Adaptive Compressed Tomography Sensing

Barkan, Oren; Weill, Jonathan; Averbuch, Amir; Dekel, Shai

doi:10.1109/cvpr.2013.285

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…In this work, the projection lines number are minimised while the angles are kept untouched. The Adaptive Tomography Acquisition (ATA) [23] algorithm tends to adaptably take the samples of the line projections associated with the object edges. At the start the image is not reconstructed and the algorithm tries to reconstruct it.…”

Section: Computed Tomography Imaging Speeding Upmentioning

confidence: 99%

Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals

Malczewski

2020

Algorithms

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One of the most challenging aspects of medical modalities such as Computed Tomography (CT) as well hybrid techniques such as CT/PET (Computed Tomography/Positron emission tomography) and PET/MRI is finding a balance between examination time, radiation dose, and image quality. The need for a dense sampling grid is associated with two major factors: image resolution enhancement, which leads to a strengthening of human perception, and image features interpretation. All these aspects make an unsupervised image processing much easier. The presented algorithm employs super-resolution-reconstruction with high accuracy motion fields’ estimation at its core for Computed Tomography/Positron Emission Tomography (CT/PET) images enhancement. The suggested method starts with processing compressively sensed input signals. This paper shows that it is possible to achieve higher image resolution while keeping the same radiation dose. The purpose of this paper is to propose a highly effective CT/PET image reconstruction strategy, allowing for simultaneous resolution enhancing and scanning time minimisation. The algorithm aims to overcome two major obstacles—image resolution limitation and algorithm reconstruction time efficiency-by combining a highly-sparse Ridgelet analysis based sampling pattern as well as PET signal sensing with super-resolution (SR) image enhancement. Due to the diverse nature of Computed Tomography, the applied Ridgelet analysis arguing its usability turned out to be efficient in reducing acquisition times in regard to maintaining satisfying scan quality. This paper presents a super-resolution image enhancement algorithm designed for handling highly sensitively compressed hybrid CT/PET scanners raw data. The presented technique allows for improving image resolution while reducing motion artefacts and keeping scanning times at pretty low levels.

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Section: Computed Tomography Imaging Speeding Upmentioning

confidence: 99%

Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals

Malczewski

2020

Algorithms

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“…Compressed sensing [202][203][204][205][206] is the efficient reconstruction of a signal from a subset of measurements. Applications include faster medical imaging [207][208][209] , image compression 210,211 , increasing image resolution 212,213 , lower medical radiation exposure [214][215][216] , and low-light vision 217,218 . In electron microscopy, compressed sensing has enabled electron beam exposure and scan time to be decreased by 10-100× with minimal information loss 200,201 .…”

Section: Compressed Sensingmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

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“…We note that the number of slices, slice spacing, and voxel resolution of different CT images are generally different, which can cause big diversities of CT images. Moreover, 3D CT images are susceptible to a number of artifacts, such as patient movement [5], representation method [6] and radiation dose [7]. To achieve the best performance, we focus on the face area in CT, which occupies the majority of CT images, especially in head and neck CT.…”

Section: Introductionmentioning

confidence: 99%

Human Recognition Using Face in Computed Tomography

Zhu¹,

Han²,

Zhou³

2020

Preprint

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With the mushrooming use of computed tomography (CT) images in clinical decision making, management of CT data becomes increasingly difficult. From the patient identification perspective, using the standard DICOM tag to track patient information is challenged by issues such as misspelling, lost file, site variation, etc. In this paper, we explore the feasibility of leveraging the faces in 3D CT images as biometric features. Specifically, we propose an automatic processing pipeline that first detects facial landmarks in 3D for ROI extraction and then generates aligned 2D depth images, which are used for automatic recognition. To boost the recognition performance, we employ transfer learning to reduce the data sparsity issue and to introduce a group sampling strategy to increase inter-class discrimination when training the recognition network. Our proposed method is capable of capturing underlying identity characteristics in medical images while reducing memory consumption. To test its effectiveness, we curate 600 3D CT images of 280 patients from multiple sources for performance evaluation. Experimental results demonstrate that our method achieves a 1:56 identification accuracy of 92.53% and a 1:1 verification accuracy of 96.12%, outperforming other competing approaches.

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Adaptive Compressed Tomography Sensing

Cited by 13 publications

References 16 publications

Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals

Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals

Review: Deep Learning in Electron Microscopy

Human Recognition Using Face in Computed Tomography

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