Abstract-Nyquist folding receiver (NYFR) is a new kind of interception architecture, which can simultaneously intercept wideband signals in multi-Nyquist zones with one or two analog-to-digital converters (ADCs). A parameter estimation algorithm of the linear frequency modulated (LFM) signal intercepted by an improved NYFR is presented. Firstly, the NYFR is improved by introducing a synchronous mechanism, and we denote this structure as a synchronous NYFR (SNYFR). Secondly, taking LFM as an example, the input and output noise distributions of an SNYFR are discussed. Then, a fast parameter estimation algorithm is derived from the frequency spectrum of the output signal, and an advice for the design of local oscillator signal is given. Simulations show that the parameter estimation accuracy is close to the maximum likelihood when the signal to noise ratio (SNR) is above −3 dB.

Purpose and background: The magnetic resonance (MR)-only radiotherapy workflow is urged by the increasing use of MR image for the identification and delineation of tumors, while a fast generation of synthetic computer tomography (sCT) image from MR image for dose calculation remains one of the key challenges to the workflow. This study aimed to develop a neural network to generate the sCT in brain site and evaluate the dosimetry accuracy. Materials and methods: A generative adversarial network (GAN) was developed to translate T1-weighted MRI to sCT. First, the "U-net" shaped encoder-decoder network with some image translation-specific modifications was trained to generate sCT, then the discriminator network was adversarially trained to distinguish between synthetic and real CT images. We enrolled 37 brain cancer patients acquiring both CT and MRI for treatment position simulation. Twenty-seven pairs of 2D T1weighted MR images and rigidly registered CT image were used to train the GAN model, and the remaining 10 pairs were used to evaluate the model performance through the metric of mean absolute error. Furthermore, the clinical Volume Modulated Arc Therapy plan was calculated on both sCT and real CT, followed by gamma analysis and comparison of dose-volume histogram. Results: On average, only 15 s were needed to generate one sCT from one T1weighted MRI. The mean absolute error between synthetic and real CT was 60.52 AE 13.32 Housefield Unit over 5-fold cross validation. For dose distribution on sCT and CT, the average pass rates of gamma analysis using the 3%/3 mm and 2%/ 2 mm criteria were 99.76% and 97.25% over testing patients, respectively. For parameters of dose-volume histogram for both target and organs at risk, no significant differences were found between both plans. Conclusion: The GAN model can generate synthetic CT from one single MRI sequence within seconds, and a state-of-art accuracy of CT number and dosimetry was achieved.

Postmastectomy radiation therapy is technically difficult and can be considered one of the most complex techniques concerning patient setup reproducibility. Slight patient setup variationsparticularly when high-conformal treatment techniques are usedcan adversely affect the accuracy of the delivered dose and the patient outcome. This research aims to investigate the inter-fraction setup variations occurring in two different scenarios of clinical practice: at the reference and at the current patient setups, when an image-guided system is used or not used, respectively.The results were used with the secondary aim of assessing the robustness of the patient setup procedure in use. Forty eight patients treated with volumetric modulated arc and intensity modulated therapies were included in this study. EPID-based in vivo dosimetry (IVD) was performed at the reference setup concomitantly with the weekly cone beam computed tomography acquisition and during the daily current setup. Three indices were analyzed: the ratio R between the reconstructed and planned isocenter doses, γ% and the mean value of γ from a transit dosimetry based on a two-dimensional γ-analysis of the electronic portal images using 5% and 5 mm as dose difference and distance to agreement gamma criteria; they were considered in tolerance if R was within 5%, γ% > 90% and γ mean < 0.4. One thousand and sixteen EPID-based IVD were analyzed and 6.3% resulted out of the tolerance level.Setup errors represented the main cause of this off tolerance with an occurrence rate of 72.2%. The percentage of results out of tolerance obtained at the current setup was three times greater (9.5% vs 3.1%) than the one obtained at the reference setup, indicating weaknesses in the setup procedure. This study highlights an EPID-based IVD system's utility in the radiotherapy routine as part of the patient's treatment quality controls and to optimize (or confirm) the performed setup procedures' accuracy. K E Y W O R D Sbreast radiotherapy, in vivo dosimetry, volumetric modulated arc therapy ---

Colorectal cancer is the third most common type of cancer, with high morbidity and mortality rates. In particular, locally advanced rectal cancer (LARC) is difficult to treat and has a high recurrence rate. Neoadjuvant chemoradiotherapy (NCRT) is one of the standard treatment programs of LARC. If the response to treatment and prognosis in patients with LARC can be predicted, it will guide clinical decision-making. Radiomics is characterized by the extraction of high-dimensional quantitative features from medical imaging data, followed by data analysis and model construction, which can be used for tumor diagnosis, staging, prediction of treatment response and prognosis. In recent years, a number of studies have assessed the role of radiomics in NCRT for LARC. MRI-based radiomics provides valuable data and is expected to become an imaging biomarker for predicting treatment response and prognosis. The potential of radiomics to guide personalized medicine is widely recognized; however, current limitations and challenges prevent its application to clinical decision-making. The present review summarizes the applications, limitations and prospects of MRI-based radiomics in LARC.

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Purpose: Motivated by recent advances in deep learning, the purpose of this study was to investigate a deep learning method in automatic segment and reconstruct applicators in computed tomography (CT) images for cervix brachytherapy treatment planning.Material and methods: U-Net model was developed for applicator segmentation in CT images. Sixty cervical cancer patients with Fletcher applicator were divided into training data and validation data according to ratio of 50 : 10, and another 10 patients with Fletcher applicator were employed to test the model. Dice similarity coefficient (DSC) and 95 th percentile Hausdorff distance (HD95) were used to evaluate the model. Segmented applicator coordinates were calculated and applied into RT structure file. Tip error and shaft error of applicators were evaluated. Dosimetric differences between manual reconstruction and deep learning-based reconstruction were compared.Results: The averaged overall 10 test patients' DSC, HD95, and reconstruction time were 0.89, 1.66 mm, and 17.12 s, respectively. The average tip error was 0.80 mm, and the average shaft error was less than 0.50 mm. The dosimetric differences between manual reconstruction and automatic reconstruction were 0.29% for high-risk clinical target volume (HR-CTV) D 90% , and less than 2.64% for organs at risk D 2cc at a scenario of doubled maximum shaft error.Conclusions: We proposed a deep learning-based reconstruction method to localize Fletcher applicator in threedimensional CT images. The achieved accuracy and efficiency confirmed our method as clinically attractive. It paves the way for the automation of brachytherapy treatment planning.

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