One of the main concerns regarding medical imaging is the danger tissue’s ionizing due to the applied radiation. Many medical procedures are based on this ionizing radiation (such as X-rays and Gamma radiation). This radiation allows the physician to perform diagnosis inside the human body. Still, the main concern is stochastic effects to the DNA, particularly the cause of cancer. The radiation dose endangers not only the patient but also the medical staff, who might be close to the patient and be exposed to this dangerous radiation in a daily manner. This paper presents a novel concept of radiation reduced Computed Tomography (CT) scans. The proposed concept includes two main methods: minification to enhance the energy concertation per pixel and subpixel resolution enhancement, using shifted images, to preserve resolution. The proposed process uses several pinhole masks as the base of the imaging modality. The proposed concept was validated numerically and experimentally and has demonstrated the capability of reducing the radiation efficiency by factor 4, being highly significant to the world of radiology and CT scans. This dose reduction allows a safer imaging process for the patient and the medical staff. This method simplifies the system and improves the obtained image quality. The proposed method can contribute additively to standard existing dose reduction or super-resolution techniques to achieve even better performance.

https://www.mdpi.com/1424-8220/20/22/6551/pdf

Reduction in Irradiation Dose in Aperture Coded Enhanced Computed Tomography Imager Using Super-Resolution Techniques.

Digital image devices have been widely applied in many fields, such as individual recognition and remote sensing. The captured image is a degraded image from the latent observation, where the degradation processing is affected by some factors, such as lighting and noise corruption. Specifically, noise is generated in the processing of transmission and compression from the unknown latent observation. Thus, it is essential to use image denoising techniques to remove noise and recover the latent observation from the given degraded image. In this research, a supervised encoder–decoder convolution neural network was used to fix image distortion stemming from the limited accuracy of inverse filter methods (Wiener filter, Lucy–Richardson deconvolution, etc.). Particularly, we will correct image degradation that mainly stems from duplications arising from multiple-pinhole array imaging.

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Signal-to-Noise Ratio Improvement for Multiple-Pinhole Imaging Using Supervised Encoder–Decoder Convolutional Neural Network Architecture

Portable radiation detectors are widely used in environmental radiation detection and medical imaging due to their portability feature, high detection efficiency, and large field of view. Lutetium-yttrium oxyorthosilicate (LYSO) is a widely used scintillator in gamma radiation detection. However, the structure and the arrangement of scintillators limit the sensitivity and detection accuracy of these radiation detectors. In this study, a novel portable sensor based on a monolithic LYSO ring was developed for the detection of environmental radiation through simulation, followed by construction and assessments. Monte Carlo simulations were utilized to prove the detection of gamma rays at 511 keV by the developed sensor. The simulations data, including energy resolutions, decoding errors, and sensitivity, showed good potential for the detection of gamma rays by the as-obtained sensor. The experimental results using the VA method revealed decoding errors in the energy window width of 50 keV less than 2°. The average error was estimated at 0.67°, a sufficient value for the detection of gamma radiation. In sum, the proposed radiation sensor appears promising for the construction of high-performance radiation detectors and systems.

A Novel Portable Gamma Radiation Sensor Based on a Monolithic Lutetium-Yttrium Oxyorthosilicate Ring.

The following paper proposes a combination of a supervised encoder-decoder neural network with coded apertures. Coded apertures provide improved sensitivity and signal-to-noise ratio (SNR) in planar images. The unique array design of this method overcomes the spatial frequency cutoff found in standard multi-pinhole arrays. In this design, the pinholes were positioned to minimize loss in spatial frequencies. The large number of pinholes results in significant overlapping on the detector. To overcome the overlapping issue, reconstruction of the object from the obtained image is done using inverse filtering methods. However, traces of duplications remain leading to a decline in SNR, contrast, and resolution. The proposed technique addresses the challenge of image distortion caused by the lack of accuracy in the inverse filter methods, by using a deep neural network. In this work, the coded aperture is combined with a deep convolutional neural network (CNN) to remove noise caused by pinhole imaging and inverse filter limitations. Compared to only using Wiener filtering, the proposed method delivers higher SNR, contrast, and resolution. The imaging system is presented in detail with experimental results that illustrate its efficiency.

