed in the vicinity of the damage, using an ECT probe with double excitation configuration to evaluate the condition of the fiber break.

Development and Validation of a Mobile Application for Measuring Tibial Torsion.

Tibial torsion lacks a single and reliable method for its measurement. While physical examination, computed tomography (CT), and EOS imaging are used complementarily, three-dimensional (3D) CT is the most widely used method for intuitive documentation and visualization. However, concern regarding the associated radiation hazard limits its use in the evaluation of pediatric patients. Moreover, EOS machines are too expensive and too large to be placed in every clinic requiring the measurement of tibial torsion. Therefore, a new method for 3D reconstruction is needed. In the present study, we tested the validity and reliability of a novel reconstruction tool for the lower leg. A statistical shape model and Laplacian constraint were adopted for the development of a new reconstruction tool for measuring tibial torsion. Tibial torsion measurements based on a 3D reconstruction application and CT images for 36 patients were evaluated for intraobserver and interobserver reliability. Tibial torsion measurements for 75 patients were compared for validation. A 3D reconstruction system for the lower leg was developed as a mobile application and was installed in a portable device for easy access in the clinical setting. In terms of interobserver reliability, the intraclass correlation coefficient among 3 clinicians was 0.896 (95% confidence interval [CI], 0.828 to 0.941). The correlation coefficient between tibial torsion measured with use of 3D CT and that measured with the mobile application was 0.865 (p < 0.001). The mobile application showed excellent reliability and validity for measuring tibial torsion. Concurrent utilization with mobile application for the femur allows visualization of the rotational profile of the leg without the need for CT or EOS. Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.