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.
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.
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.
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.
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.
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.
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