Computers in biology and medicine · 2014
Automatic measurements of arterial input and venous output functions on cerebral computed tomography perfusion images: a preliminary study.
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Kao Yi-Hsuan, Mu Huo Teng Michael, Kao Yi-Tzu, Chen Yi-Ju, Wu Chen-Hsin, Chen Wen-Chun, Chiu Fang-Ying, Chang Feng-Chi
Аннотация
BACKGROUND: The current automatic techniques for measuring arterial input function (AIF) and venous output (VOF) on cerebral computed tomography perfusion images are prone to motion artifact and random noise, and their failure rates vary between 10% and 65%. We developed a new automatic technique to overcome these problems.
METHODS: A principle axis transformation was applied to perfusion images to correct for translational and rotational motion artifacts. Bone voxels and neighboring voxels were removed from the perfusion images. Only brain voxels were included in the AIF and VOF measurement procedures. The selection criteria, such as large area under the concentration-time curve, early arrival of contrast agents, and narrow effective width, were used to select appropriate arterial and venous voxels for the AIF and VOF measurements. The proposed automatic technique was tested in 20 patients with unilateral cerebral arterial stenosis. The results of the proposed technique were compared to the results obtained by manual measurements and commercially available automatic selection software.
RESULTS: The AIFs and VOFs were successfully measured using the proposed automatic technique in all 20 patients. The curve shapes, including the area under the concentration-time curve, peak concentration, time to peak, and effective width of the automatically measured AIFs or VOFs were comparable to that were measured manually.
CONCLUSION: The proposed automatic measurement technique successfully overcomes the motion artifact and random noise problems encountered in measuring AIF and VOF. It can be easily integrated into software for the automatic calculation of cerebral blood volume and flow.
METHODS: A principle axis transformation was applied to perfusion images to correct for translational and rotational motion artifacts. Bone voxels and neighboring voxels were removed from the perfusion images. Only brain voxels were included in the AIF and VOF measurement procedures. The selection criteria, such as large area under the concentration-time curve, early arrival of contrast agents, and narrow effective width, were used to select appropriate arterial and venous voxels for the AIF and VOF measurements. The proposed automatic technique was tested in 20 patients with unilateral cerebral arterial stenosis. The results of the proposed technique were compared to the results obtained by manual measurements and commercially available automatic selection software.
RESULTS: The AIFs and VOFs were successfully measured using the proposed automatic technique in all 20 patients. The curve shapes, including the area under the concentration-time curve, peak concentration, time to peak, and effective width of the automatically measured AIFs or VOFs were comparable to that were measured manually.
CONCLUSION: The proposed automatic measurement technique successfully overcomes the motion artifact and random noise problems encountered in measuring AIF and VOF. It can be easily integrated into software for the automatic calculation of cerebral blood volume and flow.