Efficient method for MR image reconstruction using coil sensitivity encoding

US7486839B2 · kind B2 · utility

Assignee

• CASE WESTERN RESERVE UNIVERSITY · US

Inventors

• Hisamoto Moriguchi · Cleveland, US
• Jeffrey L. Duerk · Avon Lake, US

Key dates

• Filing date: Jun 28, 2004
• Grant date: Feb 3, 2009
• Expiry date: Mar 29, 2026

Classification

• Technology area (CPC G): Physics
• CPC primary: G01R33/5608
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

SENSitivity Encoding (SENSE) has demonstrated potential for significant scan time reduction using multiple receiver channels. SENSE reconstruction algorithms for non-uniformly sampled data proposed to date require relatively high computational demands. A Projection Onto Convex Sets (POCS)-based SENSE reconstruction method (POCSENSE) has been recently proposed as an efficient reconstruction technique in rectilinear sampling schemes. POCSENSE is an iterative algorithm with a few constraints imposed on the acquired data sets at each iteration. Although POCSENSE can be readily performed on rectilinearly acquired k-space data, it is difficult to apply to non-uniformly acquired k-space data. Iterative Next Neighbor re-Gridding (INNG) algorithm is a recently proposed new reconstruction method for non-uniformly sampled k-space data. The POCSENSE algorithm can be extended to non-rectilinear sampling schemes by using the INNG algorithm. The resulting algorithm (POCSENSINNG) is an efficient SENSE reconstruction algorithm for non-uniformly sampled k-space data, taking into account coil sensitivities.