Wireless links have been proposed to connect magnetic resonance (MR) array receiver coils to the rest of the system to eliminate the safety and cross-talk issues encountered using coaxial cables. Analog transmission methods are unsuited because of limited dynamic range and noise figure; therefore, fully independent individual digital receiver modules must be developed. Limited data rates supported by wireless links restrict the number of coil channels that can be transmitted to well below those of the state-of-the-art high-density arrays that would benefit the most from wireless technology. Two independent methods of compressing MR data prior to transmission are presented that when combined can readily reduce it to one-third or less of the original amount with negligible impact on image quality parameters such as artifact power (AP) and signalto-noise ratio (SNR). These conservative results were obtained from arrays of six to 16 channels which is typical of today's clinical systems and show that compression efficiency improves with increasing channel density. Simulated and experimental 2D spiral data acquired from a standard head array at 3 tesla was used to evaluate AP as a function of compression by off-line processing. Spectral compression reduces the acquired data by up to 45% using dynamic demodulation, filtering and decimation. Dynamic range compression followed by bit-depth reduction further reduces the data by up to 37.5% without visible image artifacts. Image SNR improves 1-25% at the periphery of the fieldof-view (FOV) due to spectral compression, while dynamic range compression results in a uniform SNR gain of 4-8% over the whole FOV.
MRI‐only Radiation Treatment Planning (RTP) is becoming increasingly popular because of a simplified work‐flow, and less inconvenience to the patient who avoids multiple scans. The advantages of MRI‐based RTP over traditional CT‐based RTP lie in its superior soft‐tissue contrast, and absence of ionizing radiation dose. The lack of electron‐density information in MRI can be addressed by automatic tissue classification. To distinguish bone from air, which both appear dark in MRI, an ultra‐short echo time (UTE) pulse sequence may be used. Quantitative MRI parametric maps can provide improved tissue segmentation/classification and better sensitivity in monitoring disease progression and treatment outcome than standard weighted images. Superior tumor contrast can be achieved on pure T1 images compared to conventional T1‐weighted images acquired in the same scan duration and voxel resolution.
In this study, we have developed a robust and fast quantitative MRI acquisition and post‐processing work‐flow that integrates these latest advances into the MRI‐based RTP of brain lesions. Using 3D multi‐echo FLASH images at two different optimized flip angles (both acquired in under 9 min, and 1mm isotropic resolution), parametric maps of T1, proton‐density (M0), and T2* are obtained with high contrast‐to‐noise ratio, and negligible geometrical distortions, water‐fat shifts and susceptibility effects. An additional 3D UTE MRI dataset is acquired (in under 4 min) and post‐processed to classify tissues for dose simulation. The pipeline was tested on four healthy volunteers and a clinical trial on brain cancer patients is underway.
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