CT Volume Research Articles

Visualization and comparison of CT dose distribution between axial and helical acquisitions.

It is challenging to assess the accuracy of volume CT Dose Index (CTDIvol ) when the axial scan modes corresponding to a helical scan protocol are not available. An alternative approach was proposed to directly measure using helical acquisitions and relatively small differences (<20%) from CTDIvol were observed. To visually demonstrate the 3D dose distribution for both axial and helical CT acquisitions and quantitively compare and CTDIvol . 3D dose distribution within the standard CTDI phantoms (16 and 32cm diameter) from a single CT projection, Dp (x,y,z) was first generated using Monte Carlo simulation (GEANT4) with 9×108 photons per combination of tube voltage (80-140kV), collimation width (1-8cm), and z-axis location of the central ray of the x-ray beam, with a spatial resolution of 1 mm3 . These dose distributions from one single projection were analytically ensembled to simulate 3D dose volumes DA (x,y,z) and DH (x,y,z) for axial and helical scans, respectively, with different helical pitches (0.3-2) and scan lengths (100-150mm). 2D planar dose distributions were obtained by integrating the inside 100mm of the dose volumes. CTDIvol and were calculated using the planar dose data at corresponding pencil chamber locations and the percentage differences (PD) were reported. High spatial resolution 3D CT dose volumes were generated and visualized. PDs between and CTDIvol had strong dependency on scan length and peripheral chamber locations, with subtle dependency on collimation width and pitch. PDs were mostly within the range of±3% for a scan length of 150mm with four peripheral chamber locations. With a scan length covering the entire phantom length, directly measured from helical scans can serve as an alternative to CTDIvol only if all four peripheral locations were measured.

Deep learning-based automated COVID-19 classification from computed tomography images

ABSTRACT This paper introduces a lightweight Convolutional Neural Networks (CNN) method for image classification in COVID-19 diagnosis. The proposed approach emphasizes simplicity while achieving high performance, and it leverages a meticulously annotated database. The CNN model consists of four convolutional layers, followed by flattening and two dense layers. The methodology focuses on classifying 2D slices of Computed Tomography (CT) scans. To enhance accuracy, the slices undergo anatomy-relevant masking and the removal of non-representative slices from the CT volume. This is achieved by cropping a fixed-sized rectangular area to capture the relevant region of interest and using a threshold based on bright pixels in binarized slices. The proposed methodology demonstrates improved quantitative results in slice classification by employing slice processing techniques. Additionally, augmentation techniques such as class weight balancing, slice flipping, and a learning rate scheduler are applied to diagnose at the slice level. For patient-level diagnosis, a majority voting method is employed by considering the slices of each CT scan. The proposed method surpasses the baseline approach and other alternatives in terms of macro F1 score, both on the validation set and a test partition containing previously unseen images from the rigorously annotated dataset.

Quantitative analysis of lung function and airway remodeling using ventilation/perfusion single photon emission tomography/computed tomography and HRCT in patients with chronic obstructive pulmonary disease and asthma.

