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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.

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.

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 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.