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In iterative methods for cone-beam computed tomography (CBCT) reconstruction, the use of a huge system matrix is the primary computational bottleneck and is still an obstacle to the more widespread use of these methods in practice. In this paper, to put iterative methods to practical applications, we propose a pragmatic idea, the-so-called dual-resolution voxellation scheme, for a small region-of-interest (ROI) reconstruction in CBCT in which voxels outside the ROI are binned with a double resolution such as 2×2×2, 4×4×4, 8×8×8, 16×16×16, etc., and the voxel sizewithin the ROI remains unchanged. In some situations of medical diagnosis, physicians are interested only in a small ROI containing a target diagnosis from the examined structure. We implemented an efficient compressed-sensing (CS)-based reconstruction algorithm with the proposed voxellation scheme incorporated and performed both simulation and experimental works to investigate the imaging characteristics. Our results indicate that the proposed voxellation scheme seems to be effective in reducing the computational cost considerably for a small ROI reconstruction in iterative CBCT, with the image quality inside the ROI not being noticeably impaired.

We propose a new voxellation scheme for iterative cone-beam computed tomography (CBCT) reconstruction with less computational cost. Here, the voxel configuration is designed with an isosceles-triangle shape by using polar coordinates, exploiting the rotational symmetries inherent to the CBCT acquisition geometry and keeping the uniformity of the voxel size as well. By using polar symmetries, we can reduce the size of the system matrix by a factor corresponding to the number of acquired projections, which speeds up the construction of the system matrix and, thus, allows iterative methods to be applied to practical applications within a reasonable reconstruction time. In this study, we implemented an efficient algorithm to reconstruct the system matrix based upon the proposed voxellation scheme and incorporated it into a built-in iterative CBCT reconstruction algorithm based the gradient-projection Barzilai-Borwein (GPBB) method, and we performed systematic simulation works to investigate the imaging characteristics. Our results indicate that the voxellation scheme we proposed in study seems to be effective in reducing the computational time and the memory burden considerably.

Digital breast tomosynthesis (DBT) is considered in clinics as a standard three-dimensional imaging modality, allowing the earlier detection of cancer. It typically acquires only 10–30 projections over a limited angle range of 15–60° with a stationary detector and typically uses a computationally-efficient filtered-backprojection (FBP) algorithm for image reconstruction. However, a common FBP algorithm yields poor image quality resulting from the loss of average image value and the presence of severe image artifacts due to the elimination of the dc component of the image by the ramp filter and to the incomplete data, respectively. As an alternative, iterative reconstruction methods are often used in DBT to overcome these difficulties, even though they are still computationally expensive. In this study, as a compromise, we considered a projection-angle-dependent filtering method in which one-dimensional geometry-adapted filter kernels are computed with the aid of a conjugate-gradient method and are incorporated into the standard FBP framework. We implemented the proposed algorithm and performed systematic simulation works to investigate the imaging characteristics. Our results indicate that the proposed method is superior to a conventional FBP method for DBT imaging and has a comparable computational cost, while preserving good image homogeneity and edge sharpening with no serious image artifacts.

In practical applications of three-dimensional (3D) tomographic techniques, such as digital breast tomosynthesis (DBT), computed tomography (CT), etc., there are often challenges for accurate image reconstruction from incomplete data. In DBT, in particular, the limited-angle and few-view projection data are theoretically insufficient for exact reconstruction; thus, the use of common filtered-backprojection (FBP) algorithms leads to severe image artifacts, such as the loss of the average image value and edge sharpening. One possible approach to alleviate these artifacts may employ iterative statistical methods because they potentially yield reconstructed images that are in better accordance with the measured projection data. In this work, as another promising approach, we investigated potential applications to low-dose, accurate DBT imaging with a state-of-the-art reconstruction scheme based on compressed-sensing (CS) theory. We implemented an efficient CS-based DBT algorithm and performed systematic simulation works to investigate the imaging characteristics. We successfully obtained DBT images of substantially very high accuracy by using the algorithm and expect it to be applicable to developing the next-generation 3D breast X-ray imaging system.

