Taewon Lee

Effects of sparse sampling schemes on image quality in low‐dose CT

PURPOSEnVarious scanning methods and image reconstruction algorithms are actively investigated for low-dose computed tomography (CT) that can potentially reduce a health-risk related to radiation dose. Particularly, compressive-sensing (CS) based algorithms have been successfully developed for reconstructing images from sparsely sampled data. Although these algorithms have shown promises in low-dose CT, it has not been studied how sparse sampling schemes affect image quality in CS-based image reconstruction. In this work, the authors present several sparse-sampling schemes for low-dose CT, quantitatively analyze their data property, and compare effects of the sampling schemes on the image quality.nnnMETHODSnData properties of several sampling schemes are analyzed with respect to the CS-based image reconstruction using two measures: sampling density and data incoherence. The authors present five different sparse sampling schemes, and simulated those schemes to achieve a targeted dose reduction. Dose reduction factors of about 75% and 87.5%, compared to a conventional scan, were tested. A fully sampled circular cone-beam CT data set was used as a reference, and sparse sampling has been realized numerically based on the CBCT data.nnnRESULTSnIt is found that both sampling density and data incoherence affect the image quality in the CS-based reconstruction. Among the sampling schemes the authors investigated, the sparse-view, many-view undersampling (MVUS)-fine, and MVUS-moving cases have shown promising results. These sampling schemes produced images with similar image quality compared to the reference image and their structure similarity index values were higher than 0.92 in the mouse head scan with 75% dose reduction.nnnCONCLUSIONSnThe authors found that in CS-based image reconstructions both sampling density and data incoherence affect the image quality, and suggest that a sampling scheme should be devised and optimized by use of these indicators. With this strategic approach, one can acquire optimally sampled sparse data so that the CS-based algorithms can best perform in terms of image quality.

Feasibility study on many-view under-sampling technique for low-dose computed tomography

We proposed a novel scanning method for low-dose computed tomography (CT) that uses an oscillating multi-slit collimator between the x-ray source and the patient. It can be thought as a realization of sparse data sampling that does not require a fast x-ray power switching. A simulation study was performed based on experimentally acquired microCT data of a mouse to demonstrate the feasibility of the proposed method. A numerical collimation was designed to leave only one-fourth of each projection data for use in image reconstruction. A total-variation minimization algorithm was implemented for image reconstruction from the sparely sampled data. We have successfully shown that the proposed method provides a viable option to low-dose CT.

Fully iterative scatter corrected digital breast tomosynthesis using GPU-based fast Monte Carlo simulation and composition ratio update.

PURPOSEnIn digital breast tomosynthesis (DBT), scatter correction is highly desirable, as it improves image quality at low doses. Because the DBT detector panel is typically stationary during the source rotation, antiscatter grids are not generally compatible with DBT; thus, a software-based scatter correction is required. This work proposes a fully iterative scatter correction method that uses a novel fast Monte Carlo simulation (MCS) with a tissue-composition ratio estimation technique for DBT imaging.nnnMETHODSnTo apply MCS to scatter estimation, the material composition in each voxel should be known. To overcome the lack of prior accurate knowledge of tissue composition for DBT, a tissue-composition ratio is estimated based on the observation that the breast tissues are principally composed of adipose and glandular tissues. Using this approximation, the composition ratio can be estimated from the reconstructed attenuation coefficients, and the scatter distribution can then be estimated by MCS using the composition ratio. The scatter estimation and image reconstruction procedures can be performed iteratively until an acceptable accuracy is achieved. For practical use, (i) the authors have implemented a fast MCS using a graphics processing unit (GPU), (ii) the MCS is simplified to transport only x-rays in the energy range of 10-50 keV, modeling Rayleigh and Compton scattering and the photoelectric effect using the tissue-composition ratio of adipose and glandular tissues, and (iii) downsampling is used because the scatter distribution varies rather smoothly.nnnRESULTSnThe authors have demonstrated that the proposed method can accurately estimate the scatter distribution, and that the contrast-to-noise ratio of the final reconstructed image is significantly improved. The authors validated the performance of the MCS by changing the tissue thickness, composition ratio, and x-ray energy. The authors confirmed that the tissue-composition ratio estimation was quite accurate under a variety of conditions. Our GPU-based fast MCS implementation took approximately 3 s to generate each angular projection for a 6 cm thick breast, which is believed to make this process acceptable for clinical applications. In addition, the clinical preferences of three radiologists were evaluated; the preference for the proposed method compared to the preference for the convolution-based method was statistically meaningful (p < 0.05, McNemar test).nnnCONCLUSIONSnThe proposed fully iterative scatter correction method and the GPU-based fast MCS using tissue-composition ratio estimation successfully improved the image quality within a reasonable computational time, which may potentially increase the clinical utility of DBT.