To investigate the role of V/P SPECT/CT and HRCT quantitative parameters in evaluating COPD and asthma disease severity, airway obstructivity-grade, ventilation and perfusion distribution patterns, airway remodeling, and lung parenchymal changes. Fifty-three subjects who underwent V/P SPECT/CT, HRCT, and pulmonary function tests (PFTs) were included. Preserved lung ventilation (PLVF), perfusion function (PLPF), airway obstructivity-grade (OG), proportion of anatomical volume, ventilation and perfusion contribution of each lobe, and V/P distribution patterns were evaluated using V/P SPECT/CT. The quantitative parameters of HRCT included CT bronchial and CT pulmonary function parameters. In addition, the correlation and difference of V/P SPECT/CT-, HRCT-, and PFT-related parameters were compared. There was a statistically significant difference between severe asthma and severe-very severe COPD in CT bronchial parameters, like WA, LA and AA, in the lung segment airways (P < 0.05). CT bronchial parameters, like as WT and WA, were statistically significant (p < 0.05) among asthma patients. The EI of severe-very severe COPD was different from that of the disease severity groups in asthma patients (P < 0.05). The airway obstructivity-grade, PLVF and PLPF differed significantly among the severe-very severe COPD and mild-moderate asthma patients (P < 0.05). And the PLPF was statistically significant among the disease severity groups in asthma and COPD (P < 0.05). OG and PLVF, PLPF, and PFT parameters were significantly correlated, with the FEV1 correlation being the most significant (r = - 0.901, r = 0.915, and r = 0.836, respectively; P < 0.01). There was a strong negative correlation between OG and PLVF (r = - 0.945) and OG and PLPF (r = - 0.853) and a strong positive correlation between PLPF and PLVF (r = 0.872). In addition, OG, PLVF, and PLPF were moderately to strongly correlated with CT lung function parameters (r = - 0.673 to - 0.839; P < 0.01), while lowly to moderately correlated with most CT bronchial parameters (r = - 0.366 to - 0.663, P < 0.01). There were three different V/P distribution patterns, including matched, mismatched, and reverse mismatched patterns. Last, the CT volume overestimated the contribution in the upper lobes and underestimated the lower lobes' contribution to overall function. Quantitative assessment of ventilation and perfusion abnormalities and the degree of pulmonary functional loss by V/P SPECT/CT shows promise as an objective measure to assess the severity of disease and lung function to guide localized treatments. There are differences between HRCT parameters and SPECT/CT parameters among the disease severity groups in asthma and COPD, which may enhance, to some extent, the understanding of complex physiological mechanisms in asthma and COPD.

Accurate volume alignment of arbitrarily oriented tibiae based on a mutual attention network for osteoarthritis analysis.

Damage to cartilage is an important indicator of osteoarthritis progression, but manual extraction of cartilage morphology is time-consuming and prone to error. To address this, we hypothesize that automatic labeling of cartilage can be achieved through the comparison of contrasted and non-contrasted Computer Tomography (CT). However, this is non-trivial as the pre-clinical volumes are at arbitrary starting poses due to the lack of standardized acquisition protocols. Thus, we propose an annotation-free deep learning method, D-net, for accurate and automatic alignment of pre- and post-contrasted cartilage CT volumes. D-Net is based on a novel mutual attention network structure to capture large-range translation and full-range rotation without the need for a prior pose template. CT volumes of mice tibiae are used for validation, with synthetic transformation for training and tested with real pre- and post-contrasted CT volumes. Analysis of Variance (ANOVA) was used to compare the different network structures. Our proposed method, D-net, achieves a Dice coefficient of 0.87, and significantly outperforms other state-of-the-art deep learning models, in the real-world alignment of 50 pairs of pre- and post-contrasted CT volumes when cascaded as a multi-stage network.

Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph.

The goal of this work was to methodically evaluate, optimize, and validate a self-supervised machine learning algorithm capable of real-time automatic registration and fluoroscopic localization of the spine using a single radiograph or fluoroscopic frame. The authors propose a two-dimensional to three-dimensional (2D-3D) registration algorithm that maximizes an image similarity metric between radiographic images to identify the position of a C-arm relative to a 3D volume. This work utilizes digitally reconstructed radiographs (DRRs), which are synthetic radiographic images generated by simulating the x-ray projections as they would pass through a CT volume. To evaluate the algorithm, the authors used cone-beam CT data for 127 patients obtained from an open-source de-identified registry of cervical, thoracic, and lumbar scans. They systematically evaluated and tuned the algorithm, then quantified the convergence rate of the model by simulating C-arm registrations with 80 randomly simulated DRRs for each CT volume. The endpoints of this study were time to convergence, accuracy of convergence for each of the C-arm's degrees of freedom, and overall registration accuracy based on a voxel-by-voxel measurement. A total of 10,160 unique radiographic images were simulated from 127 CT scans. The algorithm successfully converged to the correct solution 82% of the time with an average of 1.96 seconds of computation. The radiographic images for which the algorithm converged to the solution demonstrated 99.9% registration accuracy despite utilizing only single-precision computation for speed. The algorithm was found to be optimized for convergence when the search space was limited to a ± 45° offset in the right anterior oblique/left anterior oblique, cranial/caudal, and receiver rotation angles with the radiographic isocenter contained within 8000 cm3 of the volumetric center of the CT volume. The investigated machine learning algorithm has the potential to aid surgeons in level localization, surgical planning, and intraoperative navigation through a completely automated 2D-3D registration process. Future work will focus on algorithmic optimizations to improve the convergence rate and speed profile.

