Segmentation of the left atrial appendage based on fusion attention.

In clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for LAA closure procedures. The morphology determination is influenced by the segmentation results. The LAA occupies only a small part of the entire 3D medical image, and the segmentation results are more likely to be biased towards the background region, making the segmentation of the LAA challenging. In this paper, we propose a lightweight attention mechanism called fusion attention, which imitates human visual behavior. We process the 3D image of the LAA using a method that involves overview observation followed by detailed observation. In the overview observation stage, the image features are pooled along the three dimensions of length, width, and height. The obtained features from the three dimensions are then separately input into the spatial attention and channel attention modules to learn the regions of interest. In the detailed observation stage, the attention results from the previous stage are fused using element-wise multiplication and combined with the original feature map to enhance feature learning. The fusion attention mechanism was evaluated on a left atrial appendage dataset provided by Liaoning Provincial People's Hospital, resulting in an average Dice coefficient of 0.8855. The results indicate that the fusion attention mechanism achieves better segmentation results on 3D images compared to existing lightweight attention mechanisms.

Deep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images.

CT metal artefact reduction (MAR) methods based on supervised deep learning are often troubled by domain gap between simulated training dataset and real-application dataset, i.e., methods trained on simulation cannot generalize well to practical data. Unsupervised MAR methods can be trained directly on practical data, but they learn MAR with indirect metrics and often perform unsatisfactorily. To tackle the domain gap problem, we propose a novel MAR method called UDAMAR based on unsupervised domain adaptation (UDA). Specifically, we introduce a UDA regularization loss into a typical image-domain supervised MAR method, which mitigates the domain discrepancy between simulated and practical artefacts by feature-space alignment. Our adversarial-based UDA focuses on a low-level feature space where the domain difference of metal artefacts mainly lies. UDAMAR can simultaneously learn MAR from simulated data with known labels and extract critical information from unlabeled practical data. Experiments on both clinical dental and torso datasets show the superiority of UDAMAR by outperforming its supervised backbone and two state-of-the-art unsupervised methods. We carefully analyze UDAMAR by both experiments on simulated metal artefacts and various ablation studies. On simulation, its close performance to the supervised methods and advantages over the unsupervised methods justify its efficacy. Ablation studies on the influence from the weight of UDA regularization loss, UDA feature layers, and the amount of practical data used for training further demonstrate the robustness of UDAMAR. UDAMAR provides a simple and clean design and is easy to implement. These advantages make it a very feasible solution for practical CT MAR.

Method of sparse-view coded-aperture x-ray diffraction tomography.

Objective.X-ray diffraction (XRD) has been considered as a valuable diagnostic technology providing material specific 'finger-print' information i.e. XRD pattern to distinguish different biological tissues. XRD tomography (XRDT) further obtains spatial-resolved XRD pattern distribution, which has become a frontier biological sample inspection method. Currently, XRD computed tomography (XRD-CT) featured by the conventional CT scan mode with rotation has the best spatial resolution among various XRDT methods, but its scan process takes hours. Meanwhile, snapshot XRDT methods such as coded-aperture XRDT (CA-XRDT) aim at direct imaging without scan movements. With compressed-sensing acquisition applied, CA-XRDT significantly shortens data acquisition time. However, the snapshot acquisition results in a significant drop in spatial resolution. Hence, we need an advanced XRDT method that significantly accelerates XRD-CT acquisition and still maintains an acceptable imaging accuracy for biological sample inspection.Approach.Inspired by the high spatial resolution of XRD-CT from rotational scan and the fast compressed-sensing acquisition in snapshot CA-XRDT (SnapshotCA-XRDT), we proposed a new XRDT imaging method: sparse-view rotational CA-XRDT (RotationCA-XRDT). It takes SnapshotCA-XRDT as a preliminary depth-resolved XRDT method, and combines rotational scan to significantly improve the spatial resolution. A model-based iterative reconstruction (MBIR) method is adopted for RotationCA-XRDT. Moreover, we suggest a refined system model calculation for the RotationCA-XRDT MBIR which is a key factor to improve reconstruction image quality.Main results.We conducted our experimental validation based on Monte-Carlo simulation for a breast sample. The results show that the proposed RotationCA-XRDT method succeeded in producing good images for detecting 2 mm square carcinoma with a 15-view scan. The spatial resolution is significantly improved from current SnapshotCA-XRDT methods. With our refined system model, MBIR can obtain high quality images with little artifacts.Significance.In this work, we proposed a new high spatial resolution XRDT method combining coded-aperture compressed-sensing acquisition and sparse-view scan. The proposed RotationCA-XRDT method obtained significantly better image resolution than current SnapshotCA-XRDT methods in the field. It is of great potential for biological sample XRDT inspection. The proposed RotationCA-XRDT is the fastest millimetre-resolution XRDT method in the field which reduces the scan time from hours to minutes.

