Sunghoon Choi

Development of a digital panoramic X-ray imaging system for dental applications

As a continuation of our digital radiographic sensor R&D, we have developed a prototyped digital panoramic X-ray imaging system for dental applications. The imaging system consists of a slit-collimated X-ray generator with a 0.4 mm focal spot size and a 3.5 mm Al filtration, a linear-array typed CMOS imager with a 48 times 48 mum2 pixel size and a 128 (in the scan direction) times 3072 (in the vertical direction) pixel format, a series of microstep motors for the precise motion control of the imaging system, and the designed sequences for the motion control and pixel readout required to make a specific plane of interest (POI) to be focused. With the several test phantoms we designed, we, for the first time, succeeded in obtaining useful digital panoramic X-ray images by moving the X-ray generator and the CMOS imager along a continuously-sliding rotational center. In this study, we demonstrated that the prototype system can be applicable to any shaped POI or multi-POIs simultaneously to be focused, provided that adequate sequences for motion control and pixel readout are designed. We expect that the imaging system will be useful for our ongoing applications of dental panoramic radiography and nondestructive testings.

Performance of a Digital Gamma-Imaging System Based Upon CdTe-CMOS Sensor and

As a continuation of our digital radiographic sensor R&D, we have developed a digital gamma-imaging system based upon the commercially-available CdTe-CMOS sensor (AJAT, SCAN1000) and the 75Se gamma source (MDS, Gamma Mat@ SE) for our ongoing application of nondestructive testing. Here the sensor has a 750-mum-thick CdTe photoconductor as an efficient radiation converter and a CMOS pixel array having 100times100 mum2 pixel size and 5.41times51.0 mm2 active area, bump-bonded to the photoconductor for signal readout. The source has about 62.8 Ci activity and a physical size of 3.0 mm in diameter. For the first time in this project, we have succeeded in obtaining useful gamma images from the imaging system and evaluated the imaging performance in terms of the resolving power, the line spread function (LSF), the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE). For comparison, we also evaluated the image quality by using a microfocus X-ray source (Hamamatsu, L9121-01) having a focal spot size of about 5 mum.

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Abstract Dose reduction techniques have been studied in medical imaging. We propose shutter scan acquisition for region of interest (ROI) imaging to reduce the patient exposure dose received from a digital tomosynthesis system. A prototype chest digital tomosynthesis (CDT) system (LISTEM, Wonju, Korea) and the LUNGMAN phantom (Kyoto Kagaku, Japan) with lung nodules 8, 10, and 12 mm in size were used for this study. A total of 41 projections with shutter scan acquisition consisted of 21 truncated projections and 20 non‐truncated projections. For comparison, 41 projections using conventional full view scan acquisition were also acquired. Truncated projections obtained by shutter scan acquisition were corrected by proposed image processing procedure to remove the truncation artifacts. The image quality was evaluated using the contrast to noise ratio (CNR), coefficient of variation (COV), and figure of merit (FOM). We measured the dose area product (DAP) value to verify the dose reduction using shutter scan acquisition. The ROI of the reconstructed image from shutter scan acquisition showed enhanced contrast. The results showed that CNR values of 8 and 12 mm lung nodules increased by 6.38% and 21.21%, respectively, and the CNR value of 10 mm lung nodule decreased by 3.63%. COV values of the lung nodules were lower in a shutter scan image than in a full view scan image. FOM values of 8, 10, and 12 mm lung nodules increased by 3.06, 2.25, and 2.33 times, respectively. This study compared the proposed shutter scan and conventional full view scan acquisition. In conclusion, using a shutter scan acquisition method resulted in enhanced contrast images within the ROI and higher FOM values. The patient exposure dose of the proposed shutter scan acquisition method can be reduced by limiting the field of view (FOV) to focus on the ROI.

