Undersampling Errors in Region-of-Interest Tomography

Sankar, P. V.; Nalcioğlu, Orhan; Sklansky, Jack

doi:10.1109/tmi.1982.4307568

Cited by 21 publications

(11 citation statements)

References 7 publications

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“…Truncation artifacts and loss of image accuracy inside the ROI would occur when truncated projection data are directly used for image reconstruction by use of the filtered backprojection (FBP) algorithm, which is the most widely used algorithm in the commercial CT scanners. Thus, the accuracy of the reconstructed image of the ROI by the FBP algorithm may be insufficient especially for quantitative assessment 7 , 8 , 9 , 10 , 11 , 12 , 13 …”

Section: Introductionmentioning

confidence: 99%

Improving image accuracy of region‐of‐interest in cone‐beam CT using prior image

Lee

Kim

Cho

2014

J Applied Clin Med Phys

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In diagnostic follow‐ups of diseases, such as calcium scoring in kidney or fat content assessment in liver using repeated CT scans, quantitatively accurate and consistent CT values are desirable at a low cost of radiation dose to the patient. Region‐of‐interest (ROI) imaging technique is considered a reasonable dose reduction method in CT scans for its shielding geometry outside the ROI. However, image artifacts in the reconstructed images caused by missing data outside the ROI may degrade overall image quality and, more importantly, can decrease image accuracy of the ROI substantially. In this study, we propose a method to increase image accuracy of the ROI and to reduce imaging radiation dose via utilizing the outside ROI data from prior scans in the repeated CT applications. We performed both numerical and experimental studies to validate our proposed method. In a numerical study, we used an XCAT phantom with its liver and stomach changing their sizes from one scan to another. Image accuracy of the liver has been improved as the error decreased from 44.4 HU to −0.1 HU by the proposed method, compared to an existing method of data extrapolation to compensate for the missing data outside the ROI. Repeated cone‐beam CT (CBCT) images of a patient who went through daily CBCT scans for radiation therapy were also used to demonstrate the performance of the proposed method experimentally. The results showed improved image accuracy inside the ROI. The magnitude of error decreased from −73.2 HU to 18 HU, and effectively reduced image artifacts throughout the entire image.PACS number: 87.57. Q‐

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Section: Introductionmentioning

confidence: 99%

Improving image accuracy of region‐of‐interest in cone‐beam CT using prior image

Lee

Kim

Cho

2014

J Applied Clin Med Phys

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“…HE problem of image reconstruction from a complete set T of projections is to compute an image p(z,y) from its Radon Transform, i.e., from a complete set of its line integrals p (~, e), defined as p ( r , 8) = S_ y, J_, p(z, y)6(r -T(.OSe -y sin o)dzdy. (1) The most common procedure for reconstruction from a complete set of projections is filtered backprojection (FBP).…”

Section: Introductionmentioning

confidence: 99%

“…When the projections are sampled in the angular and radial variables, but cover the entire extent of the image, FBP still yields quite satisfactory results. The resolution of the reconstructed image is determined by the sampling densities in T and 8 of p(r, 8) and the cutoff frequency of h(u).…”

Section: Introductionmentioning

confidence: 99%

“…The effects of using nonuniform sampling in tomography were investigated in [8]. Exponential nonuniform sampling calls to mind the wavelet transform, which has been used recently [9] to obtain a different type of reconstruction algorithm using nonuniform sampling.…”

Section: Introductionmentioning

confidence: 99%

“…Exponential nonuniform sampling calls to mind the wavelet transform, which has been used recently [9] to obtain a different type of reconstruction algorithm using nonuniform sampling. It is interesting to compare our numerical results to those of [8] and [9], although the approaches and data required are quite different. For example, our approach is exact to discretization, the error due to which can be made arbitrarily small, while the approach of [9] requires wavelet basis functions with compact support both before and after a derivative-Hilbert transform, which clearly cannot be found (although functions with rapidly decaying support can be found).…”

Section: Introductionmentioning

confidence: 99%

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Region-of-interest tomography using exponential radial sampling

Sahiner

Yagle

1995

IEEE Trans. on Image Process.

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The authors combine several ideas, including nonuniform sampling and circular harmonic expansions, into a new procedure for reconstructing a small region of interest (ROI) of an image from a set of its projections that are densely sampled in the ROI and coarsely sampled outside the ROI. Specifically, the radial sampling density of both the projections and the reconstructed image decreases exponentially with increasing distance from the ROI. The problem and data are reminiscent of the recently formulated local tomography problem; however, the authors' algorithm reconstructs the ROI of the image itself, not the filtered version of it obtained using local tomography. The new algorithm has the added advantages of speed (it can be implemented entirely using the FFT) and parallelizability (each image harmonic is computed independently). Numerical examples compare the new algorithm to filtered backprojection.

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Computed Tomography In Industrial Hygiene

Todd¹

2001

Patty's Industrial Hygiene

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A variety of air monitoring methods are used by industrial hygienists to evaluate human exposures to contaminants, monitor process emissions and leaks, and determine the effectiveness of ventilation systems. Although methods may vary in the length of time over which the samples are obtained, essentially all of the methods use point samplers. Therefore, the results are spatially limited to the discrete locations of the sampling devices. In addition, when the concentrations are integrated over time, they are temporally limited to the length of the sample time. Limited spatial and temporal resolution is important because it reduces an industrial hygienist's ability to evaluate and control exposures to chemicals effectively. When sampling devices are placed in the breathing zones of workers, the results relate only to the physical location of the workers or to the paths that they travel during the sampling period. Industrial hygienists take these spatially limited results and assume they are representative of the larger unsampled workforce. This assumption may not always be valid; in practice, it is difficult to select a representative subset of individuals to sample because the concentration distributions in a room are unknown. Before choosing a subset of workers to sample, a larger homogeneous group is usually created based upon similarities in the tasks they perform and in the local environments in the rooms where they work. The local environment is important because contaminant flow patterns are strong determinants of exposure; however, environmental similarity is difficult to predict. Data on ventilation systems and airflow patterns are usually lacking and individuals selected for sampling may not be truly representative. An entirely new air monitoring technique, for both the occupational and environmental field, may provide spatially and temporally resolved estimates of contaminant concentrations noninvasively (does not pump air out of a space through collection media), and in real‐time, over large areas. This technique combines the real‐time chemical detection methods of optical remote sensing, such as an open‐path Fourier transform infrared (OP‐FTIR) spectrometer, with the mapping capabilities of computed tomography (CT). This environmental CT system generates near real‐time spatially and temporally resolved two‐dimensional concentration maps of multiple chemicals at low limits of detection (ppb–low ppm) for an entire area. Not just another nifty tool, this technology represents a major departure from conventional industrial hygiene air sampling methods and could allow researchers to understand and evaluate human exposures, source emissions, and chemical transport in ways that are unavailable using conventional methods. This technique provides a powerful tool for visualizing air contaminant species, concentrations, and flows in industry and outdoors in the community. Each tomographic concentration map provides a snapshot, which represents a short time period (minutes), of the concentration and location of contaminant plumes in a slice or plane through the air. As measurements are obtained over the day, the reconstructed concentration maps are linked together to provide a powerful tool for visualizing the flow of air contaminants over space and time.

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Undersampling Errors in Region-of-Interest Tomography

Cited by 21 publications

References 7 publications

Improving image accuracy of region‐of‐interest in cone‐beam CT using prior image

Improving image accuracy of region‐of‐interest in cone‐beam CT using prior image

Region-of-interest tomography using exponential radial sampling

Computed Tomography In Industrial Hygiene

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