Automatic detection of contrast enhancement in T1-weighted brain MRI of human adults

Mikhail Milchenko, Pamela Lamontagne, Daniel Marcus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Retrospective neuro-oncology imaging research relies on standardization of large, heterogeneous sets of clinical images. In particular, many tumor segmentation algorithms require pre- and post- Gadolinium (Gd) T1-weighted MRI scans. Since the presence of contrast agent cannot be reliably inferred from image metadata, we propose an automatic imagebased classifier for this purpose. We proceed with aligning a T1-weighted MR image to a standard atlas space, by selecting one of eight affine transforms produced using different registration parameters and atlases. After resampling to the standard space, we normalize the intensity distribution and compute intensity characteristics inside a pre-built binary mask of likely enhancement. Using a labeled set of 1892 scans, we evaluated logistic regressions with mean, standard deviation, and 95th percentile as possible factors. A univariable logistic regression with standard deviation as factor was most accurate at 98.9% on testing data. The slope coefficient was highly robust with p<1e-6 and Cramer-Rao bound on variance of 1%. The resulting classification script is completely unsupervised. Accuracy on two validation datasets from different sources (totaling 1328 scans) was over 99% on scans with isotropic sampling. Accuracy was lower on highly anisotropic or otherwise lower quality scans. To our knowledge, this is the first attempt to build an automated Gd enhancement classifier for big data applications. We plan to integrate it into XNAT platform for automatic labeling, to enable Gd enhanced image search. The proposed detector performed well on a wide variety of acquisition parameters. Image anisotropy and acquisition artifacts may interfere with accurate detection.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
StatePublished - Jan 1 2020
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period02/16/2002/19/20

Keywords

  • automatic labeling
  • contrast detetection
  • neuro-oncology mri
  • spatial normalization

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