Reciprocal-FDK reconstruction for x-ray diffraction computed tomography.

Objective.X-ray diffraction (XRD) technology uses x-ray small-angle scattering signal for material analysis, which is highly sensitive to material inter-molecular structure. To meet the high spatial resolution requirement in applications such as medical imaging, XRD computed tomography (XRDCT) has been proposed to provide XRD intensity with improved spatial resolution from point-wise XRD scan. In XRDCT, 2D spatial tomography corresponds to a 3D reconstruction problem with the third dimension being the XRD spectrum dimension, i.e. the momentum transfer dimension. Current works in the field have studied reconstruction methods for either angular-dispersive XRDCT or energy-dispersive XRDCT for small samples. The approximations used are only suitable for regions near the XRDCT iso-center. A new XRDCT reconstruction method is needed for more general imaging applications.Approach.We derive a new FDK-type reconstruction method (Reciprocal-FDK) for XRDCT without limitation on object size. By introducing a set of reciprocal variables, the XRDCT model is transformed into a classical cone-parallel CT model, which is an extension of a circular-trajectory cone-beam CT model, after which the FDK method is applied for XRDCT reconstruction.Main results.Both analytical simulation and Monte Carlo simulation experiments are conducted to validate the XRDCT reconstruction method. The results show that when compared to existing analytical reconstruction methods, there are improvements in the proposed Reciprocal-FDK method with regard to relative structure reconstruction and XRD pattern peak reconstruction. Since cone-parallel CT does not satisfy the data completeness condition, cone-angle effect affects the reconstruction accuracy of XRDCT. The property of cone-angle effect in XRDCT is also analyzed with ablation studies.Significance.We propose a general analytical reconstruction method for XRDCT without constraint on object size. Reciprocal-FDK provides a complete derivation and theoretical support for XRDCT reconstruction by analogy to the well-studied cone-parallel CT model. In addition, the intrinsic problem with the XRDCT data model and the corresponding reconstruction error are discussed for the first time.

A CT image feature space (CTIS) loss for restoration with deep learning-based methods.

Objective.Deep learning-based methods have been widely used in medical imaging field such as detection, segmentation and image restoration. For supervised learning methods in CT image restoration, different loss functions will lead to different image qualities which may affect clinical diagnosis. In this paper, to compare commonly used loss functions and give a better alternative, we studied a widely generalizable framework for loss functions which are defined in the feature space extracted by neural networks.Approach.For the purpose of incorporating prior knowledge, a CT image feature space (CTIS) loss was proposed, which learned the feature space from high quality CT images by an autoencoder. In the absence of high-quality CT images, an alternate loss function, random-weight (RaW) loss in the feature space of images (LoFS) was proposed. For RaW-LoFS, the feature space is defined by neural networks with random weights.Main results.In experimental studies, we used post reconstruction deep learning-based methods in the 2016 AAPM low dose CT grand challenge. Compared with perceptual loss that is widely used, our loss functions performed better both quantitatively and qualitatively. In addition, three senior radiologists were invited for subjective assessments between CTIS loss and RaW-LoFS. According to their judgements, the results of CTIS loss achieved better visual quality. Furtherly, by analyzing each channel of CTIS loss, we also proposed partially constrained CTIS loss.Significance.Our loss functions achieved favorable image quality. This framework can be easily adapted to other tasks and fields.

Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning.

PURPOSE: Metal artifact is a quite common problem in diagnostic dental computed tomography (CT) images. Due to the high attenuation of heavy materials such as metal, severe global artifacts can occur in reconstructions. Typical metal artifact reduction (MAR) techniques segment out the metal regions and estimate the corrupted projection data by various interpolation methods. However, interpolations are not accurate and introduce new artifacts or even deform the teeth in the reconstructed image. This work presents a new strategy to take advantage of the power of deep learning for metal artifact reduction. METHOD: The analysis first uses coarse reconstructions from simulated locally interpolated data affected by metal fillings as a starting point. A deep learning network is then trained using the simulated data and applied to practical data. Thus, an easily implemented three-step MAR method is formed: Firstly, use the acquired projection data to create a preliminary image reconstruction with linearly interpolated data for the metal-related projections. Secondly, a deep learning network is used to remove the artifacts from the linear interpolation and recover the nonmetal region information. Thirdly, the method adds the ROI reconstruction of the metal regions. The structures behind the shading artifacts in the direct filtered back-projection (FBP) reconstruction can be partially recovered by interpolation-based MAR (I-MAR) with the network further correcting for interpolation errors. The key to this method is that the linear interpolation reconstruction errors can be easily simulated to train a network and the effectiveness of the network can be easily generalized to I-MAR results in real situations. RESULTS: We trained a network with a simulation dataset and validated the network against a separate simulation dataset. Then, the network was tested using simulation data that did not overlap with the training/validation datasets and real patient datasets. Both tests gave encouraging results with accurate tooth structure recovery and few artifacts. The relative root mean square error and structure similarity index method indexes were significantly improved in the tests. The method was also evaluated by two experienced dentists who gave positive evaluations. CONCLUSIONS: This work presents a strategy to build a transferable learning from simulations to practical systems for metal artifact reduction using a supervised deep learning method. The system transforms the MAR analyses to an interpolation-artifact reduction problem to recover structural details from the coarse interpolation reconstruction. In this way, training data from simulations with ground truth labels can easily model the similar features in real data with I-MAR as the bridge. The network can seamlessly optimize both simulations and real data. The whole method is easily implemented with little computational cost. Test results demonstrated that this is an effective MAR method applicable to practical dental CT systems.

Assessment of 16S rRNA gene primers for studying bacterial community structure and function of aging flue-cured tobaccos.

Selection of optimal primer pairs in 16S rRNA gene sequencing is a pivotal issue in microorganism diversity analysis. However, limited effort has been put into investigation of specific primer sets for analysis of the bacterial diversity of aging flue-cured tobaccos (AFTs), as well as prediction of the function of the bacterial community. In this study, the performance of four primer pairs in determining bacterial community structure based on 16S rRNA gene sequences in AFTs was assessed, and the functions of genes were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). Results revealed that the primer set 799F-1193R covering the amplification region V5V6V7 gave a more accurate picture of the bacterial community structure of AFTs, with lower co-amplification levels of chloroplast and mitochondrial genes, and more genera covered than when using the other primers. In addition, functional gene prediction suggested that the microbiome of AFTs was involved in kinds of interested pathways. A high abundance of functional genes involved in nitrogen metabolism was detected in AFTs, reflecting a high level of bacteria involved in degrading harmful nitrogen compounds and generating nitrogenous nutrients for others. Additionally, the functional genes involved in biosynthesis of valuable metabolites and degradation of toxic compounds provided information that the AFTs possess a huge library of microorganisms and genes that could be applied to further studies. All of these findings provide a significance reference for researchers working on the bacterial diversity assessment of tobacco-related samples.