Deep learning method for pinhole array color image reconstruction.

Single photon emission computed tomography (SPECT) and positron emission tomography (PET) are established medical imaging modalities that have been implemented for decades, but improvements in detector design and camera electronics are needed for advancement of both imaging technologies. Detectors are arguably the most important aspect of the systems. Similar to SPECT, PET typically relies on indirect conversion of gamma radiation via scintillators coupled with photosensors used to convert optical photons produced by the scintillator into an electrical signal. PET detectors are defined by their energy resolution, timing resolution, and spatial resolution, all of which affect and determine the image quality. Improvements in energy resolution have been shown by increasing the brightness of the scintillator utilizing materials like cerium bromide (CeBr3) or switching to a direct conversion detector, such as cadmium zinc telluride (CZT) or thallium bromide (TlBr). Timing resolution for PET is a focal point of the current research. Improving the timing resolution improves the signal-to-noise of the PET system and is integral to the implementation of time-of-flight PET. By utilizing novel configurations, such as side readouts on scintillators, timing resolution has been improved dramatically. Similarly, metascintillators, which use complex combinations for the scintillator material, have also shown improvements to the timing resolution. Additional research has focused on using Cherenkov light emission in scintillators to further improve the timing resolution. Other research is focused on using convolutional neural networks and other signal processing to enhance timing resolution. Lastly, aside from acollinearity and positron range, spatial resolution is impacted by the PET detector, therefore improving the intrinsic spatial resolution of the detector will allow for smaller features to be imaged. One method for improving the spatial resolution is to use unique configurations with layered scintillators. Additionally, monolithic scintillators have also been shown to have reduced spatial resolution. The future for both SPECT and PET image system advancement will depend on continued development of the detectors via many different pathways including materials, signal processing, physics, and novel configurations. In this review article, we will discuss challenges and emerging technologies for state-of-the-art radiation detectors utilized in PET and SPECT.

State-of-the-art challenges and emerging technologies in radiation detection for nuclear medicine imaging: A review

Biomedical planar imaging using gamma radiation is a very important screening tool for medical diagnostics. Since lens imaging is not available in gamma imaging, the current methods use lead collimator or pinhole techniques to perform imaging. However, due to ineffective utilization of the gamma radiation emitted from the patient’s body and the radioactive dose limit in patients, poor image signal to noise ratio (SNR) and long image capturing time are evident. Furthermore, the resolution is related to the pinhole diameter, thus there is a tradeoff between SNR and resolution. Our objectives are to reduce the radioactive dose given to the patient and to preserve or improve SNR, resolution and capturing time while incorporating three-dimensional capabilities in existing gamma imaging systems. The proposed imaging system is based on super-resolved time-multiplexing methods using both variable and moving pinhole arrays. Simulations were performed both in MATLAB and GEANT4, and gamma single photon emission computed tomography (SPECT) experiments were conducted to support theory and simulations. The proposed method is able to reduce the radioactive dose and image capturing time and to improve SNR and resolution. The results and method enhance the gamma imaging capabilities that exist in current systems, while providing three-dimensional data on the object.

https://www.mdpi.com/1424-8220/20/11/3013/pdf

Gamma Radiation Imaging System via Variable and Time-Multiplexed Pinhole Arrays.

Validation and optimization of lensless radiation imaging technique for gamma and x-ray systems based on variable coded aperture is presented. The system can also provide depth information and 3D images with improved SNR and sensitivity.

Hemy Avraham

Papers

Gamma Radiation Imaging System via Variable and Time-Multiplexed Pinhole Arrays.

Time Multiplexed Pinholes Array based Imaging in the Gamma and X-ray Spectral Range