Methods and systems for image reconstruction using low noise kernel
First Claim
1. A system for visualizing relatively small structures within an object, said system comprising:
- an image acquisition sub-system for acquiring a dataset for a volume of interest; and
a processor for generating image data from the acquired data, said processor programmed to execute a high resolution filter kernel algorithm comprising a weighting factor applied to a ramp filter that scales relatively high frequency regions of the image dataset by a factor greater than one, and a windowing function applied to the weighted ramp filter that facilitates reducing aliasing artifacts in reconstructed images generated from the image dataset.
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Abstract
Methods and systems for a system for visualizing relatively small structures within an object are provided. The system includes an image acquisition sub-system for acquiring a dataset for a volume of interest and processor for generating image data from the acquired data wherein the processor is programmed to execute a high resolution filter kernel algorithm that includes a weighting factor applied to a ramp filter that scales relatively high frequency regions of the image dataset by a factor greater than one. The high resolution filter kernel algorithm also includes a windowing function applied to the weighted ramp filter that facilitates reducing aliasing artifacts in reconstructed images generated from the image dataset.
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Citations
31 Claims
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1. A system for visualizing relatively small structures within an object, said system comprising:
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an image acquisition sub-system for acquiring a dataset for a volume of interest; and a processor for generating image data from the acquired data, said processor programmed to execute a high resolution filter kernel algorithm comprising a weighting factor applied to a ramp filter that scales relatively high frequency regions of the image dataset by a factor greater than one, and a windowing function applied to the weighted ramp filter that facilitates reducing aliasing artifacts in reconstructed images generated from the image dataset. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. An imaging system comprising an image acquisition portion for acquiring data, a controller configured to control the image acquisition portion, and a processor configured to receive a dataset for an object that includes relatively small structures, said processor further programmed to:
process the dataset using a high resolution filter kernel algorithm comprising a weighting factor applied to a ramp filter that scales relatively high frequency regions of the image dataset by a factor greater than one, and a windowing function applied to the weighted ramp filter that facilitates reducing aliasing artifacts in reconstructed images generated from the image dataset. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A method of visualizing relatively small structures within an object comprising:
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receiving a dataset for a volume of interest; and applying a high resolution filter kernel including a weighting factor and a windowing function to the dataset wherein the weighting factor is applied to a ramp filter that scales relatively high frequency regions of the dataset by a factor greater than one and wherein the windowing function is applied to the weighted ramp filter such that aliasing artifacts in reconstructed images generated from the image dataset are facilitated being reduced. - View Dependent Claims (21, 22, 23, 24)
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25. A method for reconstructing an image from a dataset for a volume of interest, the method comprising:
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defining a reconstruction kernel by applying a window function to a filter kernel, the reconstruction kernel configured to substantially zero out frequencies outside of a Nyquist region associated with the filter kernel while maintaining the shape of filter kernel inside the Nyquist region; and applying the reconstruction kernel to the dataset. - View Dependent Claims (26, 27, 28, 29, 30, 31)
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Specification