Recently, reducing radiation doses has become an issue of critical importance in the broader radiological community. As a possible technical approach, especially, in dental cone-beam computed tomography (CBCT), reconstruction from limited-angle view data (< 360°) would enable fast scanning with reduced doses to the patient. In this study, we investigated and implemented an efficient reconstruction algorithm based on compressed-sensing (CS) theory for the scan geometry and performed systematic simulation works to investigate the image characteristics. We also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in incomplete data problems. We successfully reconstructed CBCT images with incomplete projections acquired at selected scan angles of 120, 150, 180, and 200° with a fixed angle step of 1.2° and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from limited-angle view data show that the algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality.

The most popular reconstruction algorithm for cone-beam computed tomography (CBCT) is based on the computationally-inexpensive filtered-backprojection (FBP) method. However, that method usually requires dense projections over the Nyquist samplings, which imposes severe restrictions on the imaging doses. Moreover, the algorithm tends to produce cone-beam artifacts as the cone angle is increased. Several variants of the FBP-based algorithm have been developed to overcome these difficulties, but problems with the cone-beam reconstruction still remain. In this study, we considered a compressed-sensing (CS)-based reconstruction algorithm for low-dose, high-quality dental CBCT images that exploited the sparsity of images with substantially high accuracy. We implemented the algorithm and performed systematic simulation works to investigate the imaging characteristics. CBCT images of high quality were successfully reconstructed by using the built-in CS-based algorithm, and the image qualities were evaluated quantitatively in terms of the universal-quality index (UQI) and the slice-profile quality index (SPQI).We expect the reconstruction algorithm developed in the work to be applicable to current dental CBCT systems, to reduce imaging doses, and to improve the image quality further.

In practical applications of three-dimensional (3D) tomographic imaging, there are often challenges for image reconstruction from insufficient data. In computed tomography (CT); for example, image reconstruction from few views would enable fast scanning with reduced doses to the patient. In this study, we investigated and implemented an efficient reconstruction method based on a compressed-sensing (CS) algorithm, which exploits the sparseness of the gradient image with substantially high accuracy, for accurate, low-dose dental cone-beam CT (CBCT) reconstruction. We applied the algorithm to a commercially-available dental CBCT system (Expert7™, Vatech Co., Korea) and performed experimental works to demonstrate the algorithm for image reconstruction in insufficient sampling problems. We successfully reconstructed CBCT images from several undersampled data and evaluated the reconstruction quality in terms of the universal-quality index (UQI). Experimental demonstrations of the CS-based reconstruction algorithm appear to show that it can be applied to current dental CBCT systems for reducing imaging doses and improving the image quality.

As a continuation of our dental imaging R&D, we have proposed a novel idea that is capable of implementing cost-effective, low-dose, volumetric image reconstruction directly onto a dental panoramic imaging system. In the proposed geometry, a linear-type panoramic detector is rotated 90° from the orientation for panoramic imaging and scanned along a limited-angle zigzag trajectory in the axial direction to cover the whole imaging volume thickness. We used an effective reconstruction algorithm based on the total-variation (TV) minimization approach for the proposed geometry and performed systematic simulation work to demonstrate the viability of our proposed approach and its effectiveness for three-dimensional (3D) dental X-ray imaging. We have successfully reconstructed images of substantially high image accuracy from the proposed geometry and evaluated the reconstruction quality by using an image similarity metric, the universal-quality index (UQI). We expect the proposed method to be applicable to developing a cost-effective, low-dose, all-in-one dental X-ray imaging system.

