Prediction of Motion Induced Image Degradation Using a Markerless Motion Tracker

Rasmus Munch Olsen; Helle Hjorth Johannesen; Otto Mølby Henriksen; Lisbeth Marner; Oline Vinter Olesen

Prediction of Motion Induced Image Degradation Using a Markerless Motion Tracker

Rasmus Munch Olsen, Helle Hjorth Johannesen, Otto Mølby Henriksen, Lisbeth Marner, Oline Vinter Olesen

Copenhagen University Hospital Herlev and Gentofte

Research output: Contribution to conference › Conference abstract for conference › Research › peer-review

Abstract

In this work a markerless motion tracker, TCL2, is used to predict image quality in 3D T1 weighted MPRAGE MRI brain scans. An experienced radiologist scored the image quality for 172 scans as being usable or not usable, i.e. if a repeated scan was required. Based on five motion parameters, a classification algorithm was trained and an accuracy for identifying not usable images of 95.9% was obtained with a sensitivity of 91.7% and specificity of 96.3%. This work shows the feasibility of the markerless motion tracker for predicting image quality with a high accuracy.

Original language	English
Publication date	2017
Publication status	Published - 2017
Event	ISMRM 25th Annual Meeting & Exhibition - Hawai'i Convention Center, Honolulu, United States Duration: 22 Apr 2017 → 27 Apr 2017

Conference

Conference	ISMRM 25th Annual Meeting & Exhibition
Location	Hawai'i Convention Center
Country/Territory	United States
City	Honolulu
Period	22/04/2017 → 27/04/2017

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title = "Prediction of Motion Induced Image Degradation Using a Markerless Motion Tracker",

abstract = "In this work a markerless motion tracker, TCL2, is used to predict image quality in 3D T1 weighted MPRAGE MRI brain scans. An experienced radiologist scored the image quality for 172 scans as being usable or not usable, i.e. if a repeated scan was required. Based on five motion parameters, a classification algorithm was trained and an accuracy for identifying not usable images of 95.9% was obtained with a sensitivity of 91.7% and specificity of 96.3%. This work shows the feasibility of the markerless motion tracker for predicting image quality with a high accuracy.",

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language = "English",

note = " ISMRM 25th Annual Meeting & Exhibition, ISMRM 2017 ; Conference date: 22-04-2017 Through 27-04-2017",

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T1 - Prediction of Motion Induced Image Degradation Using a Markerless Motion Tracker

AU - Olsen, Rasmus Munch

AU - Johannesen, Helle Hjorth

AU - Henriksen, Otto Mølby

AU - Marner, Lisbeth

AU - Olesen, Oline Vinter

PY - 2017

Y1 - 2017

N2 - In this work a markerless motion tracker, TCL2, is used to predict image quality in 3D T1 weighted MPRAGE MRI brain scans. An experienced radiologist scored the image quality for 172 scans as being usable or not usable, i.e. if a repeated scan was required. Based on five motion parameters, a classification algorithm was trained and an accuracy for identifying not usable images of 95.9% was obtained with a sensitivity of 91.7% and specificity of 96.3%. This work shows the feasibility of the markerless motion tracker for predicting image quality with a high accuracy.

AB - In this work a markerless motion tracker, TCL2, is used to predict image quality in 3D T1 weighted MPRAGE MRI brain scans. An experienced radiologist scored the image quality for 172 scans as being usable or not usable, i.e. if a repeated scan was required. Based on five motion parameters, a classification algorithm was trained and an accuracy for identifying not usable images of 95.9% was obtained with a sensitivity of 91.7% and specificity of 96.3%. This work shows the feasibility of the markerless motion tracker for predicting image quality with a high accuracy.

M3 - Conference abstract for conference

T2 - ISMRM 25th Annual Meeting & Exhibition

Y2 - 22 April 2017 through 27 April 2017

ER -

Prediction of Motion Induced Image Degradation Using a Markerless Motion Tracker

Abstract

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