The detection of microcalcification on mammograms is known as the most important feature. Microcalcifications are difficult to distinguish because they have low contrast in mammography due to size and breast density. The purpose of this study was to evaluate the feasibility of virtual monochromatic image (VMI) for quantitative assessment of the appropriate energy region for detection of malignant microcalcifications. The photon-counting spectral mammography system was modeled using Geant4 Application for Tomographic Emission (GATE) simulation tools. The breast phantom used a 50/50 ratio of adipose/glandular tissue and microcalcifications used calcium hydroxyapatite (Ca5(PO4)3(OH)), which is mainly malignant. Microcalcifications with various sizes ranging from 150 μm to 550 μm were embedded into the breast phantom. In this study, projection based VMI was used. This study quantitatively evaluates the appropriate energy region in terms of image quality using VMI technique. The results showed that VM images were optimized at an energy range of approximately 26 to 27 keV. In order to verify the usefulness of the results obtained from the VM images, the CNR was evaluated according to the microcalcification size using the bin images obtained by setting various energy thresholds based on the photon-counting detector. Compared to the results of VM images, the results of bin images showed a similar tendency. In this study, we investigated the optimum energy of monochromatic images for breast diagnostic applications. By setting the optimal energy range using VMI, we can identify microcalcifications better in mammography and expect to reduce the frequency of additional examination.
Sparse view - computed tomography (CT) for low dose and photon counting detector (PCD) for spectral imaging have been studied for improvement of image quality and quantification in medical imaging. The sparse view–CT can reduce dose, but there is a limitation that cannot be completely restored yet and PCD with physical phenomena such as charge sharing, K-escape and material characteristic can be difficult to material quantification due to different distribution of noise characteristics in a specific energy band. In this study, we propose a deep running-based wavelet-CNN for the efficient reduction of physical factors such as noise and streak artifact generated by fusion of sparse view-CT and PCD. The physical phenomena of the spatio-energetic cross-talks were reflected in PCD. We obtained images with a total of four energy thresholds with limited angles and trained through the proposed method. The proposed method was evaluated for the image quality by the peak signal to noise ratio (PSNR), the normalized mean square error (NMSE), the structural similarity (SSIM), the multi-scale SSIM (MS-SSIM), and the feature similarity (FSIM). The experimental results demonstrated that the sparse view-CT with PCD using proposed deep running structure effectively removes the streak artifacts and improves the image quality.
Many studies have shown that iterative reconstruction (IR) algorithm is possible to make the tube current and/or voltage in CT imaging lower without a major loss of image quality. However, there are not many studies on the acquisition conditions for low dose CT images using IR algorithm to achieve the same image quality as routine dose images using FBP algorithm. The aim of this study was to investigate the image quality of low dose CT images obtained with IR algorithm. Images were reconstructed with filtered back projection (FBP) and iDose4 hybrid IR algorithm (Philips Healthcare, Cleveland, OH). CTDIvol for routine protocol and low dose protocol were 5.2 mGy, and 2 mGy, respectively. Images were quantitatively assessed through Hounsfield unit (HU), noise power spectrum (NPS) and contrast to noise ratio (CNR). The results showed that image quality of iDose4 algorithm was improved than that of FBP algorithm. When the same low-dose protocol is used, the IR algorithm provided improved imaging performance compared with the FBP algorithm, and also demonstrated that IR algorithm provides potential for maintaining or improving image quality with much less radiation dose than FBP algorithm with routine dose.
Polychromatic X-ray in computed tomography (CT) can cause metal artifacts and beam hardening artifacts, which are limiting factors in the detection and diagnosis of lesions. Several groups have introduced virtual monochromatic imaging (VMI) techniques using dual-source CT to reduce these artifacts. However, the dual-source system with two exposures can increase the patient dose. The photon-counting detector with one exposure can replace a dual-source system. In this study, we investigated the feasibility of VMI in a photon-counting system. A prototype of the photon-counting CT system, which has 64 line-pixels Cadmium Zinc Telluride (CZT)-based photon-counting detector, was used. The source-to-detector distance and the source-to-center of rotation distance were 1,400 and 1,200 mm, respectively. Energy bins were set at 23 - 32, 33 - 42, 43 - 52, 53 - 62, and 63 - 90 keV. For comparison, the integrating mode was obtained by sum of five energy bins, which is assumed to polychromatic X-ray. Two copper (Cu) rods were inserted into PMMA cylinder phantom. As results, the VMI effectively removed metal artifacts. Noise and Signal-to-noise ratio (SNR) were evaluated and the optimal VMI was measured at 77 keV. Our results indicated that VMI in the prototype of the photon-counting system effectively eliminates the metal artifact and provides better image quality than integrating mode at 23 - 90 keV.
