Haitham Ahmed - Academia.edu (original) (raw)
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Papers by Haitham Ahmed
We propose a technique for suppression of both intra-slice and inter-slice types of motion artifa... more We propose a technique for suppression of both intra-slice and inter-slice types of motion artifacts simultaneously. Starting from the general assumption of rigid body motion, we consider the case when the acquisition of the k-space is in the form of bands of finite number of lines arranged in a rectilinear fashion to cover the k-space area of interest. We also assume that an averaging factor of at least 2 is desired. Instead of acquiring a full k-space of each image and then average the result, we propose a new acquisition strategy based on acquiring the k-space in consecutive bands having 50% overlap going from one end of the phase encoding direction to the other end. In case of no motion, this overlap can be used as the second acquisition (NEX=2). When motion is encountered, both types motion are reduced to the same form under this acquisition strategy. In particular, detection and correction of motion between consecutive bands result in suppression of both motion types. In this work, this is achieved by utilizing the overlap area to estimate the motion, which is then taken into consideration in further reconstruction (or even acquisition if real-time control is available on the MR system). We demonstrate the accuracy and computational efficiency of this motion estimation approach. Once the motion is estimated, we propose a simple strategy to reconstruct artifact-free images from the acquired data that take into account the possible voids in the acquired k-space as a result of rotational motion between blades.
Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set o... more Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set of signals using an array of receiving coils each with a local sensitivity pattern. An image is then reconstructed from the partial dataset using the additional information of coil sensitivity. GRAPPA (generalized auto calibrating partially parallel acquisitions) is one of the most successful reconstruction techniques in which the missing k-space lines are interpolated from the acquired data in the whole coil array using a convolution kernel estimated from a fully sampled data patch in the center of k-space. The interpolation kernel is usually small but fixed in size for all coils. Here, we show that a variable kernel with a size dependent on the coil sensitivity can lead to better image quality. The kernel size is estimated from the ratio of the coil sensitivities obtained from a reference scan or from the same dataset. Conventional GRAPPA kernel estimation and image reconstruction is modified to employ the variable-size kernel for improved reconstruction. The new technique shows improved image quality compared to GRAPPA.
We propose a technique for suppression of both intra-slice and inter-slice types of motion artifa... more We propose a technique for suppression of both intra-slice and inter-slice types of motion artifacts simultaneously. Starting from the general assumption of rigid body motion, we consider the case when the acquisition of the k-space is in the form of bands of finite number of lines arranged in a rectilinear fashion to cover the k-space area of interest. We also assume that an averaging factor of at least 2 is desired. Instead of acquiring a full k-space of each image and then average the result, we propose a new acquisition strategy based on acquiring the k-space in consecutive bands having 50% overlap going from one end of the phase encoding direction to the other end. In case of no motion, this overlap can be used as the second acquisition (NEX=2). When motion is encountered, both types motion are reduced to the same form under this acquisition strategy. In particular, detection and correction of motion between consecutive bands result in suppression of both motion types. In this work, this is achieved by utilizing the overlap area to estimate the motion, which is then taken into consideration in further reconstruction (or even acquisition if real-time control is available on the MR system). We demonstrate the accuracy and computational efficiency of this motion estimation approach. Once the motion is estimated, we propose a simple strategy to reconstruct artifact-free images from the acquired data that take into account the possible voids in the acquired k-space as a result of rotational motion between blades.
Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set o... more Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set of signals using an array of receiving coils each with a local sensitivity pattern. An image is then reconstructed from the partial dataset using the additional information of coil sensitivity. GRAPPA (generalized auto calibrating partially parallel acquisitions) is one of the most successful reconstruction techniques in which the missing k-space lines are interpolated from the acquired data in the whole coil array using a convolution kernel estimated from a fully sampled data patch in the center of k-space. The interpolation kernel is usually small but fixed in size for all coils. Here, we show that a variable kernel with a size dependent on the coil sensitivity can lead to better image quality. The kernel size is estimated from the ratio of the coil sensitivities obtained from a reference scan or from the same dataset. Conventional GRAPPA kernel estimation and image reconstruction is modified to employ the variable-size kernel for improved reconstruction. The new technique shows improved image quality compared to GRAPPA.
