Development of novel low-cost readout electronics for large field-of-view gamma camera detectors.

PURPOSE: Large scintillation crystals-based gamma cameras play a crucial role in nuclear medicine imaging. In this study, a large field-of-view (FOV) gamma detector consisting of 48 square PMTs developed using a new readout electronics, reducing 48 (6 × 8) analog signals to 14 (6 + 8) analog sums of each row and column, with reduced complexity and cost while preserving image quality. METHODS: All 14 analog signals were converted to digital signals using AD9257 high-speed analog to digital (ADC) converters driven by the SPARTAN-6 family of field-programmable gate arrays (FPGA) in order to calculate the signal integrals. The positioning algorithm was based on the digital correlated signal enhancement (CSE) algorithm implemented in the acquisition software. The performance characteristics of the developed gamma camera were measured using the NEMA NU 1-2018 standards. RESULTS: The measured energy resolution of the developed detector was 8.7 % at 140 keV, with an intrinsic spatial resolution of 3.9 mm. The uniformity was within 0.6 %, while the linearity was within 0.1 %. CONCLUSION: The performance evaluation demonstrated that the developed detector has suitable specifications for high-end nuclear medicine imaging.

HER2GAN: Overcome the Scarcity of HER2 Breast Cancer Dataset Based on Transfer Learning and GAN Model.

INTRODUCTION: Immunohistochemistry (IHC) is crucial for breast cancer diagnosis, classification, and individualized treatment. IHC is used to measure the levels of expression of hormone receptors (estrogen and progesterone receptors), human epidermal growth factor receptor 2 (HER2), and other biomarkers, which are used to make treatment decisions and predict how well a patient will do. The evaluation of the breast cancer score on IHC slides, taking into account structural and morphological features as well as a scarcity of relevant data, is one of the most important issues in the IHC debate. Several recent studies have utilized machine learning and deep learning techniques to resolve these issues. MATERIALS AND METHODS: This paper introduces a new approach for addressing the issue based on supervised deep learning. A GAN-based model is proposed for generating high-quality HER2 images and identifying and classifying HER2 levels. Using transfer learning methodologies, the original and generated images were evaluated. RESULTS AND CONCLUSION: All of the models have been trained and evaluated using publicly accessible and private data sets, respectively. The InceptionV3 and InceptionResNetV2 models achieved a high accuracy of 93% with the combined generated and original images used for training and testing, demonstrating the exceptional quality of the details in the synthesized images.

Quantitative evaluation of CT scan images to determinate the prognosis of COVID-19 patient using deep learning.

The purpose of this research is to evaluate the accuracy of AI-assisted quantification in comparison to conventional CT parameters reviewed by a radiologist in predicting the severity, progression, and clinical outcome of disease. The current study is a cross-sectional study that was conducted on patients with the diagnosis of COVID-19 and underwent a pulmonary CT scan between August 23th, 2021 to December 21th, 2022. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), and consolidation were visually evaluated. CT severity score was calculated according to a semi-quantitative method. In addition, AI based quantification of GGO and consolidation volume were also performed. 291 patients (mean age: 64.7 ± 7; 129 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume percentage (40.6%± 11.9%versus 21.7%± 8.8%, p ˂0.001) as well as consolidation volume percentage (4.8% ± 2% versus 1.9% ± 1%, p < 0.001). Among imaging parameters, consolidation volume percentage and the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.91, p < 0.001). According to multivariate regression, consolidation volume was the strongest predictor for disease progression. In conclusion, the consolidation volume measured on the initial chest CT was the most accurate predictor of disease progression, and a larger consolidation volume was associated with a poor clinical outcome. In patients with COVID-19, AI-assisted lesion quantification was useful for risk stratification and prognosis evaluation.

Quantification of Multi-Parametric Magnetic Resonance Imaging Based on Radiomics Analysis for Differentiation of Benign and Malignant Lesions of Prostate.