Region-of-interest (ROI) imaging is considered an effective method to reduce the exposure dose. We propose ROIbased beam modulation acquisition to restore the information outside of the ROI. The CT system and 3D voxelized abdominal phantom were simulated using the MATLAB R2017b program. A total of 360 projections were obtained and used for CT reconstruction with a filtered back projection (FBP) algorithm. Beam modulation CT images were reconstructed using 288 truncated and 72 full projections. An interpolation method and our proposed method based on a projection onto convex sets (POCS) algorithm corrected the truncated projections. The image quality of three ROIs was evaluated using the structural similarity index measure (SSIM). The reconstructed image obtained by beam modulation acquisition resulted in a much higher SSIM value for the external information than that obtained by the ROI scan. The proposed method based on a POCS algorithm provides the best image quality in beam modulation acquisition. In conclusion, we have verified the possibility of restoring the ROI external information using beam modulation acquisition.
Dual-energy (DE) technology is useful in chest radiography because it can separate anatomical structures such as bone and soft tissue. The standard log subtraction (SLS), simple smoothing of the high-energy image (SSH), anti-correlated noise reduction (ACNR), and a general linear noise reduction algorithm (GLNR) are used as conventional DE techniques to separate bone and soft tissue. However, conventional DE techniques cannot accurately decompose the anatomical structures because these techniques are based on the assumptions that X-ray imaging is a linear relationship. This relationship can cause quantum noise as well as anatomical loss of normal tissue and difficulty in detecting lesions. In this study, we propose a non-linear DE technique which requires a step to calculate the coefficient in advance using a calibration phantom. The calibration phantom composed to aluminum and PMMA material to calculate non-linear coefficients using the quadratic fitting model for soft tissue and bone. The results demonstrated that a non-linear DE technique showed the higher contrast-to-noise ratio (CNR), signal to noise ratio (SNR) and figure of merit (FOM) at 60 /70 kVp and 130 kVp. In addition, it showed better performance and image quality than conventional DE technique in terms of material decomposition capability. In conclusion, a non-linear DE technique is expected to increase the diagnostic accuracy in chest radiography.
The purpose of this study was to evaluate the feasibility of spectral mammography using the dual-energy method to noninvasively distinguish between type I (calcium oxalate, CO) and type II (calcium hydroxyapatite, HA) microcalcifications. Two types of microcalcifications are difficult to distinguish due to a similar linear attenuation coefficient. In order to improve the detection efficiency of microcalcifications, we used the photon counting detector with energy discrimination capability and microcalcifications were classified into optimal energy bins. Two energy bins were used to obtain dualenergy images. In this study, photon counting spectral mammography system was simulated using Geant4 Application for Tomographic Emission (GATE) simulation tools. The thickness of the breast phantom was 3 cm and microcalcifications of various sizes ranging from 130-550 μm were embedded into the breast phantom. Microcalcifications were classified as being calcium hydroxyapatite or calcium oxalate based on score calculation with the dual-energy images. According to the results, the measured CNR of calcium hydroxyapatite (HA) was higher than that of the calcium oxalate (CO) in conventional single-energy image. In addition, two types of microcalcifications were distinguished using dual-energy analysis method. This classification represents better performance with a high energy of 50 kVp and an energy threshold of 30 keV. These results indicate that the classification performance was improved when the difference in the low energy image and high energy image was used. This study demonstrated the feasibility of photon counting spectral mammography for classification of breast microcalcifications. We expect that dual-energy method can reduce the frequency of biopsy and discriminate microcalcifications in mammography. These results are expected to potentially improve the efficiency of early breast cancer diagnosis.
Contrast enhanced digital mammography (CEDM) using dual energy technique has been studied due to its ability of emphasizing breast cancer. However, when using CEDM the patient dose and the toxicity of iodine should be considered. A photon counting detector (PCD), which has the ability of energy discrimination, has been regarded as an alternative technique to resolve the problem of excessive patient dose. The purpose of this study was to confirm the feasibility of CEDM based on the PCD by using a projection-based energy weighting technique. We used Geant4 Application for Tomographic Emission (GATE) version 6.0. We simulated two different types of PCD which were constructed with silicon (Si) and cadmium zinc telluride (CZT). Each inner cylinder filled with four iodine with different low concentrations and thicknesses in cylindrical shape of breast phantom. For comparison, we acquired a convention integrating mode image and five bin images based on PCD system by projection-based weighting technique. The results demonstrated that CEDM based on the PCD significantly improved contrast to noise ratio (CNR) compared to conventional integrating mode. As a result of applying the dual energy technique to the projection-based weighing image, the CNR of low concentration iodine was improved. In conclusion, the CEDM based on PCD with projection-based weighting technique has improved a detection capability of low concentration iodine than integrating mode.
