Fast learning-based registration of sparse 3D clinical images

We introduce SparseVM, a method to register clinical 3D MR scans faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many medical image applications such as longitudinal population studies. Most registration algorithms are designed for high-resolution research-quality scans, and under-perform when applied to clinical data. Clinical scans present unique challenges because, in contrast to research-quality scans, clinical scans are often sparse, missing up to 85% of the slices available in research scans. We build on a state-of-the-art learning-based registration method to improve the accuracy of sparse clinical image registration. We evaluate our method on both a clinically-acquired MRI dataset of stroke patients, and on a simulated sparse clinical scan dataset. SparseVM registers MR scans in under a second on a GPU, which is over 1000 x faster than the most accurate clinical registration methods, without compromising accuracy. SparseVM has statistically significant improvements in runtimes over all baselines, and a statistically significant improvement in accuracy over the fastest baseline. Because of these contributions, SparseVM enables clinical analyses that were not previously possible. Our code is publicly available at voxelmorph. mit . edu.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Lewis, Kathleen M.(Kathleen Marie)

Advisor dc:contributor.advisor

• John V. Guttag and Adrian V. Dalca.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122546

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122546