Motion artifact meaning series#
Initial motion-correction approaches involved regression of motion parameters and their derivatives from voxel-wise BOLD time series ( Friston et al., 1996), or regression of the average or "global" signal ( Aguirre et al., 1998 for a recent review, see Murphy and Fox, 2017). Crucially, motion can substantially affect estimates of functional connectivity ( Power et al., 2012 Satterthwaite et al., 2012). This was recognized as an issue in fMRI long ago ( Hajnal et al., 1994 Friston et al., 1996).ĭue to the spin excitation history issue already pointed out by Bullmore et al., 1999( PDF), motion has complex effects on the signal - including increases, decreases or complex waveforms, depending on factors such as the timing, duration and trajectory of motion ( Power et al., 2015, Byrge and Kennedy 2018). Participant in-scanner motion is one of the prominent sources of artefacts in fMRI data. 3 ME-ICA processing for NSPN and BIODEP.2.3 Lagged effects of motion on fMRI time-series.2.2 Relationship of Framewise Displacement to Functional Connectivity.If the algorithm can catch these cases in real-time before the technologist completes the study, additional images can be acquired with the proper metal suppression protocol while the patient is still in the CT suite. If the algorithm can catch these cases retrospectively, they can be reviewed by a quality improvement committee and can be remedied by procedure & policies (strict review of all hardware before CT, use of scout image to guide CT protocol selection for hardware). Another possibility is that the radiologist may mistake an artifact as an abnormality, and may recommend MRI or other additional imaging studies for follow up. endoscopic ultrasound, GI fluoroscopic study) for evaluation of unseen structures. Patient needs to return for a repeat study, or the clinician needs to make decision based on the low-quality study. Radiologist cannot see structures such as bladder, reproductive organs,, and pelvic small bowel. There are marked metal artifacts obscuring the pelvis, limiting evaluation. Thepatient gets a CT of the abdomen/pelvis without metal suppression protocol. The referring physician does not indicate in the CT abdomen/pelvis order that the patient has hip replacement hardware. The technologist and radiologist do not have this information as the referring physician is outside the system. The patient forgets to mention the hardware, as it is in the hip and pain is abdominal. Patient with hip replacement hardware presents for CT for abdominal pain. If the algorithm can catch these cases in real-time before the technologist completes the study, additional images can be obtained while the patient is still in the X-ray suite, and subsequent CT may not need to be performed. If the algorithm can detect these cases retrospectively, they can be reviewed by a quality improvement committee and can be remedied by procedures & policies (e.g., for BMI > 30, use adjusted settings).
The radiologist views the study, notices poor quality, and gives the disclaimer of “cannot rule out fracture.” Emergency room doctor then orders a CT to evaluate for fracture. However, the emergency room is really busy, with multiple studies waiting to be done, the patient is in pain and has difficulty getting into proper position, given all the distractions standard settings are used. Standard kVpand mAs may not be sufficient for an obese patient due to increased scatter. Adjusting kVpand mAs can achieve reasonable image quality for diagnosis of fracture on a radiograph. Obese patient comes to the emergency room after a fall due to knee pain. Furthermore, if the algorithm can detect image quality in real time, it can warn technologists of suboptimal study before patient leaves and study can be re-done in time. If a computer algorithm can detect suboptimal study automatically, it will make it easier for quality improvement committees to review and resolve issues. However, in a busy clinical environment, often times suboptimal images are acquired, and it is impractical for every radiologist to flag every case and to follow up to resolution in an increasingly large, corporate-like healthcare practice setting. Most institutions have ongoing quality improvement procedures and policies to improve and maintain diagnostically acceptable image quality. Image quality is crucial for proper diagnosis from a medical imaging study.
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