A novel estimation method of water-equivalent thicknesses of secondary particle tracks using secondary electron bremsstrahlung emitted from therapeutic ion beams for attenuation correction

Monitoring methods of therapeutic beams based on the measurement of secondary particles have been proposed and studied worldwide. Since secondary particles are influenced by substances in the human body before they reach detectors, estimation methods of the water-equivalent thickness of the tracks of the secondary particles are required for attenuation correction. In this work, we studied a novel estimation method of the water-equivalent thickness based on the observation of energy distribution of secondary electron bremsstrahlung (SEB) emitted from the trajectories of the therapeutic beams by Monte Carlo simulations. A 12C-ion beam with an energy of 290 MeV/u was injected into a water phantom. The phantom was surrounded by a collimator having a CdTe detector behind the collimator. A distribution of energy deposition due to SEB emission was recorded during beam injection. The simulation was iterated by varying the radius of the phantom. The simulation results were plotted as an R1 vs. R2 scatter plot, where R1 was defined as the ratio of the yield having a deposition energy between 30 and 40 keV to that between 20 and 30 keV, and R2 was defined as the ratio of the yield having a deposition energy between 60 and 70 keV to that between 20 and 30 keV. We found that clusters are formed at different positions on the scatter plot by different radii of the phantom and that the water-equivalent thickness of the secondary particle tracks could be estimated using the R1 vs. R2 plot

Compton imaging with 99mTc for human imaging

We have been developing a medical imaging system using a Compton camera and demonstrated the imaging ability of Compton camera for Tc-DMSA accumulated in rat kidneys. In this study, we performed imaging experiments using a human body phantom to confirm its applicability to human imaging. Preliminary simulations were conducted using a digital phantom with varying activity ratios between the kidney and body trunk regions. Gamma rays (141 keV) were generated and detected by a Compton camera based on a silicon and cadmium telluride (Si/CdTe) detector. Compton images were reconstructed with the list mode median root prior expectation maximization method. The appropriate number of iterations of the condition was confirmed through simulations. The reconstructed Compton images revealed two bright points in the kidney regions. Furthermore, the numerical value calculated by integrating pixel values inside the region of interest correlated well with the activity of the kidney regions. Finally, experimental studies were conducted to ascertain whether the results of the simulation studies could be reproduced. The kidneys could be successfully visualised. In conclusion, considering that the conditions in this study agree with those of typical human bodies and imaginable experimental setup, the Si/CdTe Compton camera has a high probability of success in human imaging. In addition, our results indicate the capability of (semi-) quantitative analysis using Compton images

Improvement of Reconstruction Algorithm for Compton Camera

A Compton camera is a device for imaging a radio-source distribution without using a mechanical collimator. Ordered-subset Expectation-Maximization (OS-EM) is widely used to reconstruct Compton images. However, the OS-EM algorithms tend to over-concentrate and amplify noise in the reconstructed image. It is necessary to optimize the number of iterations to develop high-quality images, but it is difficult. Thus, we introduce a median root prior expectation-maximization (MRP-EM) algorithm to overcome this problem. We evaluated the quality of images reconstructed by the proposed method and compared it with that by conventional algorithms using mathematical phantoms. The spatial resolution was estimated using the images of two point sources. In addition, reproducibility was evaluated on an ellipsoidal phantom. MRP-EM reduces the generated noise and is robust with respect to the number of iterations. An evaluation of the reconstructed image quality using some statistical indices shows that our proposed method delivers better results than conventional techniques.2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (2019 IEEE-NSS-MIC

Improved iterative reconstruction method for Compton imaging using median filter.

A Compton camera is a device for imaging a radio-source distribution without using a mechanical collimator. Ordered-subset expectation-maximization (OS-EM) is widely used to reconstruct Compton images. However, the OS-EM algorithm tends to over-concentrate and amplify noise in the reconstructed image. It is, thus, necessary to optimize the number of iterations to develop high-quality images, but this has not yet been achieved. In this paper, we apply a median filter to an OS-EM algorithm and introduce a median root prior expectation-maximization (MRP-EM) algorithm to overcome this problem. In MRP-EM, the median filter is used to update the image in each iteration. We evaluated the quality of images reconstructed by our proposed method and compared them with those reconstructed by conventional algorithms using mathematical phantoms. The spatial resolution was estimated using the images of two point sources. Reproducibility was evaluated on an ellipsoidal phantom by calculating the residual sum of squares, zero-mean normalized cross-correlation, and mutual information. In addition, we evaluated the semi-quantitative performance and uniformity on the ellipsoidal phantom. MRP-EM reduces the generated noise and is robust with respect to the number of iterations. An evaluation of the reconstructed image quality using some statistical indices shows that our proposed method delivers better results than conventional techniques

Improved Iterative Reconstruction Method for Compton Imaging using Median Filter

A Compton camera is a device for imaging a radio-source distribution without using a mechanical collimator. Ordered-subset expectation-maximization (OS-EM) is widely used to reconstruct Compton images. However, the OS-EM algorithm tends to over-concentrate and amplify noise in the reconstructed image. It is, thus, necessary to optimize the number of iterations to develop high-quality images, but this has not yet been achieved. In this paper, we apply a median filter to an OS-EM algorithm and introduce a median root prior expectation-maximization (MRP-EM) algorithm to overcome this problem. In MRP-EM, the median filter is used to update the image in each iteration. We evaluated the quality of images reconstructed by our proposed method and compared them with those reconstructed by conventional algorithms using mathematical phantoms. The spatial resolution was estimated using the images of two point sources. Reproducibility was evaluated on an ellipsoidal phantom by calculating the residual sum of squares, zero-mean normalized cross-correlation, and mutual information. In addition, we evaluated the semi-quantitative performance and uniformity on the ellipsoidal phantom. MRP-EM reduces the generated noise and is robust with respect to the number of iterations. An evaluation of the reconstructed image quality using some statistical indices shows that our proposed method delivers better results than conventional techniques