A method for motion detection and categorization in perfusion weighted MRI

Abstract

The blood perfusion measurement is done by injecting a bolus of contrast agent in the brain followed by imaging over a period of time (scan). This process can extend into minutes and hence any patient motion mid-scan results in corrupted data. This is often observed in dynamic magnetic resonance (MR) imaging for both susceptibility (DSC) and contrast enhanced (DCE) scans. Motion correction done after scanning is typically the most time-intensive step in the entire measurement process since it involves registering each volume in the time-series to a reference volume. We argue that detecting the presence of motion prior to correction can mitigate this problem by reducing the number of volumes to be corrected. The challenge in motion detection is that the injected contrast alters the signal intensity as a function of time leading to false alarms. We present a robust multi-stage method: subdivision of the time series data into bolus and non-bolus phases; clustering-based identification of bolusaffected pixels followed by correction of their intensity using a Gamma variate function fitting-based method and a 2D block-wise phase correlation for detecting motion between adjacent volumes in DSC-MRI data. The proposed method was tested on a DSC MR sequence with simulated motion of varying degrees. The experimental results show that the entropy of the derived motion fields is a good metric for detecting and categorizing the motion. The proposed scheme when applied prior to correction can achieve on average a 37% reduction in the time required for motion correction.

Time-frequency analysis based motion detection in perfusion weighted MRI

“…Time taken to detect motion is around 3 seconds (see Table I) while it is from 7.68 to 132.21 seconds (depending on block size) in [5]. This reduction in time is due to the fact that proposed method detects motion using one dimensional time series instead of two dimensional images.…”

“…Hence, non-corrupted volumes are also registered which makes the process computationally expensive and it is obvious that these volumes do not need any correction [5]. Therefore, it is preferable to have a prior knowledge about motion corrupted volumes.…”

“…Hence, they may fail to detect motion in perfusion MRI. In [5] and [6], motion is detected by bolus dependent approach. Here, perfusion MRI data is divided into three sets as (i) pre wash-in, (ii) transit and (iii) post wash-out sets.…”