Centerline depth world for left atrial appendage orifice localization using reinforcement learning.

Left atrial appendage (LAA) occlusion (LAAO) is a minimally invasive implant-based method to prevent cardiovascular stroke in patients with non-valvular atrial fibrillation. Assessing the LAA orifice in preoperative CT angiography plays a crucial role in choosing an appropriate LAAO implant size and a proper C-arm angulation. However, accurate orifice localization is hard because of the high anatomic variation of LAA, and unclear position and orientation of the orifice in available CT views. With the major research focus being on LAA segmentation, the only existing computational method for orifice localization utilized a rule-based decision. Nonetheless, using such a fixed rule may yield high localization error due to the varied anatomy of LAA. While deep learning-based models usually show improvements under such variation, learning an effective localization model is difficult because of the tiny orifice structure compared to the vast search space of CT volume. In this paper, we propose a centerline depth-based reinforcement learning (RL) world for effective orifice localization in a small search space. In our scheme, an RL agent observes the centerline-to-surface distance and navigates through the LAA centerline to localize the orifice. Thus, the search space is significantly reduced facilitating improved localization. The proposed formulation could result in high localization accuracy compared to the expert annotations. Moreover, the localization process takes about 7.3s which is 18 times more efficient than the existing method. Therefore, this can be a useful aid to physicians during the preprocedural planning of LAAO.

An automatic cascaded approach for pancreas segmentation via an unsupervised localization using 3D CT volumes

An automatic cascaded approach for pancreas segmentation via an unsupervised localization using 3D CT volumes

Generation of [formula omitted]CT images from medical CT scans of carbonate rocks using a diffusion-based model

Carbonate rocks are known for their high heterogeneity and textural and compositional complexity. Evaluating their petrophysical properties is thus challenging, especially with limited information. One way to obtain an internal image of such rocks is to scan them with X-ray computed tomography scanners, revealing their internal structures. The problem with this approach is the trade-off between cheap bigger volumes scanned with medical CT, but with lower spatial resolution, and higher spatial resolution with μCT, but higher financial and time costs involved, as well as smaller samples (normally centimeter scales). This work proposes a way to have the volume and cost advantages of the medical CT with the spatial resolution of the μCT using a diffusion probabilistic model to convert medical CT into a μCT. The model was trained with a sample of the Brazilian pre-salt composed by Shrub boundstones and tested with one sample of the same facies and another composed of packstone–grainstones. The results show that the model could replicate both samples well, but worked better when the input sample has the same facies as the training set.

Dual-energy CT of the pancreas: comparison between virtual non-contrast images and true non-contrast images in the detection of pancreatic lesion.

