Monte Carlo computer simulation of a camera system for proton beam range verification in cancer treatment

Sun, X.; Wang, Hui; Li, Xinke; Cao, Guohong; Kuang, Yu; Zhang, Xiaochen

doi:10.1016/j.future.2019.09.011

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…In this figure, each diode symbol represents a SiPM. Position detection has also been performed using multiple cameras, algorithms to determine the center of gravity of signals spread across the pixels, Compton scattering, and time of flight measurements, [215][216][217][218][219][220]. In many cases these approaches involve significant use of signal processing software and thus approaches that utilize microprocessors on the same chip as the SiPM or with 3D integration can potentially offer improved readout speed and throughput.…”

Section: Position Sensingmentioning

confidence: 99%

A Comprehensive Survey of Readout Strategies for SiPMs Used in Nuclear Imaging Systems

2021

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Silicon photomultipliers (SiPMs) offer advantages such as lower relative cost, smaller size, and lower operating voltages compared to photomultiplier tubes. A SiPM’s readout circuit topology can significantly affect the characteristics of an imaging array. In nuclear imaging and detection, energy, timing, and position are the primary characteristics of interest. Nuclear imaging has applications in the medical, astronomy, and high energy physics fields, making SiPMs an active research area. This work is focused on the circuit topologies required for nuclear imaging. We surveyed the readout strategies including the front end preamplification topology choices of transimpedance amplifier, charge amplifier, and voltage amplifier. In addition, a review of circuit topologies suitable for energy, timing, and position information extraction was performed along with a summary of performance limitations and current challenges.

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Section: Position Sensingmentioning

confidence: 99%

A Comprehensive Survey of Readout Strategies for SiPMs Used in Nuclear Imaging Systems

2021

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“…Given the favorite properties of the CZT photon counting detector (PCD), including high energy resolution, high intrinsic spatial resolution, and compact detector size, this type of detector has been increasingly studied for multiple medical imaging modalities. [13][14][15][16][17] Based on the previous studies, we designed the tri-modal OBI system using a single CZT-based PCD mounted on the Linacs. The new OBI adds additional dimensions to address complicated tumor changes during the treatment course of RT for ART procedures.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo study to investigate the feasibility of an on‐board SPECT/spectral‐CT/CBCT imager for medical linear accelerator

et al. 2020

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The on-board flat-panel cone-beam computed tomography (CBCT) lacks molecular/functional information for current online image-guided radiation therapy (IGRT). It might not be adequate for adaptive radiation therapy (ART), particularly for biologically guided tumor delineation and targeting which might be shifted and/or distorted during the course of RT. A linear accelerator (Linac) gantry-mounted on-board imager (OBI) was proposed using a single photon counting detector (PCD) panel to achieve single photon emission computed tomography (SPECT), energy-resolved spectral CT, and conventional CBCT triple on-board imaging, which might facilitate online ART with an addition of volumetric molecular/functional imaging information. Methods: The system was designed and evaluated in the GATE Monte Carlo platform. The OBI system including a kV-beam source and a pixelated cadmium zinc telluride (CZT) detector panel mounted on a medical Linac orthogonally to the MV beam direction was designed to obtain online CBCT, spectral CT, and SPECT tri-modal imaging of patients in the treatment room. The spatial resolutions of the OBI system were determined by imaging simulated phantoms. The CBCT imaging was evaluated by a simulated contrast phantom. A PMMA phantom containing gadolinium was imaged to demonstrate quantitative imaging of spectral-CT/CBCT of the system. The capability of tri-modal imaging of the OBI was demonstrated using three different spectral CT imaging methods to differentiate gadolinium, gold, calcium within simulated PMMA and the SPECT to image radioactive 99m Tc distribution. The dual-isotope SPECT imaging of the system was also evaluated by imaging a phantom containing 99m Tc and 123 I. The radiotherapy-related parameters of iodine contrast fraction and virtual non-contrast (VNC) tissue electron density in the Kidney1 inserts of a simulated phantom were decomposed using the Bayesian eigentissue decomposition method for contrastenhanced CBCT/spectral-CT of the OBI in a single scan. Results: The spatial resolutions of CBCT and SPECT of the OBI were determined to be 15.1 lp/cm at 10% MTF and 4.8-12 mm for radii of rotation of 10-40 cm, respectively. In CBCT image of the contrast phantom, most of the soft-tissue inserts were visible with sufficient spatial structure details. As compared to the CBCT image of gadolinium, the spectral CT image provided higher image contrasts. Calcium, gadolinium, and gold were separated well by using the spectral CT material imaging methods. The reconstructed distribution of 99m Tc agreed with the spatial position within the phantom. The two isotopes were separated from each other in dual-isotope SPECT imaging of the OBI. The iodine fractions and the VNC electron densities were estimated in the iodine-enhanced Kidney1 tissue inserts with reasonable RMS errors. The main procedures of the tri-modal imaging guided online ART workflow were presented with new functional features included. Conclusions: Using a single photon counting CZT detector panel, an on-board SPECT, spectral CT, and CBCT tr...

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Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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Monte Carlo computer simulation of a camera system for proton beam range verification in cancer treatment

Cited by 5 publications

References 39 publications

A Comprehensive Survey of Readout Strategies for SiPMs Used in Nuclear Imaging Systems

A Comprehensive Survey of Readout Strategies for SiPMs Used in Nuclear Imaging Systems

A Monte Carlo study to investigate the feasibility of an on‐board SPECT/spectral‐CT/CBCT imager for medical linear accelerator

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

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