Byungdu Jo

Dosimetry in small-animal CT using Monte Carlo simulations

Small-animal computed tomography (micro-CT) imaging devices are increasingly being used in biological research. While investigators are mainly interested in high-contrast, low-noise, and high-resolution anatomical images, relatively large radiation doses are required, and there is also growing concern over the radiological risk from preclinical experiments. This study was conducted to determine the radiation dose in a mouse model for dosimetric estimates using the GEANT4 application for tomographic emission simulations (GATE) and to extend its techniques to various small-animal CT applications. Radiation dose simulations were performed with the same parameters as those for the measured micro-CT data, using the MOBY phantom, a pencil ion chamber and an electrometer with a CT detector. For physical validation of radiation dose, absorbed dose of brain and liver in mouse were evaluated to compare simulated results with physically measured data using thermoluminescent dosimeters (TLDs). The mean difference between simulated and measured data was less than 2.9% at 50 kVp X-ray source. The absorbed doses of 37 brain tissues and major organs of the mouse were evaluated according to kVp changes. The absorbed dose over all of the measurements in the brain (37 types of tissues) consistently increased and ranged from 42.4 to 104.0 mGy. Among the brain tissues, the absorbed dose of the hypothalamus (157.8–414.30 mGy) was the highest for the beams at 50–80 kVp, and that of the corpus callosum (11.2–26.6 mGy) was the lowest. These results can be used as a dosimetric database to control mouse doses and preclinical targeted radiotherapy experiments. In addition, to accurately calculate the mouse-absorbed dose, the X-ray spectrum, detector alignment, and uncertainty in the elemental composition of the simulated materials must be accurately modeled.

Scatter correction method with primary modulator for dual energy digital radiography: a preliminary study

In conventional digital radiography (DR) using a dual energy subtraction technique, a significant fraction of the detected photons are scattered within the body, resulting in the scatter component. Scattered radiation can significantly deteriorate image quality in diagnostic X-ray imaging systems. Various methods of scatter correction, including both measurement and non-measurement-based methods have been proposed in the past. Both methods can reduce scatter artifacts in images. However, non-measurement-based methods require a homogeneous object and have insufficient scatter component correction. Therefore, we employed a measurement-based method to correct for the scatter component of inhomogeneous objects from dual energy DR (DEDR) images. We performed a simulation study using a Monte Carlo simulation with a primary modulator, which is a measurement-based method for the DEDR system. The primary modulator, which has a checkerboard pattern, was used to modulate primary radiation. Cylindrical phantoms of variable size were used to quantify imaging performance. For scatter estimation, we used Discrete Fourier Transform filtering. The primary modulation method was evaluated using a cylindrical phantom in the DEDR system. The scatter components were accurately removed using a primary modulator. When the results acquired with scatter correction and without correction were compared, the average contrast-to-noise ratio (CNR) with the correction was 1.35 times higher than that obtained without correction, and the average root mean square error (RMSE) with the correction was 38.00% better than that without correction. In the subtraction study, the average CNR with correction was 2.04 (aluminum subtraction) and 1.38 (polymethyl methacrylate (PMMA) subtraction) times higher than that obtained without the correction. The analysis demonstrated the accuracy of scatter correction and the improvement of image quality using a primary modulator and showed the feasibility of introducing the primary modulation technique into dual energy subtraction. Therefore, we suggest that the scatter correction method with a primary modulator is useful for the DEDR system.

Optimal contrast enhancement achieved by the synthetic method for bone and tissue separation based on a dual-energy radiographic system

In dual-energy digital radiography (DEDR), the energy subtraction and equivalent thickness methods have been used for detecting thorax lesions. However, the image contrast of the energy subtraction method is low in comparion with that of the equivalent thickness and synthetic methods. Therefore, we applied the equivalent thickness and synthetic methods to material separation to enhance the bone and tissue contrast, and these results were compared with the results of the energy subtraction method in a chest DEDR system. The purpose of this work was to evaluate the image quality of the energy subtraction, equivalent thickness, and synthetic methods. In the energy subtraction method, the optimal weighting factors were selected for bone and tissue visualization, respectively. The equivalent thickness was obtained with a calibration procedure by using combinations of aluminum and polymethyl methacrylate (PMMA) blocks. The synthetic images were acquired with the known equation from the results of the equivalent thickness method. According to these results, the contrast-to-noise-ratio (CNR) values using the equivalent thickness and synthetic methods were improved than those obtained with the energy subtraction method in both aluminum and PMMA enhancement trial. In a cylindrical phantom study, the equivalent thickness and the synthetic method improved the contrast better than energy subtraction method. The synthetic method supplements the air shadows shown in the equivalent thickness method. We compared the enhanced images of bone and tissue with the energy subtraction, equivalent thickness, and synthetic methods. Our results showed that the effects of the synthetic method can improve the image contrast on both bone and tissue and overcome the bone shadows in tissue images in a DEDR system.