To evaluate the image quality and diagnostic performance for pancreatic lesion between true non-contrast (TNC) and virtual non-contrast (VNC) images obtained from the dual-energy computed tomography (DECT). One hundred six patients with pancreatic mass underwent contrast-enhanced DECT examinations were retrospectively included in this study. VNC images of the abdomen were generated from late arterial (aVNC) and portal (pVNC) phases. For quantitative analysis, the attenuation differences and reproducibility of abdominal organs were compared between TNC and aVNC/pVNC measurements. Qualitatively image quality was assessed by two radiologists using a five-point scale, and they independently compared the detection accuracy of pancreatic lesions between TNC and aVNC/pVNC images. The volume CT dose index (CTDIvol) and size-specific dose estimates (SSDE) were recorded to evaluate the potential dose reduction when using VNC reconstruction to replace the unenhanced phase. A total of 78.38% (765/976) of the attenuation measurement pairs were reproducible between TNC and aVNC images, and 71.0% (693/976) between TNC and pVNC images. In triphasic examinations, a total of 108 pancreatic lesions were found in 106 patients, and no significant difference in detection accuracy was found between TNC and VNC images (p = 0.587-0.957). Qualitatively, image quality was rated diagnostic (score ≥ 3) in all the VNC images. Calculated CTDIvol and SSDE reduction of about 34% could be achieved by omitting the non-contrast phase. VNC images of DECT provide diagnostic image quality and accurate pancreatic lesions detection, which are a promising alternative to unenhanced phase with a substantial reduction of radiation exposure in clinical routine.

The efficacy of low-dose CT with deep learning image reconstruction in the surveillance of incidentally detected pancreatic cystic lesions.

To evaluate the efficacy of low-dose CT (LDCT) with deep learning image reconstruction (DLIR) for the surveillance of pancreatic cystic lesions (PCLs) compared with standard-dose CT (SDCT) with adaptive statistical iterative reconstruction (ASIR-V). The study enrolled 103 patients who underwent pancreatic CT for follow-up of incidentally detected PCLs. The CT protocol included LDCT in the pancreatic phase with 40% ASIR-V, DLIR at medium (DLIR-M) and high levels (DLIR-H), and SDCT in the portal-venous phase with 40% ASIR-V. The overall image quality and conspicuity of PCLs were qualitatively assessed using five-point scales by two radiologists. The size of PCLs, presence of thickened/enhancing walls, enhancing mural nodules, and main pancreatic duct dilatation were reviewed. CT noise and cyst-to-pancreas contrast-to-noise ratio (CNR) were measured. Qualitative and quantitative parameters were analyzed using the chi-squared test, one-way ANOVA, and t-test. Additionally, interobserver agreement was analyzed using the kappa and weighted-kappa statistics. The volume CT dose-indexes in LDCT and SDCT were 3.0 ± 0.6mGy and 8.4 ± 2.9mGy, respectively. LDCT with DLIR-H showed the highest overall image quality, the lowest noise, and the highest CNR. The PCL conspicuity in LDCT with either DLIR-M or DLIR-H was not significantly different from that in SDCT with ASIR-V. Other findings depicting PCLs also revealed no significant differences between LDCT with DLIR and SDCT with ASIR-V. Moreover, the results revealed good or excellent interobserver agreement. LDCT with DLIR has a comparable performance with SDCT for the follow-up of incidentally detected PCLs.

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.

CT volume analysis in living donor liver transplantation: accuracy of three different approaches

ObjectivesThe aim of this retrospective study is to compare and evaluate accuracy of three different approaches of liver volume quantification in living donor transplantations.MethodsThis is a single-center, retrospective study of 60 donors. The total and right lobe liver volumes were analyzed in the portal-venous phase by two independent radiologists who estimated the volumes using manual, semi-automated and automated segmentation methods. The measured right lobe liver volume was compared to the real weight of the graft after back-table examinations.ResultsThe mean estimated overall liver volume was 1164.4 ± 137.0 mL for manual, 1277.4 ± 190.4 mL for semi-automated and 1240.1 ± 108.5 mL for automated segmentation. The mean estimated right lobe volume was 762.0 ± 122.4 mL for manual, 792.9 ± 139.9 mL for semi-automated and 765.4 ± 132.7 mL for automated segmentation. The mean graft weight was 711.2 ± 142.9 g. The manual method better correlated with the graft weight (r = 0.730) in comparison with the semi-automated (r = 0.685) and the automated (r = 0.699) methods (p < 0.001). The mean error ratio in volume estimation by each application was 12.7 ± 16.6% for manual, 17.1 ± 17.3% for semi-automated, 14.7 ± 16.8% for automated methods. There was a statistically significant difference between the mean error ratio of the manual and the semi-automated segmentations (p = 0.017), and no statistically significant difference between the manual and the automated applications (p = 0.199).ConclusionVolume analysis application better correlates with graft weight, but there is no obvious difference between correlation coefficients of all three methods. All three modalities had an error ratio, of which the semi-automated method showed the highest value.Critical relevance statementVolume analysis application was more accurate, but there is no drastic difference between correlation coefficients of all three methods.Graphical abstract