Se Source for Nondestructive Testing

Recently, we have successfully developed very precise carbon-interspaced antiscatter grids by adopting the sawing process used in the semiconductor industry, in order to employ them to digital radiographic (DR) systems. For the systematic performance evaluate, we prepared 12 carbon-interspaced sample grids with strip densities in the range of 40-85 lines/cm for a fixed grid ratio of 5:1 and with grid ratios in the range of 5:1-10:1, in turn, for a fixed strip density of 80 lines/cm and established a well-controlled test condition based upon the IEC standard. For comparison, we also prepared 6 additional aluminum-interspaced sample grids, which were fabricated by using the conventional method, with the same geometrical parameters. In this paper, we presented the performance characteristics of the prepared sample grids with both experimental measurements and calculations in terms of the transmission of primary radiation (Tp), the transmission of scattered radiation (Ts), the transmission of total radiation (Tt), the selectivity (Sigma), the contrast improvement factor (Cif), and the Bucky factor (B). We also described the line artifacts which were easily found in the use of grids in DR imaging systems and proposed possible solutions to overcome these difficulties effectively.

Feasibility study of shutter scan acquisition for region of interest (ROI) digital tomosynthesis

PURPOSEnThis work describes the hardware and software developments of a prototype chest digital tomosynthesis (CDT) R/F system. The purpose of this study was to validate the developed system for its possible clinical application on low-dose chest tomosynthesis imaging.nnnMETHODSnThe prototype CDT R/F system was operated by carefully controlling the electromechanical subsystems through a synchronized interface. Once a command signal was delivered by the user, a tomosynthesis sweep started to acquire 81 projection views (PVs) in a limited angular range of ±20°. Among the full projection dataset of 81 images, several sets of 21 (quarter view) and 41 (half view) images with equally spaced angle steps were selected to represent a sparse view condition. GPU-accelerated and total-variation (TV) regularization strategy-based compressed sensing (CS) image reconstruction was implemented. The imaged objects were a flat-field using a copper filter to measure the noise power spectrum (NPS), a Catphan® CTP682 quality assurance (QA) phantom to measure a task-based modulation transfer function (MTFTask ) of three different cylinders edge, and an anthropomorphic chest phantom with inserted lung nodules. The authors also verified the accelerated computing power over CPU programming by checking the elapsed time required for the CS method. The resultant absorbed and effective doses that were delivered to the chest phantom from two-view digital radiographic projections, helical computed tomography (CT), and the prototype CDT system were compared.nnnRESULTSnThe prototype CDT system was successfully operated, showing little geometric error with fast rise and fall times of R/F x-ray pulse less than 2 and 10 ms, respectively. The in-plane NPS presented essential symmetric patterns as predicted by the central slice theorem. The NPS images from 21 PVs were provided quite different pattern against 41 and 81 PVs due to aliased noise. The voxel variance values which summed all NPS intensities were inversely proportional to the number of PVs, and the CS method gave much lower voxel variance by the factors of 3.97-6.43 and 2.28-3.36 compared to filtered backprojection (FBP) and 20 iterations of simultaneous algebraic reconstruction technique (SART). The spatial frequencies of the f50 at which the MTFTask reduced to 50% were 1.50, 1.55, and 1.67 cycles/mm for FBP, SART, and CS methods, respectively, in the case of Bone 20% cylinder using 41 views. A variety of ranges of TV reconstruction parameters were implemented during the CS method and we could observe that the NPS and MTFTask preserved best when the regularization and TV smoothing parameters α and τ were in a range of 0.001-0.1. For the chest phantom data, the signal difference to noise ratios (SDNRs) were higher in the proposed CS scheme images than in the FBP and SART, showing the enhanced rate of 1.05-1.43 for half view imaging. The total averaged reconstruction time during 20 iterations of the CS scheme was 124.68 s, which could match-up a clinically feasible time (<3 min). This computing time represented an enhanced speed 386 times greater than CPU programming. The total amounts of estimated effective doses were 0.12, 0.53 (half view), and 2.56 mSv for two-view radiographs, the prototype CDT system, and helical CT, respectively, showing 4.49 times higher than conventional radiography and 4.83 times lower than a CT exam, respectively.nnnCONCLUSIONSnThe current work describes the development and performance assessment of both hardware and software for tomosynthesis applications. The authors observed reasonable outcomes by showing a potential for low-dose application in CDT imaging using GPU acceleration.

