Deformable registration and region-of-interest image reconstruction in sparse repeat CT scanning

2020 ◽  
Vol 28 (6) ◽  
pp. 1069-1089
Author(s):  
Zeev Adelman ◽  
Leo Joskowicz
Keyword(s):  
Image Reconstruction ◽  
Radiation Dose ◽  
Region Of Interest ◽  
Ct Scanning ◽  
Deformable Registration ◽  
Ct Scans ◽  
High Quality ◽  
X Ray ◽  
Radon Space ◽  
A Minor

BACKGROUND: Repeat CT scanning is ubiquitous in many clinical situations, e.g. to follow disease progression, to evaluate treatment efficacy, and to monitor interventional CT procedures. However, it incurs in cumulative radiation to the patient which can be significantly reduced by using a region of interest (ROI) and the existing baseline scan. OBJECTIVE: To obtain a high-quality reconstruction of a ROI with a significantly reduced X-ray radiation dosage that accounts for deformations. METHODS: We present a new method for deformable registration and image reconstruction inside an ROI in repeat CT scans with a highly reduced X-ray radiation dose based on sparse scanning. Our method uses the existing baseline scan data, a user-defined ROI, and a new sparse repeat scan to compute a high-quality repeat scan ROI image with a significantly reduced radiation dose. Our method first performs rigid registration between the densely scanned baseline and the sparsely scanned repeat CT scans followed by deformable registration with a low-order parametric model, both in 3D Radon space and without reconstructing the repeat scan image. It then reconstructs the repeat scan ROI without computing the entire repeat scan image. RESULTS: Our experimental results on clinical lung and liver CT scans yield a mean × 14 computation speedup and a × 7.6-12.5 radiation dose reduction, with a minor image quality loss of 0.0157 in the NRMSE metric. CONCLUSION: Our method is considerably faster than existing methods, thereby enabling intraoperative online repeat scanning that it is accurate and accounts for position, deformation, and structure changes at a fraction of the radiation dose required by existing methods.

scholarly journals Optimization of X-ray Investigations in Dentistry Using Optical Coherence Tomography

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4554
Author(s):  
Ralph-Alexandru Erdelyi ◽  
Virgil-Florin Duma ◽  
Cosmin Sinescu ◽  
George Mihai Dobre ◽  
Adrian Bradu ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Radiation Dose ◽  
Imaging Techniques ◽  
Image Resolution ◽  
Optical Coherence ◽  
X Rays ◽  
High Quality ◽  
X Ray

The most common imaging technique for dental diagnoses and treatment monitoring is X-ray imaging, which evolved from the first intraoral radiographs to high-quality three-dimensional (3D) Cone Beam Computed Tomography (CBCT). Other imaging techniques have shown potential, such as Optical Coherence Tomography (OCT). We have recently reported on the boundaries of these two types of techniques, regarding. the dental fields where each one is more appropriate or where they should be both used. The aim of the present study is to explore the unique capabilities of the OCT technique to optimize X-ray units imaging (i.e., in terms of image resolution, radiation dose, or contrast). Two types of commercially available and widely used X-ray units are considered. To adjust their parameters, a protocol is developed to employ OCT images of dental conditions that are documented on high (i.e., less than 10 μm) resolution OCT images (both B-scans/cross sections and 3D reconstructions) but are hardly identified on the 200 to 75 μm resolution panoramic or CBCT radiographs. The optimized calibration of the X-ray unit includes choosing appropriate values for the anode voltage and current intensity of the X-ray tube, as well as the patient’s positioning, in order to reach the highest possible X-rays resolution at a radiation dose that is safe for the patient. The optimization protocol is developed in vitro on OCT images of extracted teeth and is further applied in vivo for each type of dental investigation. Optimized radiographic results are compared with un-optimized previously performed radiographs. Also, we show that OCT can permit a rigorous comparison between two (types of) X-ray units. In conclusion, high-quality dental images are possible using low radiation doses if an optimized protocol, developed using OCT, is applied for each type of dental investigation. Also, there are situations when the X-ray technology has drawbacks for dental diagnosis or treatment assessment. In such situations, OCT proves capable to provide qualitative images.

scholarly journals A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 174
Author(s):  
Sun ◽  
Zhang ◽  
Li ◽  
Meng
Keyword(s):  
Image Reconstruction ◽  
Radiation Dose ◽  
Image Restoration ◽  
Sparse Representation ◽  
Iterative Reconstruction ◽  
Model Parameters ◽  
Projection Data ◽  
Ct Image ◽  
High Quality ◽  
Ct Image Reconstruction

Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

Percutaneous Computed Tomography-Guided Lung Biopsies: Preliminary Results using an Augmented Reality Navigation System

2012 ◽  
Vol 98 (6) ◽  
pp. 775-782 ◽  
Author(s):  
Rosario Francesco Grasso ◽  
Giacomo Luppi ◽  
Roberto Luigi Cazzato ◽  
Eliodoro Faiella ◽  
Francesco D'Agostino ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Augmented Reality ◽  
Radiation Dose ◽  
Navigation System ◽  
Ct Scans ◽  
Ultrasound Images ◽  
Procedure Time ◽  
Ct Guided ◽  
X Ray ◽  
Lung Biopsies

Aims and background “Augmented reality” is a technique to create a composite view by augmenting the real intervention field, visualized by the doctor, with additional information coming from a virtual volume generated using computed tomography (CT), magnetic resonance or ultrasound images previously acquired from the same patient. In the present study we verified the accuracy and validated the clinical use of an augmented reality navigation system produced to perform percutaneous CT-guided lung biopsies. Methods One hundred and eighty consecutive patients with solitary parenchymal lung lesions, enrolled using a nonrandom enrollment system, underwent percutaneous CT-guided aspiration and core biopsy using a traditional technique (group C, 90 patients) and navigation system assistance (group S, 90 patients). For each patient we recorded the largest lesion diameter, procedure time, overall number of CT scans, radiation dose, and complications. The entire experimental project was evaluated and approved by the local institutional review board (ethics committee). Results Each procedure was concluded successfully and a pathological diagnosis was reached in 96% of cases in group S and 90% of cases in group C. Procedure time, overall number of CT scans and incident x-ray radiation dose (CTDIvol) were significantly reduced in navigation system-assisted procedures (P <0.001; z = 5.64) compared with traditional CT-guided procedures. The percentage of procedural complications was 14% in group S and 17% in group C. Conclusion The augmented reality navigation system used in this study was a highly safe, technically reliable and effective support tool in percutaneous CT-guided lung biopsy, allowing to shorten the procedure time and reduce the incident x-ray radiation dose to patients and the rate of insufficient specimens. Furthermore, it has the potential to increase the number of procedures executed in the allocated time without increasing the number of complications.

scholarly journals A Design and Fabrication Method for Wood-Inspired Composites by Micro X-Ray Computed Tomography and 3D Printing

2020 ◽  
Vol 10 (4) ◽  
pp. 1400
Author(s):  
Yubo Tao ◽  
Zelong Li ◽  
Peng Li
Keyword(s):  
Computed Tomography ◽  
3D Printing ◽  
Region Of Interest ◽  
Ct Scanning ◽  
Quality Factors ◽  
Elementary Model ◽  
Wood Structures ◽  
X Ray ◽  
X Ray Computed ◽  
3D Volume

Developments in 3D printing and CT scanning technologies have facilitated the imitation of natural wood structures. However, creating composites from the elementary features of anisotropic wood structures remains a new frontier. This paper aims to investigate the potential of constructing and 3D printing mechanically customizable composites by combining anisotropic elementary models reconstructed from the micro X-ray computed tomography (μ-CT) scanning of wood. In this study, an arbitrary region of interest selected from the μ-CT scanning of a sample of Manchurian walnut (Juglans mandshurica) was reconstructed into isosurfaces that constituted the 3D model of an elementary model. Elementary models were combined to form the wood-inspired composites in various arrangements. The surface and interior structures of the elementary model were found to be customizable through adjusting the image Threshold and Surface Quality Factors during 3D volume reconstruction. Compressional simulations and experiments performed on the elementary model (digital and 3D printed) revealed that its compressive behavior was wood-like and anisotropic. Numerical analysis established a preliminary link between the arrangements of elementary models and the compressive stiffness of respective composites, showing that it is possible to control the compressive behaviors of the composites through the design of specific elementary model arrangements.

Change detection in sparse repeat CT scans with non-rigid deformations

2021 ◽  
pp. 1-21
Author(s):  
Naomi Shamul ◽  
Leo Joskowicz
Keyword(s):  
Change Detection ◽  
Experimental Studies ◽  
Region Of Interest ◽  
Recall Rate ◽  
Image Space ◽  
Ct Scans ◽  
Two Phase ◽  
Low Contrast ◽  
The Difference ◽  
Radon Space

