PURPOSE: Several methods in MRI use the phase information of the complex signal and require phase unwrapping (e.g., B0 field mapping, chemical shift imaging, and velocity measurements). In this work, an algorithm was developed focusing on the needs and requirements of MR temperature imaging applications. METHODS: The proposed method performs fully automatic unwrapping using a list of all pixels sorted by magnitude in descending order and creates and merges clusters of unwrapped pixels until the entire image is unwrapped. The algorithm was evaluated using simulated phantom data and in vivo clinical temperature imaging data. RESULTS: The evaluation of the phantom data demonstrated no errors in regions with signal-to-noise ratios of at least 4.5. For the in vivo data, the algorithm did not fail at an average of more than one pixel for signal-to-noise ratios greater than 6.3. Processing times less than 30 ms per image were achieved by unwrapping pixels inside a region of interest (53 × 53 pixels) used for referenceless MR temperature imaging. CONCLUSIONS: The algorithm has been demonstrated to operate robustly with clinical in vivo data in this study. The processing time for common regions of interest in referenceless MR temperature imaging allows for online updates of temperature maps without noticeable delay.
PURPOSE: Several methods in MRI use the phase information of the complex signal and require phase unwrapping (e.g., B0 field mapping, chemical shift imaging, and velocity measurements). In this work, an algorithm was developed focusing on the needs and requirements of MR temperature imaging applications. METHODS: The proposed method performs fully automatic unwrapping using a list of all pixels sorted by magnitude in descending order and creates and merges clusters of unwrapped pixels until the entire image is unwrapped. The algorithm was evaluated using simulated phantom data and in vivo clinical temperature imaging data. RESULTS: The evaluation of the phantom data demonstrated no errors in regions with signal-to-noise ratios of at least 4.5. For the in vivo data, the algorithm did not fail at an average of more than one pixel for signal-to-noise ratios greater than 6.3. Processing times less than 30 ms per image were achieved by unwrapping pixels inside a region of interest (53 × 53 pixels) used for referenceless MR temperature imaging. CONCLUSIONS: The algorithm has been demonstrated to operate robustly with clinical in vivo data in this study. The processing time for common regions of interest in referenceless MR temperature imaging allows for online updates of temperature maps without noticeable delay.