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Research PaperResearchia:202609.17038

Highly accelerated 3D Cartesian MPnRAGE with implicit neural representation reconstruction

Natascha Niessen

Abstract

MPnRAGE enables multiple inversion contrast images in a single scan, allowing quantitative T1 mapping, tissue nulled contrasts, and standard MPRAGE synthesis. However, current 3D scan times remain clinically impractical, motivating accelerated 3D MPnRAGE. This work provides a highly accelerated Cartesian 3D MPnRAGE sequence with joint implicit neural representation (INR) reconstruction. The sequence uses a tailored view-ordering strategy, flip angle schedule and complementary variable-density Po...

Submitted: September 17, 2026Subjects: Engineering; Biomedical Engineering

Description / Details

MPnRAGE enables multiple inversion contrast images in a single scan, allowing quantitative T1 mapping, tissue nulled contrasts, and standard MPRAGE synthesis. However, current 3D scan times remain clinically impractical, motivating accelerated 3D MPnRAGE. This work provides a highly accelerated Cartesian 3D MPnRAGE sequence with joint implicit neural representation (INR) reconstruction. The sequence uses a tailored view-ordering strategy, flip angle schedule and complementary variable-density Poisson-disk undersampling. Calibration data acquired during otherwise unused delay time are used for sensitivity map estimation and complementary high-frequency sampling. Ten INR-reconstructed inversion images at 1.5 mm3^3 are evaluated against fully sampled references via retrospective undersampling. Prospectively accelerated 1 mm3^3 images at R = 20 (5.39 min) demonstrate clinical feasibility. INR reconstruction outperforms subspace and iterative local low rank reconstruction on highly accelerated data. The proposed highly undersampled 3D Cartesian MPnRAGE with INR reconstruction generates multiple high-quality inversion contrasts in substantially reduced scan time. Scan-specific INR reconstruction improves image quality while reducing reconstruction time versus state-of-the-art methods.


Source: arXiv:2609.18589v1 - http://arxiv.org/abs/2609.18589v1 PDF: https://arxiv.org/pdf/2609.18589v1 Original Link: http://arxiv.org/abs/2609.18589v1

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Submission Info
Date:
Sep 17, 2026
Topic:
Biomedical Engineering
Area:
Engineering
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