Computed Tomography (CT) is one of the most used imaging modality in clinics. However, the associated high radiation is a major concern, and then the tradeoff between clinically accepted CT images and radiation dose is desired. In recent years, inspired by deep learning techniques, various deep learning-based methods have been developed for low-dose CT imaging, and they have potential to improve image quality of low dose CT. Meanwhile, most of these methods are trained on the CT datasets with the specific imaging geometry, and they could fail to successfully reconstruct the CT images from the other imaging geometries simultaneously, which might lead to poor generalization ability. In this work, to address this issue, we propose a dual-domain modulation for high-performance multi-geometry low-dose CT image reconstruction (DM-MG) method. Specific, the proposed DM-MG consists of two sub-networks, i.e., multilayer perceptron controlling the multiple imaging geometries, and inverse Radon transform approximation sub-network reconstructing the CT images from the different geometries. Moreover, the inverse Radon transform approximation sub-network contains sinogram-domain filtering module, back-projection module, and image-domain filtering module, which maps the Radon transformation to the network. Finally, the proposed DM-MG can reconstruct the CT images from the different imaging geometries and dose levels simultaneously. In this work, the CT data from 2016 NIHAAPM-Mayo Clinic Low Dose CT Grand Challenge are used to validate and evaluate the reconstruction performance of the proposed DM-MG. Experimental results demonstrate that the presented DM-MG method can produce high-quality CT images from multiple geometries simultaneously and outperform the competing method in the quantitative assessments and visual inspection show that the dual-domain modulated model we propose provides better reconstruction of low-dose CT images under different imaging geometries compared to other modulated models.

Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms.Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated.Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different.Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.

Purpose Automated measurement of spine indices on axial magnetic resonance (MR) images plays a significant role in lumbar spinal stenosis diagnosis. Existing direct spine indices measurement approaches fail to explicitly focus on the task‐specific region or feature channel with the additional information for guiding. We aim to achieve accurate spine indices measurement by introducing the guidance of the segmentation task. Methods In this paper, we propose a segmentation‐guided regression network (SGRNet) to achieve automated spine indices measurement. SGRNet consists of a segmentation path for generating the spine segmentation prediction and a regression path for producing spine indices estimation. The segmentation path is a U‐Net‐like network which includes a segmentation encoder and a decoder which generates multilevel segmentation features and segmentation prediction. The proposed segmentation‐guided attention module (SGAM) in the regression encoder extracts the attention‐aware regression feature under the guidance of the segmentation feature. Based on the attention‐aware regression feature, a fully connected layer is utilized to output the accurate spine indices estimation. Results Experiments on the open‐access Lumbar Spine MRI data set show that SGRNet achieves state‐of‐the‐art performance with a mean absolute error of 0.49 mm and mean Pearson correlation coefficient of 0.956 for four indices estimation. Conclusions The proposed SGAM in SGRNet is capable of improving the performance of spine indices measurement by focusing on the task‐specific region and feature channel under the guidance of the segmentation task.

Cardiac arrest is a fatal and urgent disease in humans. A high‐quality electrocardiogram (ECG) has a positive guide to the success of defibrillation and resuscitation. However, because of artificial motion interference and ambient noise, reliable ECG signals can be obtained only during chest compression (CC) pauses. To address this issue, the adaptive recursive least squares (RLS) denoising approach is proposed. First, the ECG signals of porcine are divided into three groups: CC, without CC, and both with and without CC. Then, five Gaussian noises with different signals‐to‐noise ratios (SNR) and five noises with different distribution types are added, respectively. Furthermore, RLS is compared with six other different denoising approaches. Experimental results demonstrate significant differences between RLS and the other six algorithms in main metrics. SNR and related factors are larger, while the root mean square error is smaller. In conclusion, RLS can significantly eliminate many types of ambient noise, and improve the quality of ECG signals during cardiopulmonary resuscitation.

Low-dose computed tomography (CT) is of great potential advantage for disease diagnosis. Usually, paired training datasets are difficult to obtain in clinical routine, which catalyzes the development of unsupervised learning techniques to improve the low-dose CT imaging. Recently, most existing unsupervised learning approaches for low-dose CT imaging were developed in the image domain, and only a few approaches have been developed in the sinogram domain, which is a challenging task. In this paper, we propose a dedicated unpaired learning technique for low-dose CT sinogram restoration with a novel data-dependent noise-generative model. The general idea is to construct a paired pseudo normal-/low-dose sinogram dataset based on the existing unpaired normal-/low-dose sinogram dataset, after which a sinogram restoration network can be obtained by training on the paired pseudo normal-/low-dose sinogram dataset. However, the difficulty of the presented idea lies in the construction of the pseudo low-dose sinogram generative network, due to the complexity of the texture feature and noise property in the sinogram domain. To address this issue, we construct an appropriative generative network architecture based on a reasonable noise-generative model in the sinogram domain, which can be used to produce pseudo low-dose sinogram data within an adversarial learning framework. To validate the proposed technique, a clinical dataset was adopted. Experimental results demonstrate that the proposed method can produce promising pseudo low-dose sinogram data, which is sufficient to train an effective sinogram restoration network. Both quantitative and qualitative measurements show that the proposed method can obtained promising low-dose CT imaging performance.