As a new direction for computed tomography (CT) imaging, inverse-geometry CT (IGCT) has been recently introduced and is intended to overcome limitations in conventional cone-beam CT (CBCT) such as the cone-beam artifacts, imaging dose, temporal resolution, scatter, cost, and so on. While the CBCT geometry consists of X-rays emanating from a small focal spot and collimated toward a larger detector, the IGCT geometry employs a large-area scanned source array with the Xray beams collimated toward a smaller-area detector. In this research, we explored an effective IGCT reconstruction algorithm based on the total-variation (TV) minimization method and studied the feasibility of the IGCT geometry for potential applications to fast, low-dose volumetric dental X-ray imaging. We implemented the algorithm, performed systematic simulation works, and evaluated the imaging characteristics quantitatively. Although much engineering and validation works are required to achieve clinical implementation, our preliminary results have demonstrated a potential for improved volumetric imaging with reduced dose.

In the paper, we proposed a pragmatic method capable of implementing a cost-effective, low-dose CT reconstruction directly onto a dental panoramic X-ray imaging system by adopting a spiral source trajectory. In the proposed geometry, a linear-type panoramic imaging sensor is rotated 90° from the orientation for panoramic imaging to imitate fan-beam image acquisition. For image reconstruction, we considered a total variation (TV) minimization-based algorithm that exploited the sparsity of the image gradient and was capable of reconstructing CT images with substantially high image accuracy against the image artifacts from sparse-view data. We implemented the algorithm for the proposed geometry and performed systematic simulation works to demonstrate its feasibility for dental imaging applications. CT images were successfully reconstructed from the proposed geometry, and the reconstruction quality was evaluated quantitatively by using an image similarity metric. We expect the proposed method to be applicable to developing a cost-effective, low-dose, all-in-one dental imaging system.

In this paper, we introduce limited-angle tomography in which the object being imaged is rotated around the center of an inclined X-ray beam, the so-called micro-DTS (digital tomosynthesis), with a few-view image reconstruction based on the compressed-sensing (CS) theory for applications of fast, low-dose X-ray imaging. We implemented an effective CS-based reconstruction algorithm for micro-DTS and performed systematic simulation works. The assessment of the image characteristics was performed by using several figures of merit such as the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal image-quality index (UQI) to compare the reconstructed images to the simulated phantoms. According to our results, compared to the FBP-based method, the CS-based reconstruction method substantially enhanced image accuracy against image artifacts from few-view and limited-angle projections. In the simulation, 41 projections were used for the half-tomographic angles of 30°, 45°, and 60°, giving UQI values of 0.92 ∼ 0.97, which seems promising for potential applications of fast, low-dose X-ray imaging.

Panoramic radiography with which only structures within a certain image layer are in focus and others out of focus on the panoramic image has become a popular imaging technique especially in dentistry. However, the major drawback to the technique is a mismatch between the structures to be focused and the predefined image layer mainly due to the various shapes and sizes of dental arches and/or to malpositioning of the patient. These result in image quality typically inferior to that obtained using intraoral radiographic techniques. In this paper, to overcome these difficulties, we suggest a new panoramic reconstruction algorithm, the so-called adaptive panoramic tomography (APT), capable of reconstructing multifocal image layers with no additional exposure. In order to verify the effectiveness of the proposed algorithm, we performed systematic simulation studies with a circular rotational movement and investigated the image performance.

As a continuation of our digital X-ray imaging sensor R&D, we have developed a cost-effective, intraoral imaging device based on the complementary-metal–oxide semiconductor (CMOS) photosensor array coupled with an integrated X-ray conversion fiber-optic faceplate. It consists of a commercially available CMOS photosensor of a 35×35 μm 2 pixel size and a 688×910 pixel array dimension, and a high-efficiency columnar CsI(Tl) scintillator of a 90 μm thickness directly deposited on a fiber-optic faceplate of a 6 μm core size and an 1.46 mm thickness with 85/15 core–cladding ratio (NA∼1.0 in air). The fiber-optic faceplate is a highly X-ray attenuating material that minimizes X-ray absorption on the end CMOS photosensor array, thus, minimizing X-ray induced noise at the photosensor array. It uses a high light-output columnar CsI(Tl) scintillator with a peak spectral emission at 545 nm, giving better spatial resolution, but attenuates some of this light due to interfacial and optical attenuation factors. In this paper, we presented the performance analysis of the intraoral imaging device with experimental measurements and acquired X-ray images in terms of modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE).