The synthesis of computed tomography images can supplement electron density information and eliminate MR-CT image registration errors. Consequently, an increasing number of MR-to-CT image translation approaches are being proposed for MR-only radiotherapy planning. However, due to substantial anatomical differences between various regions, traditional approaches often require each model to undergo independent development and use. In this paper, we propose a unified model driven by prompts that dynamically adapt to the different anatomical regions and generates CT images with high structural consistency. Specifically, it utilizes a region-specific attention mechanism, including a region-aware vector and a dynamic gating factor, to achieve MRI-to-CT image translation for multiple anatomical regions. Qualitative and quantitative results on three datasets of anatomical parts demonstrate that our models generate clearer and more anatomically detailed CT images than other state-of-the-art translation models. The results of the dosimetric analysis also indicate that our proposed model generates images with dose distributions more closely aligned to those of the real CT images. Thus, the proposed model demonstrates promising potential for enabling MR-only radiotherapy across multiple anatomical regions. we have released the source code for our RSAM model. The repository is accessible to the public at: https://github.com/yhyumi123/RSAM.

Accurate segmentation of breast lesions is vital for diagnosing breast diseases. Masked image modeling (MIM) with random masking performs well in self-supervised learning but struggles in breast ultrasound segmentation due to (1) ambiguous representations from similar intensities near lesion boundaries and (2) a bias toward irrelevant regions. We propose UA-MAE, an uncertainty-aware masked autoencoder that uses pixel-wise uncertainty maps to dynamically select masking patches, prioritizing boundaries and morphologically relevant lesion areas. Experiments on two public datasets for pre-training and three for fine-tuning show UA-MAE outperforming four state-of-theart SSL methods and two supervised approaches in segmentation accuracy across diverse breast ultrasound images. The code is available at https://github.com/yXiangXiong/UA-MAE.

Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets.

Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing aortic dissection (AD). However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non-contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, we propose a novel cascaded multi-task generative framework for AD detection using NCE-CT volumes. The framework includes a 3D nnU-Net and a 3D multi-task generative architecture (3D MTGA). Specifically, the 3D nnU-Net was employed to segment aortas from NCE-CT volumes. The 3D MTGA was then employed to simultaneously synthesize CE-CT volumes, segment true & false lumen, and classify the patient as AD or non-AD. A theoretical formulation demonstrated that the 3D MTGA could increase the Jensen-Shannon Divergence (JSD) between AD and non-AD for each NCE-CT volume, thus indirectly improving the AD detection performance. Experiments also showed that the proposed framework could achieve an average accuracy of 0.831, a sensitivity of 0.938, and an F1-score of 0.847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively. The experimental results indicate that the proposed framework obtains superior performance to state-of-the-art models in AD detection. Thus, it has great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice. The source codes and supplementary materials for our framework are available at https://github.com/yXiangXiong/CMTGF.

High-precision parameter estimation is crucial for micromotion feature acquisitions. A method of high-precision range estimation based on phase of wideband radar echo is proposed in this paper. After the proposed processing, unwrapped signal phases without any ambiguity can be obtained to calculate the relative range of the target. No constraint condition for phase ambiguity resolution is imposed on the proposed method, which means that it could still work well in low signal-to-noise ratio situations. Cramér-Rao lower bound for the root-mean-squared error of the range estimation is derived in analytical expression. Results of several simulations and experiment with FEKO generated data are presented to show the effectiveness and antinoise performance of the proposed method.

Vibration or rotation on a target or any structure of a target might induce a frequency modulation on the returned radar signal, which is called micro-Doppler effect, and is widely used for many of the remote sensing applications such as target detection, classification, and recognition. However, the micro-Doppler frequency could be large and it may cause micro-Doppler ambiguities which are undesirable in these applications. In this paper, we propose a novel and flexible method to resolve the micro-Doppler ambiguities based on the time-delayed cross correlation processing of wideband radar echoes for range-spread targets. Variable-sized shrinkage ratio can be obtained to reduce the micro-Doppler frequency of the signal to an appropriate numerical value, and the micro-Doppler ambiguities can be resolved in different situations. The effectiveness of the proposed method is validated by simulations and real data experiments.