Ultra-low radiation dose and contrast volume CT protocol and TAVI-CT score for TAVI planning and outcome.

To investigate the feasibility of an ultra-low radiation dose and contrast volume protocol using third-generation dual-source (DS) CT for transcatheter aortic valve implantation (TAVI) planning with coronary artery disease (CAD) assessment, coronary artery calcium score (CACS) and aortic valve calcium score (AVCS) quantification and to evaluate their relationship with TAVI outcome. In this retrospective study were selected 203 patients (131 males, 79.4 ± 5.4 years) underwent to TAVI and at 30- and 90-day follow-up. All patients had performed a third-generation 2 × 192-slices DSCT. The CT protocol included a non-contrast and a contrast high-pitch aortic acquisition for TAVI planning and CAD assessment. Semi-qualitative and quantitative image analysis were performed; the performance in CAD assessment was compared with ICA; the relationship between AVCS and CACS and paravalvular aortic regurgitation (PAR) and major cardiovascular events (MACEs) were evaluated. Mean radiation dose were calculated. Non-parametric tests were used. Semi-qualitative image analysis was good. Contrast enhancement >500 Hounsfield unit (HU) and contrast-to-noise ratio <20 were obtained in all segments. The diagnostic accuracy in CAD was 89.0%. AVCS was significantly higher in patients with 30-day severe PAR. AVCS and CACS were higher in patients with 90-day MACE complications, respectively, 1904.5 ± 621.3 HU (p < 0.0001) and 769.2 ± 365.5 HU (p < 0.0230). Mean radiation dose was 2.8 ± 0.3 mSv. A TAVI planning ultra-low radiation dose and contrast volume protocol using third-generation DSCT provides highly diagnostic images with CAD assessment, AVCS and CACS quantification and these latter were related with TAVI outcomes. The proposed protocol using third-generation 2 × 192-slices DSCT allows with an ultra-low radiation dose and contrast volume the TAVI planning and the coronary artery assessment.

ESTABLISHMENT OF LOCAL DIAGNOSTIC REFERENCE LEVELS FOR PAEDIATRIC ABDOMINAL-PELVIS AND CHEST-ABDOMINAL-PELVIS COMPUTED TOMOGRAPHY IN MOROCCO: SUGGESTS THE NEED FOR IMPROVED OPTIMIZATION EFFORTS.

The purpose of the current study was to derive the local diagnostic reference levels (LDRLs) for paediatric abdominal-pelvis (AP) and chest-abdominal-pelvis (CAP) computed tomography in Morocco. The data were gathered retrospectively from two hospitals for 6 months. The LDRLs were defined by volume CT dose index (CTDIvol), dose-length product (DLP) per sequence, DLP per procedure and size-specific dose estimates (SSDE). The SSDE assessment was based on the effective diameters of patients scanned. A total of 630 CT examinations were collected involving 324 AP and 306 CAP scans. The proposed LDRLs for AP, in terms of CTDIvol (mGy), were 6.9, 8.5, 8.5 and 8.5 for < 1, 1 to <5, 5 to <10 and 10 to <15y age groups, respectively. In terms of DLP (mGy.cm) per procedure, they were 436.3, 534.5, 687.9 and 961.7. In terms of SSDE (mGy), thet were 16.73, 16.83, 17.5 and 15.8 for < 1, 1 to <5, 5 to <10 and 10 to <15y, respectively. The corresponding LDRLs for CAP, in terms of CTDIvol (mGy), were 7.3, 7.3, 7.3 and 10.35. In terms of DLP (mGy.cm) per procedure, they were 531, 622.5, 705 and 936. In terms of SSDE (mGy), they were 16.22, 15.05, 14.47 and 15.2, respectively, for the four age groups. The derived dose levels were mostly higher than those found in other studies, which demonstrates the need for dose optimization and paediatric protocol standardization as well as the timeliness of the intent to establish not only local DRLs but national ones in the near future.