Performance evaluate of carbon-interspaced and aluminum-interspaced antiscatter grids based upon the IEC standard fixture

PURPOSEnSparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventional CT. In general, iterative reconstruction techniques have been applied to sparsely sampled CT to realize high quality images. These methodologies require high computing power due to the modeling of the system and the trajectory of radiation rays. Therefore, the purpose of this study was to obtain high quality three-dimensional (3D) reconstructed images with deep learning under sparse sampling conditions.nnnMETHODSnWe used a deep learning model based on a fully convolutional network and a wavelet transform to predict high quality images. To reduce the spatial resolution loss of predicted images, we replaced the pooling layer with a wavelet transform. Three different domains were evaluated - the sinogram domain, the image domain, and the hybrid domain - to optimize a reconstruction technique based on deep learning. To train and develop a deep learning model, The Cancer Imaging Archive (TCIA) dataset was used.nnnRESULTSnStreak artifacts, which generally occur under sparse sampling conditions, were effectively removed from deep learning-based sparsely sampled reconstructed images. However, image characteristics of fine structures varied depending on the application of deep learning technologies. The use of deep learning techniques in the sinogram domain removed streak artifacts well, but some image noise remained. Likewise, when applying deep learning technology to the image domain, a blurring effect occurred. The proposed hybrid domain sparsely sampled reconstruction based on deep learning was able to restore images to a quality similar to fully sampled images. The structural similarity (SSIM) index values of sparsely sampled CT reconstruction based on deep learning technology were 0.85 or higher. Among the three domains studied, the hybrid domain techniques achieved the highest SSIM index values (0.9 or more).nnnCONCLUSIONnWe proposed a method of sparsely sampled CT reconstruction from a new perspective - unlike iterative reconstruction. In addition, we developed an optimal deep learning-based sparse sampling reconstruction technique by evaluating image quality with deep learning technologies.

Development of a chest digital tomosynthesis R/F system and implementation of low‐dose GPU‐accelerated compressed sensing (CS) image reconstruction

Quantitative imaging performance analysis has recently been the focus in medical imaging fields. It would not only provide objective information but also it could aid a patient diagnosis by giving optimized system parameters for various imaging tasks. However, the previous studies on task-based metric in breast tomosynthesis usually take into account a cascaded system modeling for generalized noise equivalent quanta. In this study, the authors have been focused on the experimental study for calculating task-based detectability index (d) in the prototype breast tomosynthesis system for different angular ranges. According to the summarized d the authors observed that the highest d could be found in the angular range of ±10.5° (1.5° angle step) among several cases for detection of 4.7 mm mass in our prototype breast tomosynthesis system. Our study would be easily applied in practical breast tomosynthesis for the quantitative performance analysis of imaging parameter is needed. More various imaging tasks with different parameter combinations would be conducted in the future for generalized optimization of breast tomosynthesis study.

High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains

Quantitative imaging analysis has become a focus of medical imaging fields in recent days. In this study, Fourier-based imaging metrics for task-based quantitative assessment of lung nodules were applied in low-dose chest tomosynthesis. Compared to the conventional filtered back-projection (FBP), a compressed-sensing (CS) image reconstruction has been proposed for dose and artifact reduction. We implemented the CS-based low-dose reconstruction scheme to a sparsely sampled projection dataset and compared the lung nodule detectability index (d’) between the FBP and CS methods. We used the non-prewhitening (NPW) model observer to estimate the in-plane slice detectability in tomosynthesis and theoretically calculated d’ using the weighted amounts of local noise, spatial resolution, and task function in Fourier domain. We considered spatially varying noise and spatial resolution properties because the iterative reconstruction showed non-stationary characteristics. For analysis of task function, we adopted a simple binary hypothesis-testing model which discriminates outer and inner region of the encapsulated shape of lung nodule. The results indicated that the local noise power spectrum showed smaller intensities with increasing the number of projections, whereas the local transfer function provided similar appearances between the FBP and CS schemes. The resulted task functions for the same size of lung nodules showed the same pattern with different intensity, whereas the task function for different size of lung nodules presented different shapes due to different object functions. The theoretically calculated d’ values showed that the CS schemes provided higher values than the FBP method by factors of 2.64-3.47 and 2.50-3.10 for two different lung nodules among all projection views. This could demonstrate that the low-dose CS algorithm provide a comparable lung nodule images in comparison to FBP from 37.9% up to 28.8% reduced dose in the same projection views. Moreover, we observed that the CS method implemented with small number of projections provided similar or somewhat higher d’ values compared to the FBP method with large number of projections. In conclusion, the CS scheme may present a potential dose reduction for lung nodule detection in the chest tomosynthesis by showing higher d’ in comparison to the conventional FBP method.

