Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored ...technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled projection data. However, iterative algorithms take much longer computation time than the analytic algorithms, and images are usually prone to different types of image artifacts that heavily depend on the reconstruction parameters. Interpolation methods have also been utilized to fill the missing data in the sinogram of sparse-view CT thus providing synthetically full data for analytic image reconstruction. In this paper, we introduce a deep-neural-network-enabled sinogram synthesis method for sparse-view CT, and show its outperformance to the existing interpolation methods and also to the iterative image reconstruction approach.
Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse ...optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.
A single-scan dual-energy low-dose cone-beam CT (CBCT) imaging technique that exploits a multi-slit filter is proposed in this paper. The multi-slit filter installed between the x-ray source and the ...scanned object is reciprocated during a scan. The x-ray beams through the slits would generate relatively low-energy x-ray projection data, while the filtered beams would make high-energy projection data. An iterative image reconstruction algorithm that uses an adaptive-steepest-descent method to minimize image total-variation under the constraint of data fidelity was applied to reconstructing the image from the low-energy projection data. Since the high-energy projection data suffer from a substantially high noise level due to the beam filtration, we have developed a new algorithm that exploits the joint sparsity between the low- and high-energy CT images for image reconstruction of the high-energy CT image. The proposed image reconstruction algorithm uses a gradient magnitude image (GMI) of the low-energy CT image by regularizing the difference of GMIs of the low- and high-energy CT images to be minimized. The feasibility of the proposed technique has been demonstrated by the use of various phantoms in the experimental CBCT setup. Furthermore, based on the proposed dual-energy imaging, a material differentiation was performed and its potential utility has been shown. The proposed imaging technique produced promising results for its potential application to a low-dose single-scan dual-energy CBCT.
Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this ...study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient's dental arch from the patient's CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image.
This study was to investigate the new analysis manner of dental hard tissue change using in vivo micro-computed tomography (CT) in rat. Scanning, registration, analyzing, and presenting method to ...track longitudinal in vivo micro-CT data on dental hard tissues were validated in murine models: formative, dentin thickness after direct pulp capping with mineral trioxide aggregate; resorptive, development of apical bone rarefaction in apical periodontitis model. Serial in vivo micro-CT scans were analyzed through rigid-registration, active-contouring, deformable-registration, and motion vector-based quantitative analyses. The rate and direction of hard tissue formation after direct pulp capping was datafied by tracing coordinate shift of fiducial points on pulp chamber outline in formative model. The development of apical periodontitis could be monitored with voxel counts, and quantitatively analyzed in terms of lesion size, bone loss, and mineral density in resorptive model. This study supports the application of longitudinal in vivo micro-CT for resorptive- and formative-phase specific monitoring of dental hard tissues.
This paper experimentally demonstrates a feasibility of moving beam-blocker-based low-dose cone-beam CT (CBCT) and exploits the beam-blocking configurations to reach an optimal one that leads to the ...highest contrast-to-noise ratio (CNR). Sparse-view CT takes projections at sparse view angles and provides a viable option to reducing dose. We have earlier proposed a many-view under-sampling (MVUS) technique as an alternative to sparse-view CT. Instead of switching the x-ray tube power, one can place a reciprocating multi-slit beam-blocker between the x-ray tube and the patient to partially block the x-ray beam. We used a bench-top circular cone-beam CT system with a lab-made moving beam-blocker. For image reconstruction, we used a modified total-variation minimization (TV) algorithm that masks the blocked data in the back-projection step leaving only the measured data through the slits to be used in the computation. The number of slits and the reciprocation frequency have been varied and the effects of them on the image quality were investigated. For image quality assessment, we used CNR and the detectability. We also analyzed the sampling efficiency in the context of compressive sensing: the sampling density and data incoherence in each case. We tested three sets of slits with their number of 6, 12 and 18, each at reciprocation frequencies of 10, 30, 50 and 70 Hz/rot. The optimum condition out of the tested sets was found to be using 12 slits at 30 Hz/rot.
This paper presents, for the first time, a study to analyze the surface morphology of metal extracted from a high temperature molten salt medium in the electrodeposit using x-ray radiography and ...computed tomography. Widely used methods such as scanning electron microscopy and inductively coupled plasma-optical emission spectrometry/mass spectrometry are destructive and the related processes are often subject to the air condition. The x-ray imaging can provide rich information of the target sample in a non-destructive way without invoking hydrolysis or oxidation of a hygroscopic sample. In this study, the x-ray imaging conditions were optimized as following: tube voltage at 100 kVp and the current exposure time product at 8.8 mAs in our in-house x-ray imaging system. LiCl-KCl and cerium metals used in this work produced substantially distinguishable contrasts in the radiography due to their distinctive attenuation characteristics, and this difference was well quantified in the histograms of brightness. Electrodeposits obtained by chronoamperometry and chronopotentiometry demonstrated a completely different behavior of electrodeposition even at the same applied charge. In particular, computed tomography and volumetric analysis clearly showed the structural and morphological dissimilarity. The structure of cerium metal in the electrodeposit was successfully separated from the chloride salt structure in the CT image by an image segmentation process.
The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). ...However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT.
In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation.
The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map.
This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.
The planning accuracy of charged particle therapy (CPT) is subject to the accuracy of stopping power (SP) estimation. In this study, we propose a method of deriving a pseudo-triple-energy CT (pTECT) ...that can be achievable in the existing dual-energy CT (DECT) systems for better SP estimation. In order to remove the direct effect of errors in CT values, relative CT values according to three scanning voltage settings were used. CT values of each tissue substitute phantom were measured to show the non-linearity of the values thereby suggesting the absolute difference and ratio of CT values as parameters for SP estimation. Electron density, effective atomic number (EAN), mean excitation energy and SP were calculated based on these parameters. Two of conventional methods were implemented and compared to the proposed pTECT method in terms of residuals, absolute error and root-mean-square-error (RMSE). The proposed method outperformed the comparison methods in every evaluation metrics. Especially, the estimation error for EAN and mean excitation using pTECT were converging to zero. In this proof-of-concept study, we showed the feasibility of using three CT values for accurate SP estimation. Our suggested pTECT method indicates potential clinical utility of spectral CT imaging for CPT planning.
Diffuse optical tomography (DOT) non-invasively measures the functional characteristics of breast lesions using near infrared light to probe tissue optical properties. This study aimed to evaluate a ...new digital breast tomosynthesis (DBT)/DOT fusion imaging technique and obtain preliminary data for breast cancer detection. Twenty-eight women were prospectively enrolled and underwent both DBT and DOT examinations. DBT/DOT fusion imaging was created after acquisition of both examinations. Two breast radiologists analyzed DBT and DOT images independently, and then finally evaluated the fusion images. The diagnostic performance of each reading session was compared and interobserver agreement was assessed. The technical success rate was 96.4%, with one failure due to an error during DOT data storage. Among the 27 women finally included in the analysis, 13 had breast cancer. The areas under the receiver operating characteristic curve (AUCs) for DBT were 0.783 and 0.854 for readers 1 and 2, respectively. DOT showed comparable diagnostic performance to DBT for both readers. The AUCs were significantly improved (P = 0.004) when the DBT/DOT fusion images were used. Interobserver agreements were highest for the DBT/DOT fusion images. In conclusion, this study suggests that DBT/DOT fusion imaging technique appears to be a promising tool for breast cancer diagnosis.
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