Low-dose dual-energy cone-beam CT using a total-variation minimization algorithm

Dual-energy cone-beam CT is an important imaging modality in diagnostic applications, and may also find its use in other applications such as therapeutic image guidance. Despite of its clinical values, relatively high radiation dose of dual-energy scan may pose a challenge to its wide use. In this work, we investigated a low-dose, pre-reconstruction type of dual-energy cone-beam CT (CBCT) using a total-variation minimization algorithm for image reconstruction. An empirical dual-energy calibration method was used to prepare material-specific projection data. Raw data acquired at high and low tube voltages are converted into a set of basis functions which can be linearly combined to produce material-specific data using the coefficients obtained through the calibration process. From much fewer views than are conventionally used, material specific images are reconstructed by use of the total-variation minimization algorithm. An experimental study was performed to demonstrate the feasibility of the proposed method using a micro-CT system. We have reconstructed images of the phantoms from only 90 projections acquired at tube voltages of 40 kVp and 90 kVp each. Aluminum-only and acryl-only images were successfully decomposed. A low-dose dual-energy CBCT can be realized via the proposed method by greatly reducing the number of projections.

Moving Beam-Blocker-Based Low-Dose Cone-Beam CT

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.

Efficient digitalization method for dental restorations using micro-CT data

The objective of this study was to demonstrate the feasibility of using micro-CT scan of dental impressions for fabricating dental restorations and to compare the dimensional accuracy of dental models generated from various methods. The key idea of the proposed protocol is that dental impression of patients can be accurately digitized by micro-CT scan and that one can make digital cast model from micro-CT data directly. As air regions of the micro-CT scan data of dental impression are equivalent to the real teeth and surrounding structures, one can segment the air regions and fabricate digital cast model in the STL format out of them. The proposed method was validated by a phantom study using a typodont with prepared teeth. Actual measurement and deviation map analysis were performed after acquiring digital cast models for each restoration methods. Comparisons of the milled restorations were also performed by placing them on the prepared teeth of typodont. The results demonstrated that an efficient fabrication of precise dental restoration is achievable by use of the proposed method.

Auto-focused panoramic dental tomosynthesis imaging with exponential polynomial based sharpness indicators

In this paper, we develop an improved auto-focusing capability of a panoramic dental tomosynthesis imager. We propose an auto-focusing algorithm with an efficient sharpness indicator based on exponential polynomials which provides better quantitation of steep gradients than the conventional one based on algebraic polynomials. With its accurate estimation of the sharpness of the reconstructed slices, the proposed method resulted in a better performance of automatically extracting in-focus slices in the dental panoramic tomosynthesis.

A novel pre‐processing technique for improving image quality in digital breast tomosynthesis

Purpose: Nonlinear pre‐reconstruction processing of the projection data in computed tomography (CT) where accurate recovery of the CT numbers is important for diagnosis is usually discouraged, for such a processing would violate the physics of image formation in CT. However, one can devise a pre‐processing step to enhance detectability of lesions in digital breast tomosynthesis (DBT) where accurate recovery of the CT numbers is fundamentally impossible due to the incompleteness of the scanned data. Since the detection of lesions such as micro‐calcifications and mass in breasts is the purpose of using DBT, it is justified that a technique producing higher detectability of lesions is a virtue. Methods: A histogram modification technique was developed in the projection data domain. Histogram of raw projection data was first divided into two parts: One for the breast projection data and the other for background. Background pixel values were set to a single value that represents the boundary between breast and background. After that, both histogram parts were shifted by an appropriate amount of offset and the histogram‐modified projection data were log‐transformed. Filtered‐backprojection (FBP) algorithm was used for image reconstruction of DBT. To evaluate performance of the proposed method, we computed the detectability index for the reconstructed images from clinically acquired data. Results: Typical breast border enhancement artifacts were greatly suppressed and the detectability of calcifications and masses was increased by use of the proposed method. Compared to a global threshold‐based post‐reconstruction processing technique, the proposed method produced images of higher contrast without invoking additional image artifacts. Conclusions: In this work, we report a novel pre‐processing technique that improves detectability of lesions in DBT and has potential advantages over the global threshold‐based post‐reconstruction processing technique. The proposed method not only increased the lesion detectability but also reduced typical image artifacts pronounced in conventional FBP‐based DBT.

Investigation on Beam-Blocker-Based Scatter Correction Method for Improving CT Number Accuracy

Cone-beam computed tomography (CBCT) is gaining widespread use in various medical and industrial applications but suffers from substantially larger amount of scatter than that in the conventional diagnostic CT resulting in relatively poor image quality. Various methods that can reduce and/or correct for the scatter in the CBCT have therefore been developed. Scatter correction method that uses a beam-blocker has been considered a direct measurement-based approach providing accurate scatter estimation from the data in the shadows of the beam-blocker. To the best of our knowledge, there has been no record reporting the significance of the scatter from the beam-blocker itself in such correction methods. In this paper, we identified the scatter from the beam-blocker that is detected in the object-free projection data investigated its influence on the image accuracy of CBCT reconstructed images, and developed a scatter correction scheme that takes care of this scatter as well as the scatter from the scanned object.

A Feasibility Study of Low-Dose Single-Scan Dual-Energy Cone-Beam CT in Many-View Under-Sampling Framework

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.