Energy-Based MRI Semantic Augmented Segmentation for Unpaired CT Images

The multimodal segmentation of medical images is essential for clinical applications as it allows medical professionals to detect anomalies, monitor treatment effectiveness, and make informed therapeutic decisions. However, existing segmentation methods depend on paired images of modalities, which may not always be available in practical scenarios, thereby limiting their applicability. To address this challenge, current approaches aim to align modalities or generate missing modality images without a ground truth, which can introduce irrelevant texture details. In this paper, we propose the energy-basedsemantic augmented segmentation (ESAS) model, which employs the energy of latent semantic features from a supporting modality to enhance the segmentation performance on unpaired query modality data. The proposed ESAS model is a lightweight and efficient framework suitable for most unpaired multimodal image-learning tasks. We demonstrate the effectiveness of our ESAS model on the MM-WHS 2017 challenge dataset, where it significantly improved Dice accuracy for cardiac segmentation on CT volumes. Our results highlight the potential of the proposed ESAS model to enhance patient outcomes in clinical settings by providing a promising approach for unpaired multimodal medical image segmentation tasks.

Low Contrast Volume Protocol in Routine Chest CT Amid the Global Contrast Shortage: A Single Institution Experience

To assess the effectiveness of low contrast volume (LCV) chest CT performed with multiple contrast agents on multivendor CT with varying scanning techniques. The study included 361 patients (65 ± 15 years; M: F 173:188) who underwent LCV chest CT on one of the six 64-256 detector-row CT scanners using single-energy (SECT) or dual-energy (DECT) modes. All patients were scanned with either a fixed-LCV (LCVf, n=103) or weight-based LCV (LCVw, n=258) protocol. Two thoracic radiologists independently assessed all LCV CT and patients' prior standard contrast volume (SCV, n=263) chest CT for optimality of contrast enhancement in thoracic vasculature, cardiac chambers, and in pleuro-parenchymal and mediastinal abnormalities. CT attenuations were recorded in the main pulmonary trunk, ascending, and descending thoracic aorta. To assess the interobserver agreement, pulmonary arterial enhancement was divided into two groups: optimal or suboptimal. There was no significant difference among patients' BMI (p=0.883) in the three groups. DECT had a significantly higher aortic arterial enhancement (250 ± 99HU vs 228 ± 76 HU for SECT, p < 0.001). Optimal enhancement was present in 558 of 624 chest CT (89.4%), whereas 66 of 624 chest CT with suboptimal enhancement was noted in 48 of 258 LCVw (18.6%) and 14 of 103 LCVf (13.6%). Most patients with suboptimal enhancement with LCVw injection protocol were overweight/obese (30/48; 62.5%), (p < 0.001). LCV chest CT can be performed across complex multivendor, multicontrast media, multiscanner, and multiprotocol CT practices. However, LCV chest CT examinations can result in suboptimal contrast enhancement in patients with larger body habitus.

GVirtualXray (gVXR): Simulating X-ray radiographs and CT volumes of anthropomorphic phantoms

gVirtualXray (gVXR) is an open-source framework that relies on the Beer–Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes. We produced four Jupyter Notebooks to compare images simulated with gVXR and ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. Image registration was deployed in two Notebooks to align the simulated image on the corresponding ground truth image. We demonstrated that accurate images can be generated in milliseconds with gVirtualXray when scattering can be ignored.

