Haenghwa Lee

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

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.

Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms

AbstractDigital breast tomosynthesis (DBT) is a recently developed system for three-dimensional imaging that offers the potential to reduce the false positives of mammography by preventing tissue overlap. Many qualitative evaluations of digital breast tomosynthesis were previously performed by using a phantom with an unrealistic model and with heterogeneous background and noise, which is not representative of real breasts. The purpose of the present work was to compare reconstruction algorithms for DBT by using various breast phantoms; validation was also performed by using patient images. DBT was performed by using a prototype unit that was optimized for very low exposures and rapid readout. Three algorithms were compared: a back-projection (BP) algorithm, a filtered BP (FBP) algorithm, and an iterative expectation maximization (EM) algorithm. To compare the algorithms, three types of breast phantoms (homogeneous background phantom, heterogeneous background phantom, and anthropomorphic breast phantom) were evaluated, and clinical images were also reconstructed by using the different reconstruction algorithms. The in-plane image quality was evaluated based on the line profile and the contrast-to-noise ratio (CNR), and out-of-plane artifacts were evaluated by means of the artifact spread function (ASF). Parenchymal texture features of contrast and homogeneity were computed based on reconstructed images of an anthropomorphic breast phantom. The clinical images were studied to validate the effect of reconstruction algorithms. The results showed that the CNRs of masses reconstructed by using the EM algorithm were slightly higher than those obtained by using the BP algorithm, whereas the FBP algorithm yielded much lower CNR due to its high fluctuations of background noise. The FBP algorithm provides the best conspicuity for larger calcifications by enhancing their contrast and sharpness more than the other algorithms; however, in the case of small-size and low-contrast microcalcifications, the FBP reduced detectability due to its increased noise. The EM algorithm yielded high conspicuity for both microcalcifications and masses and yielded better ASFs in terms of the full width at half maximum. The higher contrast and lower homogeneity in terms of texture analysis were shown in FBP algorithm than in other algorithms. The patient images using the EM algorithm resulted in high visibility of low-contrast mass with clear border. In this study, we compared three reconstruction algorithms by using various kinds of breast phantoms and patient cases. Future work using these algorithms and considering the type of the breast and the acquisition techniques used (e.g., angular range, dose distribution) should include the use of actual patients or patient-like phantoms to increase the potential for practical applications.

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

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.

Optimization of shutter scan parameters in digital tomosynthesis system

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.

Improvement of image quality and density accuracy of breast peripheral area in mammography

During breast image acquisition from the mammography, the inner regions of the breast are relatively thicker and denser than the peripheral areas, which can lead to overexposure to the periphery. Some images show low visibility of tissue structures in the breast peripheral areas due to the intensity change. It has a negative effect on diagnosis for breast cancer detection. To improve image quality, we have proposed pre-processing technique based on distance transformation to enhance the visibility of peripheral areas. The distance transform method aims to calculate the distance between each zero pixel and the nearest nonzero pixel in the binary images. For each pixel with the distance to the skin-line, the intensity of pixel is iteratively corrected by multiplying a propagation ratio. To evaluate the quality of processed images, the texture features were extracted using gray-level co-occurrence matrices (GLCM). And the breast density is quantitatively calculated. According to the results, the structure of breast tissues in the overexposed peripheral areas was well observed. The processed images showed more complexity and improved contrast. On the other hand, the homogeneity tended to be similar to the original images. The pixel values of peripheral areas were normalized without losing information and weighted to reduce the intensity variation. In this study, the pre-processing technique based on distance transformation was used to overcome the problem of overexposed peripheral areas in the breast images. The results demonstrated that appropriate pre-processing techniques are useful for improving image quality and accuracy of density measurement.

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Development of a prototype chest digital tomosynthesis R/F system

Digital tomosynthesis has an advantage of low radiation dose compared to conventional computed tomography (CT) by utilizing small number of projections (~80) acquired over a limited angular range. It can produce 3D volumetric data although they may have some artifacts due to incomplete sampling. Based upon these attractive merits, we developed a prototype digital tomosynthesis R/F system especially for the purpose of applications in chest imaging. Prototype chest digital tomosynthesis (CDT) R/F system contains an X-ray tube with high power R/F pulse generator, flat-panel detector, R/F table, electromechanical radiographic subsystems including precise motor controller, and a reconstruction server. For image reconstruction, users could select the reconstruction option between analytic and iterative methods. Reconstructed images of Catphan700 and LUNGMAN phantoms clearly and rapidly described the internal structures of the phantoms using graphics processing unit (GPU) programming. Contrast-to-noise ratio (CNR) values of the CTP682 module was higher in images using the simultaneous algebraic reconstruction technique (SART) than those using filtered backprojection (FBP) for all materials by factors of 2.60, 3.78, 5.50, 2.30, 3.70, and 2.52 for air, lung foam, low density polyethylene (LDPE), Delrin (acetal homopolymer resin), bone 50% (hydroxyapatite), and Teflon, respectively. Total elapsed times for producing 3D volume were 2.92 sec and 86.29 sec on average for FBP and SART (20 iterations), respectively. The times required for reconstruction were clinically feasible. Moreover, the total radiation dose from the system (5.68 mGy) could demonstrate a significant lowered radiation dose compared to conventional chest CT scan. Consequently, our prototype tomosynthesis R/F system represents an important advance in digital tomosynthesis applications.

