14 research outputs found
Noise2Inverse: Self-supervised deep convolutional denoising for tomography
Recovering a high-quality image from noisy indirect measurements is an
important problem with many applications. For such inverse problems, supervised
deep convolutional neural network (CNN)-based denoising methods have shown
strong results, but the success of these supervised methods critically depends
on the availability of a high-quality training dataset of similar measurements.
For image denoising, methods are available that enable training without a
separate training dataset by assuming that the noise in two different pixels is
uncorrelated. However, this assumption does not hold for inverse problems,
resulting in artifacts in the denoised images produced by existing methods.
Here, we propose Noise2Inverse, a deep CNN-based denoising method for linear
image reconstruction algorithms that does not require any additional clean or
noisy data. Training a CNN-based denoiser is enabled by exploiting the noise
model to compute multiple statistically independent reconstructions. We develop
a theoretical framework which shows that such training indeed obtains a
denoising CNN, assuming the measured noise is element-wise independent and
zero-mean. On simulated CT datasets, Noise2Inverse demonstrates an improvement
in peak signal-to-noise ratio and structural similarity index compared to
state-of-the-art image denoising methods and conventional reconstruction
methods, such as Total-Variation Minimization. We also demonstrate that the
method is able to significantly reduce noise in challenging real-world
experimental datasets.Comment: This paper appears in: IEEE Transactions on Computational Imaging On
page(s): 1320-1335 Print ISSN: 2333-9403 Online ISSN: 2333-9403 Digital
Object Identifier: 10.1109/TCI.2020.301964
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography
At X-ray beamlines of synchrotron light sources, the achievable
time-resolution for 3D tomographic imaging of the interior of an object has
been reduced to a fraction of a second, enabling rapidly changing structures to
be examined. The associated data acquisition rates require sizable
computational resources for reconstruction. Therefore, full 3D reconstruction
of the object is usually performed after the scan has completed. Quasi-3D
reconstruction -- where several interactive 2D slices are computed instead of a
3D volume -- has been shown to be significantly more efficient, and can enable
the real-time reconstruction and visualization of the interior. However,
quasi-3D reconstruction relies on filtered backprojection type algorithms,
which are typically sensitive to measurement noise. To overcome this issue, we
propose Noise2Filter, a learned filter method that can be trained using only
the measured data, and does not require any additional training data. This
method combines quasi-3D reconstruction, learned filters, and self-supervised
learning to derive a tomographic reconstruction method that can be trained in
under a minute and evaluated in real-time. We show limited loss of accuracy
compared to training with additional training data, and improved accuracy
compared to standard filter-based methods
Prototyping X-ray tomographic reconstruction pipelines with FleXbox
Computer Tomography (CT) scanners for research applications are often designed to facilitate flexible acquisition geometries. Making full use of such CT scanners requires advanced reconstruction software that can (i) deal with a broad range of geometrical scanning settings, (ii) allows for customization of processing algorithms, and (iii) has the capability to process large amounts of data. FleXbox is a Python-based tomographic reconstruction toolbox focused on these three functionalities. It is built to bridge the gap between low-level tomographic reconstruction packages (e.g. ASTRA toolbox) and high-level distributed systems (e.g. Livermore Tomography Tools). FleXbox allows to model arbitrary source, detector and object trajectories. The modular architecture of FleXbox allows to design an optimal reconstruction approach for a single CT dataset. When multiple datasets of an object are acquired (either different spatial regions or different snapshots in time), they can be combined into a larger high resolution volume or a time series of volumes. The software allows to then create a computational reconstruction pipeline that can run without user interaction and enables efficient computation on large-scale 3D volumes on a single workstation
Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python
Tomography is a powerful tool for reconstructing the interior of an object from a series of projection images. Typically, the source and detector traverse a standard path (e.g., circular, helical). Recently, various techniques have emerged that use more complex acquisition geometries. Current software packages require significant handwork, or lack the flexibility to handle such geometries. Therefore, software is needed that can concisely represent, visualize, and compute reconstructions of complex acquisition geometries. We present tomosipo, a Python package that provides these capabilities in a concise and intuitive way. Case studies demonstrate the power and flexibility of tomosipo
Evaluation of whole genome sequencing for outbreak detection of Salmonella enterica
Salmonella enterica is a common cause of minor and large food borne outbreaks. To achieve successful and nearly 'real-time' monitoring and identification of outbreaks, reliable sub-typing is essential. Whole genome sequencing (WGS) shows great promises for using as a routine epidemiological typing tool. Here we evaluate WGS for typing of S. Typhimurium including different approaches for analyzing and comparing the data. A collection of 34 S. Typhimurium isolates was sequenced. This consisted of 18 isolates from six outbreaks and 16 epidemiologically unrelated background strains. In addition, 8 S. Enteritidis and 5 S. Derby were also sequenced and used for comparison. A number of different bioinformatics approaches were applied on the data; including pan-genome tree, k-mer tree, nucleotide difference tree and SNP tree. The outcome of each approach was evaluated in relation to the association of the isolates to specific outbreaks. The pan-genome tree clustered 65% of the S. Typhimurium isolates according to the pre-defined epidemiology, the k-mer tree 88%, the nucleotide difference tree 100% and the SNP tree 100% of the strains within S. Typhimurium. The resulting outcome of the four phylogenetic analyses were also compared to PFGE revealing that WGS typing achieved the greater performance than the traditional method. In conclusion, for S. Typhimurium, SNP analysis and nucleotide difference approach of WGS data seem to be the superior methods for epidemiological typing compared to other phylogenetic analytic approaches that may be used on WGS. These approaches were also superior to the more classical typing method, PFGE. Our study also indicates that WGS alone is insufficient to determine whether strains are related or un-related to outbreaks. This still requires the combination of epidemiological data and whole genome sequencing results
Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python
Tomography is a powerful tool for reconstructing the interior of an object from a series of projection images. Typically, the source and detector traverse a standard path (e.g., circular, helical). Recently, various techniques have emerged that use more complex acquisition geometries. Current software packages require significant handwork, or lack the flexibility to handle such geometries. Therefore, software is needed that can concisely represent, visualize, and compute reconstructions of complex acquisition geometries. We present tomosipo, a Python package that provides these capabilities in a concise and intuitive way. Case studies demonstrate the power and flexibility of tomosipo
Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Convolutional neural networks (CNNs) have emerged as a powerful tool to remove noise from reconstructed images. However, their training typically requires collecting a dataset of paired noisy and high-quality measurements, which is a major obstacle to their use in practice. To circumvent this problem, methods for CNN-based denoising have recently been proposed that require no separate training data beyond the already available noisy reconstructions. Among these, the Noise2Inverse method is specifically designed for tomography and related inverse problems. To date, applications of Noise2Inverse have only taken into account 2D spatial information. In this paper, we expand the application of Noise2Inverse in space, time, and spectrum-like domains. This development enhances applications to static and dynamic micro-tomography as well as X-ray diffraction tomography. Results on real-world datasets establish that Noise2Inverse is capable of accurate denoising and enables a substantial reduction in acquisition time while maintaining image quality.ISSN:2045-232