2003
DOI: 10.1002/mrm.10435
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Local reconstruction of stenosed sections of artery using multiple MRA acquisitions

Abstract: A method for reconstructing magnetic resonance angiography (MRA) volumes from successive acquisitions is described. The method is based on double oblique acquisitions of highly anisotropic MRA volumes, each of which corresponds to reduced k-space filling. These partial k-spaces are then combined to obtain a 3D k-space adapted to the frequency spread of the angiographic image of the stenosis. The SNR-resolution compromise of MRA is thus improved by focusing the acquisition on the most relevant k-space regions. … Show more

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Cited by 13 publications
(10 citation statements)
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“…Two kinds of approaches can be identified: one works at the acquisition level over raw data (frequency space), while the others act on the volumetric images (spatial or image space) as an additional processing step. At the acquisition stage, the k-space data can be manipulated and combined to obtain adequate spatial resolution while reducing acquisition time (Herment et al, 2003); or parameters can be configured to obtain multiple scans with different slice directions which are then mixed up (Shilling et al, 2009). Regarding volumetric images, Peled and Yeshurun (2001) and Greenspan et al (2002) have proposed the first approaches to adapt the iterative back-projection method proposed by Irani and Peleg (1993) to 2D and 3D MR images, respectively; followed by other strategies such as the resolution enhancement method described by Carmi et al (2006).…”
Section: Introductionmentioning
confidence: 99%
“…Two kinds of approaches can be identified: one works at the acquisition level over raw data (frequency space), while the others act on the volumetric images (spatial or image space) as an additional processing step. At the acquisition stage, the k-space data can be manipulated and combined to obtain adequate spatial resolution while reducing acquisition time (Herment et al, 2003); or parameters can be configured to obtain multiple scans with different slice directions which are then mixed up (Shilling et al, 2009). Regarding volumetric images, Peled and Yeshurun (2001) and Greenspan et al (2002) have proposed the first approaches to adapt the iterative back-projection method proposed by Irani and Peleg (1993) to 2D and 3D MR images, respectively; followed by other strategies such as the resolution enhancement method described by Carmi et al (2006).…”
Section: Introductionmentioning
confidence: 99%
“…When multiple 2D scans with different slice select directions are available, the k-space data from those scans can be combined to provide a dense sampling of the k-space cube involving high-frequency k-space samples in different directions. Examples of these multiple 2D scans are orthogonal acquisitions with axial, coronal, and sagittal slice select directions, 3,9 and acquisitions with rotated slice select directions. 19 The fusion of k-space data from these scans provides dense sampling of the center of k-space and covers high-frequency corners of the k-space as well, thus simultaneously improves SNR and spatial resolution in 3D.…”
Section: A Super-resolution Reconstruction In the Frequency Domainmentioning
confidence: 99%
“…There has been an interesting body of work on postacquisition fusion of thick-slice anisotropic 2D MRI scans. This includes simple averaging of volumes, selective combination in the Fourier domain, 3 wavelet fusion, 4 and super-resolution reconstruction (SRR) in the image domain. [5][6][7] Super-resolution MRI closely follows the concepts of super-resolution reconstruction in digital image and video processing with techniques in frequency, image, and wavelet domains.…”
Section: Introductionmentioning
confidence: 99%
“…Some other researchers (Greenspan et al, 12 Peeters et al, 13 and Kim et al 14 ) proposed methods to reduce thickness in the slice direction of volumetric images. Other methods (Herment et al, 15 Shilling et al, 16 Carmi et al 17 ) combined multiple shifted scans to achieve a HR result. Woo et al 18 proposed an interpolation method for enhancement of tongue by generating a 3D image volume using three orthogonal images.…”
Section: Introductionmentioning
confidence: 99%