Due to the inherent limitation of MRI in directly capturing early metabolic abnormalities associated with neurological disorders, and considering the high cost and radiation risks associated with PET scans, cross-modal MRI-to-PET image synthesis has emerged as a critical pathway for early and precise diagnosis. However, current methods generally suffer from structural distortion, blurred details, and computational inefficiencies, significantly restricting their clinical applicability. To address these limitations, this paper proposes an innovative multi-scale autoregressive-driven framework for MRI-to-PET cross-modal image generation. By explicitly modeling scalewise transformations between MRI and PET via a multi-scale autoregressive mechanism, and incorporating wavelet transform with a linear multi-step connection strategy, our framework effectively enhances structural accuracy and texture detail expression, especially in lesion regions. Experimental results on the ADNI Alzheimer's Disease dataset and a private epilepsy dataset demonstrate that the proposed method consistently outperforms state-of-the-art approaches, generating high-quality PET images efficiently and robustly. Furthermore, it substantially reduces diagnostic costs and radiation exposure, showcasing promising prospects for clinical adoption. Our source code is available at https://github.com/Guanyu-Zhou/WARPNet.

-weighted (T1w) anatomical MRI data, which may be unacquired, corrupted by subject motion or hardware failure, or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled "DeepAnat"), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or assist the co-registration using synthesized T1w images. The quantitative and systematic evaluations using data of 60 young subjects provided by the Human Connectome Project (HCP) show that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w data. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by directly co-registering the diffusion and T1w images using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications.

Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image processing pipeline that transforms the sensor data into a form, that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. We explain a method that combines machine learning and image systems simulation that automates the pipeline design. The approach is based on a new way of thinking of the image processing pipeline as a large collection of local linear filters. We illustrate how the method has been used to design pipelines for novel sensor architectures in consumer photography applications.

This paper presents a very fast local tone mapping method for displaying high dynamic range (HDR) images. Though local tone mapping operators produce better local contrast and details, they are usually slow. We have solved this problem by designing a highly parallel algorithm, which can be easily implemented on a Graphics Processing Unit (GPU) to harvest high computational efficiency. At the same time, the proposed method mimics the local adaption mechanism of the human visual system and thus gives good results for a wide variety of images.

In this paper, we consider the design of a network-coded cooperation (NetCC) transmission scheme for free-space optical (FSO) two-way relay networks (TWRN). The achieved network-coded spatial diversity gain can effectively enhance the FSO communication quality over strong turbulence channels. Different from traditional assumption of perfect channel state information (CSI) prior known at destination, we develop an optimal bit detection method based on Bayesian estimate for receiving nodes in the absence of CSI. We then derive the closed-form algorithm for it by taking advantage of Meijer's G-function characterized by gamma-gamma strong turbulence model. Numerical simulations confirm that the proposed NetCC relay scheme has significant BER performance improvement compared with that of direct transmission scheme. In addition, we also investigate the impact of turbulence level with various scintillation indices on the NetCC scheme.