Computed tomography lung iodine contrast mapping by image registration and subtraction

Keith Goatman; Costas Plakas; Joanne Schuijf; Erin Beveridge; Mathias Prokop

doi:10.1117/12.2043551

21 March 2014 Computed tomography lung iodine contrast mapping by image registration and subtraction

Keith Goatman, Costas Plakas, Joanne Schuijf, Erin Beveridge, Mathias Prokop

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90343I (2014) https://doi.org/10.1117/12.2043551
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Pulmonary embolism (PE) is a relatively common and potentially life threatening disease, affecting around 600,000 people annually in the United States alone. Prompt treatment using anticoagulants is effective and saves lives, but unnecessary treatment risks life threatening haemorrhage. The specificity of any diagnostic test for PE is therefore as important as its sensitivity. Computed tomography (CT) angiography is routinely used to diagnose PE. However, there are concerns it may over-report the condition. Additional information about the severity of an occlusion can be obtained from an iodine contrast map that represents tissue perfusion. Such maps tend to be derived from dual-energy CT acquisitions. However, they may also be calculated by subtracting pre- and post-contrast CT scans. Indeed, there are technical advantages to such a subtraction approach, including better contrast-to-noise ratio for the same radiation dose, and bone suppression. However, subtraction relies on accurate image registration. This paper presents a framework for the automatic alignment of pre- and post-contrast lung volumes prior to subtraction. The registration accuracy is evaluated for seven subjects for whom pre- and post-contrast helical CT scans were acquired using a Toshiba Aquilion ONE scanner. One hundred corresponding points were annotated on the pre- and post-contrast scans, distributed throughout the lung volume. Surface-to-surface error distances were also calculated from lung segmentations. Prior to registration the mean Euclidean landmark alignment error was 2.57mm (range 1.43–4.34 mm), and following registration the mean error was 0.54mm (range 0.44–0.64 mm). The mean surface error distance was 1.89mm before registration and 0.47mm after registration. There was a commensurate reduction in visual artefacts following registration. In conclusion, a framework for pre- and post-contrast lung registration has been developed that is sufficiently accurate for lung subtraction iodine mapping.

Citation Download Citation

Keith Goatman, Costas Plakas, Joanne Schuijf, Erin Beveridge, and Mathias Prokop "Computed tomography lung iodine contrast mapping by image registration and subtraction", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90343I (21 March 2014); https://doi.org/10.1117/12.2043551

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