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Purpose: To evaluate the error in T1 estimates using inversion-recovery-based T1 mapping due to imperfect inversion and to perform a systematic study of adiabatic inversion pulse designs in order to maximize inversion efficiency for values of transverse relaxation (T2) in the myocardium subject to a peak power constraint., Methods: The inversion factor for hyperbolic secant and tangent/hyperbolic tangent adiabatic full passage waveforms was calculated using Bloch equations. A brute-force search was conducted for design parameters: pulse duration, frequency range, shape parameters, and peak amplitude. A design was selected that maximized the inversion factor over a specified range of amplitude and off-resonance and validated using phantom measurements. Empirical correction for imperfect inversion was performed., Results: The tangent/hyperbolic tangent adiabatic pulse was found to outperform hyperbolic secant designs and achieve an inversion factor of 0.96 within ±150 Hz over 25% amplitude range with 14.7 µT peak amplitude. T1 mapping errors of the selected design due to imperfect inversion was ∼4% and could be corrected to <1%., Conclusions: Nonideal inversion leads to significant errors in inversion-recovery-based T1 mapping. The inversion efficiency of adiabatic pulses is sensitive to transverse relaxation. The tangent/hyperbolic tangent design achieved the best performance subject to the peak amplitude constraint., (Copyright © 2013 Wiley Periodicals, Inc.)

This work presents a new open source framework for medical image reconstruction called the "Gadgetron." The framework implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or "Gadgets" from raw data to reconstructed images. The data processing pipeline is configured dynamically at run-time based on an extensible markup language configuration description. The framework promotes reuse and sharing of reconstruction modules and new Gadgets can be added to the Gadgetron framework through a plugin-like architecture without recompiling the basic framework infrastructure. Gadgets are typically implemented in C/C++, but the framework includes wrapper Gadgets that allow the user to implement new modules in the Python scripting language for rapid prototyping. In addition to the streaming framework infrastructure, the Gadgetron comes with a set of dedicated toolboxes in shared libraries for medical image reconstruction. This includes generic toolboxes for data-parallel (e.g., GPU-based) execution of compute-intensive components. The basic framework architecture is independent of medical imaging modality, but this article focuses on its application to Cartesian and non-Cartesian parallel magnetic resonance imaging., (Copyright © 2012 Wiley Periodicals, Inc.)

A barrier to the adoption of non-Cartesian parallel magnetic resonance imaging for real-time applications has been the times required for the image reconstructions. These times have exceeded the underlying acquisition time thus preventing real-time display of the acquired images. We present a reconstruction algorithm for commodity graphics hardware (GPUs) to enable real time reconstruction of sensitivity encoded radial imaging (radial SENSE). We demonstrate that a radial profile order based on the golden ratio facilitates reconstruction from an arbitrary number of profiles. This allows the temporal resolution to be adjusted on the fly. A user adaptable regularization term is also included and, particularly for highly undersampled data, used to interactively improve the reconstruction quality. Each reconstruction is fully self-contained from the profile stream, i.e., the required coil sensitivity profiles, sampling density compensation weights, regularization terms, and noise estimates are computed in real-time from the acquisition data itself. The reconstruction implementation is verified using a steady state free precession (SSFP) pulse sequence and quantitatively evaluated. Three applications are demonstrated; real-time imaging with real-time SENSE 1) or k- t SENSE 2) reconstructions, and 3) offline reconstruction with interactive adjustment of reconstruction settings.