BACKGROUND: Detecting and interpreting changes in the images of follow-up CT scans by the clinicians is often time-consuming and error-prone due to changes in patient position and non-rigid anatomy deformations. Thus, reconstructed repeat scan images are required, precluding reduced dose sparse-view repeat scanning. OBJECTIVE: To develop a method to automatically detect changes in a region of interest of sparse-view repeat CT scans in the presence of non-rigid deformations of the patient’s anatomy without reconstructing the original images. METHODS: The proposed method uses the sparse sinogram data of two CT scans to distinguish between genuine changes in the repeat scan and differences due to non-rigid anatomic deformations. First, size and contrast level of the changed regions are estimated from the difference between the scans’ sinogram data. The estimated types of changes in the repeat scan help optimize the method’s parameter values. Two scans are then aligned using Radon space non-rigid registration. Rays which crossed changes in the ROI are detected and back-projected onto image space in a two-phase procedure. These rays form a likelihood map from which the binary changed region map is computed. RESULTS: Experimental studies on four pairs of clinical lung and liver CT scans with simulated changed regions yield a mean changed region recall rate >  86%and a mean precision rate >  83%when detecting large changes with low contrast, and high contrast changes, even when small. The new method outperforms image space methods using prior image constrained compressed sensing (PICCS) reconstruction, particularly for small, low contrast changes (recall = 15.8%, precision = 94.7%). CONCLUSION: Our method for automatic change detection in sparse-view repeat CT scans with non-rigid deformations may assist radiologists by highlighting the changed regions and may obviate the need for a high-quality repeat scan image when no changes are detected.

Other Early Designs of Micropattern Detectors Developed Between 1998 and 2003

2014 ◽  
pp. 114-128
Keyword(s):  
Radiation Dose ◽  
Plasma Diagnostics ◽  
Parallel Plate ◽  
Leak Detector ◽  
High Quality ◽  
X Ray ◽  
3 Dimensional ◽  
Micro Hole ◽  
Strip Plate

This chapter focuses on the intense developments of micropattern detectors that happened between 1998 and 2003. In this period, many new designs were invented and manufactured by means of a photolithographic technology. These detectors include microwire detectors, microslit detectors, LEAK multiplication structures, microgap parallel-plate chambers, micro-hole strip plate gaseous detectors, etc. Some of them remain simply as interesting exercises demonstrating the great capability of microelectronic technique. Some of them are used in practice, for example in 2 dimensional and 3 dimensional mammographic scanners. These scanners are based on microgap parallel-plate chambers and give high quality X-ray images at a reduced radiation dose delivered to the patients. Early versions of the LEAK detector were intensively used in plasma diagnostics. Micro-hole strip plate gaseous detectors are currently used in some prototypes of photodetectors. This chapter also describes an MSGC type MWPC invented by Charpak et al. in an attempt to overcome the problems associate with the MSGC (i.e. charging up effects and poor rate characteristics).

scholarly journals Deep Encoder-Decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-View Data

2019 ◽  
Vol 6 (4) ◽  
pp. 111 ◽  
Author(s):  
Huidong Xie ◽  
Hongming Shan ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Radiation Dose ◽  
Image Data ◽  
Ct Image ◽  
3D Ct ◽  
X Ray ◽  
3D Network ◽  
Ct Image Reconstruction ◽  
3D Information ◽  
3D Volume

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT image reconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D deep network has a great potential for improving the image quality in a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing 3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publicly available abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other 2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to produce promising reconstruction results.

scholarly journals A new methodology exploring the record of snow avalanches in lake sediments

2016 ◽  
Author(s):  
Laurent Fouinat ◽  
Pierre Sabatier ◽  
Jérôme Poulenard ◽  
Jean-Louis Reyss ◽  
Xavier Montet ◽  
...  
Keyword(s):  
Organic Matter ◽  
Global Change ◽  
Lake Sediments ◽  
Sediment Cores ◽  
Ct Scanning ◽  
Ct Scans ◽  
Snow Avalanches ◽  
X Ray ◽  
Steep Slopes ◽  
Non Destructive

Abstract. In recent years, wet avalanche deposits have become a subject of increasing concern in a context of both global change and winter mountain tourism activities. This study focuses on the use of a new methodology based on CT scans to identify snow avalanche deposits in lake sediment. Here, we study the mid-elevation Lake Lauvitel system (western French Alps), which features steep slopes and avalanche corridors. CT scanning is a fast, non-destructive method based on X-ray technology and allows the identification of elements with different densities. We applied this method to sediment cores, leading to the 3D identification of the dense rocks and organic matter macroremains that characterize wet avalanches. A total of eight periods of higher avalanche activity are identified since AD 1880 at the site. This new methodology is suitable for avalanche deposit reconstruction and may be applicable more widely in paleolimnological studies.

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