We propose a technique for suppression of both intra-slice and inter-slice types of motion artifa... more We propose a technique for suppression of both intra-slice and inter-slice types of motion artifacts simultaneously. Starting from the general assumption of rigid body motion, we consider the case when the acquisition of the k-space is in the form of bands of finite number of lines arranged in a rectilinear fashion to cover the k-space area of interest. We also assume that an averaging factor of at least 2 is desired. Instead of acquiring a full k-space of each image and then average the result, we propose a new acquisition strategy based on acquiring the k-space in consecutive bands having 50% overlap going from one end of the phase encoding direction to the other end. In case of no motion, this overlap can be used as the second acquisition (NEX=2). When motion is encountered, both types motion are reduced to the same form under this acquisition strategy. In particular, detection and correction of motion between consecutive bands result in suppression of both motion types. In this work, this is achieved by utilizing the overlap area to estimate the motion, which is then taken into consideration in further reconstruction (or even acquisition if real-time control is available on the MR system). We demonstrate the accuracy and computational efficiency of this motion estimation approach. Once the motion is estimated, we propose a simple strategy to reconstruct artifact-free images from the acquired data that take into account the possible voids in the acquired k-space as a result of rotational motion between blades.
Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set o... more Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set of signals using an array of receiving coils each with a local sensitivity pattern. An image is then reconstructed from the partial dataset using the additional information of coil sensitivity. GRAPPA (generalized auto calibrating partially parallel acquisitions) is one of the most successful reconstruction techniques in which the missing k-space lines are interpolated from the acquired data in the whole coil array using a convolution kernel estimated from a fully sampled data patch in the center of k-space. The interpolation kernel is usually small but fixed in size for all coils. Here, we show that a variable kernel with a size dependent on the coil sensitivity can lead to better image quality. The kernel size is estimated from the ratio of the coil sensitivities obtained from a reference scan or from the same dataset. Conventional GRAPPA kernel estimation and image reconstruction is modified to employ the variable-size kernel for improved reconstruction. The new technique shows improved image quality compared to GRAPPA.
We propose a technique for suppression of both intra-slice and inter-slice types of motion artifa... more We propose a technique for suppression of both intra-slice and inter-slice types of motion artifacts simultaneously. Starting from the general assumption of rigid body motion, we consider the case when the acquisition of the k-space is in the form of bands of finite number of lines arranged in a rectilinear fashion to cover the k-space area of interest. We also assume that an averaging factor of at least 2 is desired. Instead of acquiring a full k-space of each image and then average the result, we propose a new acquisition strategy based on acquiring the k-space in consecutive bands having 50% overlap going from one end of the phase encoding direction to the other end. In case of no motion, this overlap can be used as the second acquisition (NEX=2). When motion is encountered, both types motion are reduced to the same form under this acquisition strategy. In particular, detection and correction of motion between consecutive bands result in suppression of both motion types. In this work, this is achieved by utilizing the overlap area to estimate the motion, which is then taken into consideration in further reconstruction (or even acquisition if real-time control is available on the MR system). We demonstrate the accuracy and computational efficiency of this motion estimation approach. Once the motion is estimated, we propose a simple strategy to reconstruct artifact-free images from the acquired data that take into account the possible voids in the acquired k-space as a result of rotational motion between blades.
Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set o... more Parallel magnetic resonance imaging achieves reduction in scan time by collecting a partial set of signals using an array of receiving coils each with a local sensitivity pattern. An image is then reconstructed from the partial dataset using the additional information of coil sensitivity. GRAPPA (generalized auto calibrating partially parallel acquisitions) is one of the most successful reconstruction techniques in which the missing k-space lines are interpolated from the acquired data in the whole coil array using a convolution kernel estimated from a fully sampled data patch in the center of k-space. The interpolation kernel is usually small but fixed in size for all coils. Here, we show that a variable kernel with a size dependent on the coil sensitivity can lead to better image quality. The kernel size is estimated from the ratio of the coil sensitivities obtained from a reference scan or from the same dataset. Conventional GRAPPA kernel estimation and image reconstruction is modified to employ the variable-size kernel for improved reconstruction. The new technique shows improved image quality compared to GRAPPA.