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Respiration‐based sorting of dynamic MRI to derive representative 4D‐MRI for radiotherapy planning
Author(s) -
Tryggestad Erik,
Flammang Aaron,
HanOh Sarah,
Hales Russell,
Herman Joseph,
McNutt Todd,
Roland Teboh,
Shea Steven M.,
Wong John
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4800808
Subject(s) - breathing , nuclear medicine , medical imaging , sagittal plane , computer science , dynamic contrast enhanced mri , magnetic resonance imaging , projection (relational algebra) , chin , template matching , artificial intelligence , pattern recognition (psychology) , mathematics , computer vision , medicine , algorithm , radiology , image (mathematics) , anatomy
Purpose: Current pretreatment, 4D imaging techniques are suboptimal in that they sample breathing motion over a very limited “snap‐shot” in time. To potentially address this, the authors have developed a longer‐duration MRI and postprocessing technique to derive the average or most‐probable state of mobile anatomy and meanwhile capture and convey the observed motion variability.Methods: Sagittal and coronal multislice, 2D dynamic MRI was acquired in a sequential fashion over extended durations in two abdominal and four lung studies involving healthy volunteers. Two sequences, readily available on a commercial system, were employed. Respiratory interval‐correlated, or 4D‐MRI, volumes were retrospectively derived using a two‐pass approach. In a first pass, a respiratory trace acquired simultaneous with imaging was processed and slice stacking was used to derive a set of MRI volumes, each representing an equal time or proportion of respiration. Herein, all raw 2D frames mapping to the given respiratory interval, per slice location, were averaged. In a second‐pass, this prior reconstruction provided a set of template images and a similarity metric was employed to discern the subset of best‐matching raw 2D frames for secondary averaging (per slice location and respiratory interval). Breathing variability (per respiratory interval and slice location) was depicted by computing both a maximum intensity projection as well as a pixelwise standard deviation image.Results: These methods were successfully demonstrated in both the lung and abdomen for both applicable sequences, performing reconstructions with ten respiratory intervals. The first‐pass (average) resulted in motion‐induced blurring, especially for irregular breathing. The authors have demonstrated qualitatively that the second‐pass result can mitigate this blurring.Conclusions: They have presented a novel methodology employing dMRI to derive representative 4D‐MRI. This set of techniques are practical in that (1) they employ MRI sequences that are standard across commercial vendors; (2) the 2D imaging planes can be oriented onto an arbitrary axis (e.g., sagittal, coronal, axial…); (3) the image processing techniques are relatively simple. Systematically applying this and similar dMRI‐based techniques in patients is a crucial next step to demonstrate efficacy beyond CT‐only based practice.