Comparison study of task-based detectability index according to angular distribution in a prototype breast tomosynthesis

Patient respiration induces motion artifacts during cone-beam CT (CBCT) imaging in LINAC-based radiotherapy guidance. This could be relieved by retrospectively sorting the projection images, which could be called as a respiratorycorrelated CBCT or a four-dimensional (4D) CBCT imaging. However, the slowness of the LINAC head gantry rotation limits a rapid scan time so that 4D-CBCT usually involves large amounts of radiation dose. Digital tomosynthesis (DTS) which utilizes limited angular range would present a faster 4D imaging with much lower radiation dose. One drawback of 4D-DTS is strong streak artifacts represented in the tomosynthesis reconstructed images due to sparsely sampled projections in each phase bin. The authors suggest a fast low-dose 4D-DTS image reconstruction method in order to effectively reduce the artifacts in a sparse imaging tomosynthesis condition. We used a flat-panel detector to acquire tomosynthesis projections of respiratory moving phantom in anterior-posterior (AP) and lateral views. We entered a sinusoidal periodic respiratory motion for an input signal to the phantom. An external monitor of Varian real-time position management (RPM) system was used to estimate the input respiratory motion, thereby four respiratory gating phases were determined to retrospectively arrange the projections. For streak line reduction, we introduced a simple iterative scheme suggested by McKinnon and Bates (MKB) and regarded it as a prior input image of the proposed lowdose compressed sensing (CS) method. Three different 4D-DTS image reconstruction schemes of conventional Feldkamp (FDK), MKB, and MKB-CS were implemented to phase-wise projections of both AP and lateral views. All reconstructions were accelerated by using a GPU card to reduce the computation times. For assessment of algorithmic performance, we compared a streak reduction ratio (SRR) and a contrast-to-noise-ratio (CNR) among the outcome images. The results showed that SRRs for MKB and MKB-CS schemes were 0.24 and 0.69, respectively, which indicates that the proposed MKB-CS method provided smaller streaking artifacts than conventional one by factor of 2.88. The resulted CNRs of coronal tomosynthesis images at peak-inhale phase were 3.24, 6.36, and 10.56 for FDK, MKB, and MKB-CS, respectively. This shows that the proposed method provides better image quality compared to others. The reconstruction time for MKB-CS was 196.07 sec, showing that our GPU-accelerated programming would enhance the algorithmic performance to match clinically feasible times (~3 min). In conclusion, the proposed low-dose 4D-DTS reconstruction scheme may provide better outcomes than the conventional methods with fast speed, and could thus it could be applied in practical 4D imaging for radiotherapy.

Quantitative lung nodule detectability and dose reduction in low-dose chest tomosynthesis

In medical imaging field, various dose reduction techniques have been studied. We proposed shutter scan acquisition for region of interest (ROI) imaging to reduce the patient exposure dose in digital tomosynthesis system. Projections obtained by shutter scan acquisition is a combination of truncated projections and non-truncated projections. In this study, we call the number of truncated projections divided by the number of non-truncated projections as shutter weighting factor. The shutter scan acquisition parameters were optimized using 5 different acquisition sets with the shutter weighting factor (0.16, 0.35, 1.03, 3.05 and 7.1). A prototype CDT system (LISTEM, Korea) and the LUNGMAN phantom (Kyoto Kagaku, Japan) with an 8 mm lung nodule were used. A total of 81 projections with shutter scan acquisition were obtained in 5 sets according to shutter weighting factor. The image quality was investigated using the contrast noise ratio (CNR). We also calculated figure of merit (FOM) to determine optimal acquisition parameters for the shutter scan acquisition. The ROI of the reconstructed image with shutter scan acquisition showed enhanced contrast. The highest CNR and FOM value, shutter weighting factor 7.1, is the acquisition set consisting of 71 truncated projections and 10 non-truncated projections. In this study, we investigated the effects of composition ratio of the truncated and non-truncated projections on reconstructed images through the shutter scan acquisition. In addition, the optimal acquisition conditions for the shutter scan acquisition were determined by deriving the FOM values. In conclusion, we can suggest optimal shutter scan acquisition parameters on the lesion within the ROI to be diagnosed.