Improving visualization of free fibula flap perforators and reducing radiation dose in dual-energy CT angiography.

The precise assessment of the perforators of the fibula free flap (FFF) is crucial for minimizing procedure-related complications when harvesting the FFF in patients with maxillofacial lesions. This study aims to investigate the utility of virtual noncontrast (VNC) images for radiation dose saving and to determine the optimal energy level of virtual monoenergetic imaging (VMI) reconstructions in dual-energy computed tomography (DECT) for visualization of the perforators of the fibula free flap (FFF). Data from 40 patients with maxillofacial lesions who received lower extremity DECT examinations in the noncontrast and arterial phase were collected in this retrospective, cross-sectional study. To compare VNC images from the arterial phase with true non-contrast images in a DECT protocol (M_0.5-TNC) and to compare VMI images with 0.5 linear images blending from the arterial phase (M_0.5-C), the attenuation, noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality were assessed in different arteries, muscles, and fat tissues. Two readers evaluated the image quality and visualization of the perforators. The dose-length product (DLP) and CT volume dose index (CTDIvol) were used to determine the radiation dose. Objective and subjective analyses showed no significant difference between the M_0.5-TNC and VNC images in the arteries and muscles (P>0.09 to P>0.99), and VNC imaging could reduce 50% of the radiation dose (P<0.001). Compared with those of the M_0.5-C images, the attenuation and CNR of VMI reconstructions at 40 kiloelectron volt (keV) and 60 keV were higher (P<0.001 to P=0.04). Noise was similar at 60 keV (all P>0.99) and increased at 40 keV (all P<0.001), and the SNR in arteries was increased at 60 keV (P<0.001 to P=0.02) in VMI reconstructions compared with those in the M_0.5-C images. The subjective scores in VMI reconstructions at 40 and 60 keV was higher than those in M_0.5-C images (all P<0.001). The image quality at 60 keV was superior to that at 40 keV (P<0.001), and there was no difference in the visualization of the perforators between 40 and 60 keV (P=0.31). VNC imaging is a reliable technique for replacing M_0.5-TNC and provides radiation dose saving. The image quality of the 40-keV and 60-keV VMI reconstructions was higher than that of the M_0.5-C images, and 60 keV provided the best assessment of perforators in the tibia.

A super-voxel-based method for generating surrogate lung ventilation images from CT.

Purpose: This study aimed to develop and evaluate , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield . For the performance evaluation, the voxel- and region-wise differences between and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, and , and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the (0.33 ± 0.14, p < 0.05) and (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for (0.63 ± 0.07) was significantly higher than the corresponding values for the (0.43 ± 0.08, p < 0.05) and (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

EVALUATION OF RADIATION DOSE IN MULTI-SLICE COMPUTED TOMOGRAPHY PROTOCOLS OF HEAD AND NECK REGIONS.

In head and neck computed tomography (CT) imaging, the optimisation of radiation dose is crucial due to the presence of radio-sensitive organs. This study aimed to evaluate the radiation dose in multi-slice CT for head and neck examinations. Volume CT dose index, dose length product and effective dose (E) were assessed for 10 head and neck CT scans performed on 292 adult patients (mean age 49.2±15.9y). The study resulted in median E values of 0.82, 1.62, 2.43, 0.93, 1.70, 0.83, 3.55, 6.25, 2.19 and 5.26mSv, respectively, for sinuses (non-contrast (NC)), sinuses (NC) and contrast-enhanced (CE), petrous bone (PTB)/internal auditory meatus (IAM) (NC+CE), PTB/IAM (NC), orbit (NC+CE), orbit (NC), brain with the orbit (NC), brain CT angiography (CTA) subtraction, neck (NC) and brain/neck (NC). Furthermore, the overall radiation doses of this institution were found to be below the values suggested by similar studies. However, optimisation of the dose is required for brain CTA.