A Monte Carlo simulation study of the feasibility of a high resolution parallel-hole collimator with a CdTe pixelated semiconductor SPECT system

It is recommended that a pixelated parallel-hole collimator in which the hole and pixel sizes are equal be used to improve the sensitivity and spatial resolution when using a small pixel size and a single-photon emission computed tomography (SPECT) system with pixelated semiconductor detector materials (e.g., CdTe and CZT). However, some significant problems arise in the manufacturing of a pixelated parallel-hole collimator. Therefore, we sought to simulate a pixelated semiconductor SPECT system with various collimator geometric designs. The purpose of this study was to compare the quality of images generated with a pixelated semiconductor SPECT system simulated with pixelated parallel-hole collimators of various geometric designs. The sensitivity and spatial resolution of the various collimator geometric designs with varying septal heights and hole sizes were measured. Moreover, to evaluate the overall performance of the imaging system, a hot-rod phantom was designed using a Monte Carlo simulation. According to the results, the average sensitivity using a 15 mm septal height was 1.80, 2.87, and 4.16 times higher than that obtained with septal heights of 20, 25, and 30 mm, respectively. Also, the average spatial resolution using the 30 mm septal height was 44.33, 22.08, and 9.26% better than that attained with 15, 20, and 25 mm septal heights, respectively. When the results acquired with 0.3 and 0.6 mm hole sizes were compared, the average sensitivity with the 0.6 mm hole size was 3.97 times higher than that obtained with the 0.3 mm hole size, and the average spatial resolution with the 0.3 mm hole size was 45.76% better than that with the 0.6 mm hole size. We have presented the pixelated parallel-hole collimators of various collimator geometric designs and evaluations. Our results showed that the effect of various collimator geometric designs can be investigated by Monte Carlo simulation so as to evaluate the feasibility of a high resolution parallel-hole collimator with a CdTe pixelated semiconductor SPECT system.

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.

Improvement of material decomposition and image quality in dual-energy radiography by reducing image noise

Although digital radiography has been widely used for screening human anatomical structures in clinical situations, it has several limitations due to anatomical overlapping. To resolve this problem, dual-energy imaging techniques, which provide a method for decomposing overlying anatomical structures, have been suggested as alternative imaging techniques. Previous studies have reported several dual-energy techniques, each resulting in different image qualities. In this study, we compared three dual-energy techniques: simple log subtraction (SLS), simple smoothing of a high-energy image (SSH), and anti-correlated noise reduction (ACNR) with respect to material thickness quantification and image quality. To evaluate dual-energy radiography, we conducted Monte Carlo simulation and experimental phantom studies. The Geant 4 Application for Tomographic Emission (GATE) v 6.0 and tungsten anode spectral model using interpolation polynomials (TASMIP) codes were used for simulation studies and digital radiography, and human chest phantoms were used for experimental studies. The results of the simulation study showed improved image contrast-to-noise ratio (CNR) and coefficient of variation (COV) values and bone thickness estimation accuracy by applying the ACNR and SSH methods. Furthermore, the chest phantom images showed better image quality with the SSH and ACNR methods compared to the SLS method. In particular, the bone texture characteristics were well-described by applying the SSH and ACNR methods. In conclusion, the SSH and ACNR methods improved the accuracy of material quantification and image quality in dual-energy radiography compared to SLS. Our results can contribute to better diagnostic capabilities of dual-energy images and accurate material quantification in various clinical situations.

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.

Feasibility study of contrast enhanced digital mammography based on photon-counting detector by projection-based weighting technique: a simulation study

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.

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.

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.