Evaluation of effective detective quantum efficiency considering breast thickness and glandularity in prototype digital breast tomosynthesis system

Digital breast tomosynthesis (DBT) system is a novel imaging modality which is strongly depended on the performance of a detector. Recently, effective detective quantum efficiency (eDQE) has been introduced to solve the disadvantages of conventional DQE evaluations which do not consider clinical operating conditions. For eDQE evaluation, the variety of patient breast, especially the glandularity and thickness needs to be studied to consider different races of patient. For these reasons, eDQE in a prototype DBT system considering different breast thickness and glandularity was evaluated. In this study, we used the prototype DBT system with CsI(Tl) scintillator/CMOS flat panel digital detector developed by Korea Electrotechnology Research Institute (KERI). A scatter fraction, a transmission factor, an effective modulation transfer function (eMTF) and an effective normalized noise power spectrum (eNNPS) were measured in different thickness and glandularity of breast equivalent phantom. As results, scatter fraction increased and transmission fraction decreased by a factor of 2.09 and 6.25, respectively, as increasing glandularity and thickness. We also found that the breast phantom with small thickness presented high eMTF and low eNNPS. As results, eDQE from 4 cm thick breast phantom with 30% and 70% glandularity showed small changes from 0.20 to 0.19 at 0.1 mm-1, whereas eDQE from 50% glandularity of 3 cm and 5 cm presented relatively significant increase from 0.16 to 0.20 at 0.1 mm-1 spatial frequency. These indicated that eDQE was strongly affected by phantom thickness, but the effect of glandularity seemed to be trivial. According to our study, the whole system evaluation considering the races of patients from standard to abnormal cases is needed to be studied in future works.

Performance evaluation of algebraic reconstruction technique (ART) for prototype chest digital tomosynthesis (CDT) system

Chest digital tomosynthesis (CDT) is a new 3D imaging technique that can be expected to improve the detection of subtle lung disease over conventional chest radiography. Algorithm development for CDT system is challenging in that a limited number of low-dose projections are acquired over a limited angular range. To confirm the feasibility of algebraic reconstruction technique (ART) method under variations in key imaging parameters, quality metrics were conducted using LUNGMAN phantom included grand-glass opacity (GGO) tumor. Reconstructed images were acquired from the total 41 projection images over a total angular range of ±20°. We evaluated contrast-to-noise ratio (CNR) and artifacts spread function (ASF) to investigate the effect of reconstruction parameters such as number of iterations, relaxation parameter and initial guess on image quality. We found that proper value of ART relaxation parameter could improve image quality from the same projection. In this study, proper value of relaxation parameters for zero-image (ZI) and back-projection (BP) initial guesses were 0.4 and 0.6, respectively. Also, the maximum CNR values and the minimum full width at half maximum (FWHM) of ASF were acquired in the reconstructed images after 20 iterations and 3 iterations, respectively. According to the results, BP initial guess for ART method could provide better image quality than ZI initial guess. In conclusion, ART method with proper reconstruction parameters could improve image quality due to the limited angular range in CDT system.

GPU-accelerated compressed-sensing (CS) image reconstruction in chest digital tomosynthesis (CDT) using CUDA programming

A compressed-sensing (CS) technique has been rapidly applied in medical imaging field for retrieving volumetric data from highly under-sampled projections. Among many variant forms, CS technique based on a total-variation (TV) regularization strategy shows fairly reasonable results in cone-beam geometry. In this study, we implemented the TV-based CS image reconstruction strategy in our prototype chest digital tomosynthesis (CDT) R/F system. Due to the iterative nature of time consuming processes in solving a cost function, we took advantage of parallel computing using graphics processing units (GPU) by the compute unified device architecture (CUDA) programming to accelerate our algorithm. In order to compare the algorithmic performance of our proposed CS algorithm, conventional filtered back-projection (FBP) and simultaneous algebraic reconstruction technique (SART) reconstruction schemes were also studied. The results indicated that the CS produced better contrast-to-noise ratios (CNRs) in the physical phantom images (Teflon region-of-interest) by factors of 3.91 and 1.93 than FBP and SART images, respectively. The resulted human chest phantom images including lung nodules with different diameters also showed better visual appearance in the CS images. Our proposed GPU-accelerated CS reconstruction scheme could produce volumetric data up to 80 times than CPU programming. Total elapsed time for producing 50 coronal planes with 1024×1024 image matrix using 41 projection views were 216.74 seconds for proposed CS algorithms on our GPU programming, which could match the clinically feasible time (~ 3 min). Consequently, our results demonstrated that the proposed CS method showed a potential of additional dose reduction in digital tomosynthesis with reasonable image quality in a fast time.