Background: The most common cancer (non-cutaneous) malignancy among men is prostate cancer. Management of prostate cancer, including staging and treatment, playing an important role in decreasing mortality rates. Among all current diagnostic tools, multiparametric MRI (mp-MRI) has shown high potential in localizing and staging prostate cancer. Quantification of mp-MRI helps to decrease the dependency of diagnosis on readers' opinions. Objective: The aim of this research is to set a method based on quantification of mp-MRI images for discrimination between benign and malignant prostatic lesions with fusion-guided MR imaging/transrectal ultrasonography biopsy as a pathology validation reference. Material and Methods: It is an analytical research that 27 patients underwent the mp-MRI examination, including T1- and T2- weighted and diffusion weighted imaging (DWI). Quantification was done by calculating radiomic features from mp-MRI images. Receiver-operating-characteristic curve was done for each feature to evaluate the discriminatory capacity and linear discriminant analysis (LDA) and leave-one-out cross-validation for feature filtering to estimate the sensitivity, specificity and accuracy of the benign and malignant lesion differentiation process is used. Results: An accuracy, sensitivity and specificity of 92.6%, 95.2% and 83.3%, respectively, were achieved from a subset of radiomics features obtained from T2-weighted images and apparent diffusion coefficient (ADC) maps for distinguishing benign and malignant prostate lesions. Conclusion: Quantification of mp-MRI (T2-weighted images and ADC-maps) based on radiomics feature has potential to distinguish benign with appropriate accuracy from malignant prostate lesions. This technique is helpful in preventing needless biopsies in patients and provides an assisted diagnosis for classifications of prostate lesions.

Optimizing the Radiation Treatment Planning of Brain Tumors by Integration of Functional MRI and White Matter Tractography.

Background: Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) present the ability to selectively protect functional regions and fiber tracts of the brain when brain tumors are treated with radiotherapy. Objective: This study aimed to assess whether the incorporation of fMRI and DTI data into the radiation treatment planning process of brain tumors could prevent the neurological parts of the brain from high doses of radiation. Material and Methods: In this investigational theoretical study, the fMRI and DTI data were obtained from eight glioma patients. This patient-specific fMRI and DTI data were attained based on tumor location, the patient's general conditions, and the importance of the functional and fiber tract areas. The functional regions, fiber tracts, anatomical organs at risk, and the tumor were contoured for radiation treatment planning. Finally, the radiation treatment planning with and without fMRI & DTI information was obtained and compared. Results: The mean dose to the functional areas and the maximum doses were reduced by 25.36% and 18.57% on fMRI & DTI plans compared with the anatomical plans. In addition, 15.59% and 20.84% reductions were achieved in the mean and maximum doses of the fiber tracts, respectively. Conclusion: This study demonstrated the feasibility of using fMRI and DTI data in radiation treatment planning to maximize radiation protection of the functional cortex and fiber tracts. The mean and maximum doses significantly decreased to neurologically relevant brain regions, resulting in reducing the neuro-cognitive complications and improving the patient's quality of life.

Evaluating the Impact of Various Parameters on the Gamma Index Values of 2D Diode Array in IMRT Verification.

BACKGROUND: MapCHECK2 is a two-dimensional diode arrays planar dosimetry verification system. Dosimetric results are evaluated with gamma index. This study aims to provide comprehensive information on the impact of various factors on the gamma index values of MapCHECK2, which is mostly used for IMRT dose verification. METHODS: Seven fields were planned for 6 and 18 MV photons. The azimuthal angle is defined as any rotation of collimators or the MapCHECK2 around the central axis, which was varied from 5 to -5°. The gantry angle was changed from -8 to 8°. Isodose sampling resolution was studied in the range of 0.5 to 4 mm. The effects of additional buildup on gamma index in three cases were also assessed. Gamma test acceptance criteria were 3%/3 mm. RESULTS: The change of azimuthal angle in 5° interval reduced gamma index value by about 9%. The results of putting buildups of various thicknesses on the MapCHECK2 surface showed that gamma index was generally improved in thicker buildup, especially for 18 MV. Changing the sampling resolution from 4 to 2 mm resulted in an increase in gamma index by about 3.7%. The deviation of the gantry in 8° intervals in either directions changed the gamma index only by about 1.6% for 6 MV and 2.1% for 18 MV. CONCLUSION: Among the studied parameters, the azimuthal angle is one of the most effective factors on gamma index value. The gantry angle deviation and sampling resolution are less effective on gamma index value reduction.