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.
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.
Spectral computed tomography (SCT) is a promising technique for obtaining enhanced image with contrast agent and distinguishing different materials. We focused on developing the analytic reconstruction algorithm in material decomposition technique with lower radiation exposure and shorter acquisition time. Sparse-angular sampling can reduce patient dose and scanning time for obtaining the reconstruction images. In this study, the sinogram interpolation method was used to improve the quality of material decomposed images in sparse angular sampling. A prototype of spectral CT system with 64 pixels CZT-based photon counting detector was used. The source-to-detector distance and the source-tocenter of rotation distance were 1200 and 1015 mm, respectively. The x-ray spectrum at 90 kVp with a tube current of 110 μA was used. Two energy bins (23-33 keV and 34-44 keV) were set to obtain the two images for decomposed iodine and calcification. We used PMMA phantom and its height and radius were 50 mm and 17.5 mm, respectively. The phantom contained 4 materials including iodine, gadolinium, calcification, and liquid state lipid. We evaluated the signal to noise ratio (SNR) of materials to examine the significance of sinogram interpolation method. The decomposed iodine and calcification images were obtained by projection based subtraction method using two energy bins with 36 projection data. The SNR in decomposed images were improved by using sinogram interpolation method. And these results indicated that the signal of decomposed material was increased and the noise of decomposed material was reduced. In conclusion, the sinogram interpolation method can be used in material decomposition method with sparse-angular sampling.
KEYWORDS: Photon counting, Dual energy imaging, Sensors, Breast, Imaging systems, Iodine, Mammography, Monte Carlo methods, Windows, Signal attenuation, Tissues, X-rays
The photon counting detector with energy discrimination capabilities provides the spectral information and energy of each photon with single exposure. The energy-resolved photon counting detector makes it possible to improve the visualization of contrast agent by selecting the appropriate energy window. In this study, we simulated the photon counting spectral mammography system using a Monte Carlo method and compared three contrast enhancement methods (K-edge imaging, projection-based energy weighting imaging, and dual energy subtraction imaging). For the quantitative comparison, we used the homogeneous cylindrical breast phantom as a reference and the heterogeneous XCAT breast phantom. To evaluate the K-edge imaging methods, we obtained images by increasing the energy window width based on K-edge absorption energy of iodine. The iodine which has the K-edge discontinuity in the attenuation coefficient curve can be separated from the background. The projection-based energy weighting factor was defined as the difference in the transmissions between the contrast agent and the background. Each weighting factor as a function of photon energy was calculated and applied to the each energy bin. For the dual energy subtraction imaging, we acquired two images with below and above the iodine K-edge energy using single exposure. To suppress the breast tissue in high energy images, the weighting factor was applied as the ratio of the linear attenuation coefficients of the breast tissue at high and low energies. Our results demonstrated the CNR improvement of the K-edge imaging was the highest among the three methods. These imaging techniques based on the energy-resolved photon counting detector improved image quality with the spectral information.
Chest digital tomosynthesis (CDT) is a recently introduced new imaging modality for better detection of high- and smallcontrast lung nodules compared to conventional X-ray radiography. In CDT system, several projection views need to be acquired with limited angular range. The acquisition of insufficient number of projection data can degrade the reconstructed image quality. This image degradation easily affected by acquisition parameters such as angular dose distribution, number of projection views and reconstruction algorithm. To investigate the imaging characteristics, we evaluated the impact of the angular dose distribution on image quality by simulation studies with Geant4 Application for Tomographic Emission (GATE). We designed the different angular dose distribution conditions. The results showed that the contrast-to-noise ratio (CNR) improves when exposed the higher dose at central projection views than peripheral views. While it was found that increasing angular dose distribution at central views improved lung nodule detectability, although both peripheral regions slightly suffer from image noise due to low dose distribution. The improvements of CNR by using proposed image acquisition technique suggest possible directions for further improvement of CDT system for lung nodule detection with high quality imaging capabilities.
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