Automated three-dimensional image registration for longitudinal photoacoustic imaging

Bruno De Santi*, Lucia Kim, Rianne F.G. Bulthuis, Felix Lucka, Srirang Manohar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Significance: Photoacoustic tomography (PAT) has great potential in monitoring disease progression and treatment response in breast cancer. However, due to variations in breast repositioning, there is a chance of geometric misalignment between images. Further, poor repositioning can affect light fluence distribution and imaging field-of-view, making images different from one another. The net effect is that it becomes challenging to distinguish between image changes due to repositioning effects and those due to true biological variations. Aim: The aim is to develop a three-dimensional image registration framework for geometrically aligning repeated PAT volumetric images, which are potentially affected by repositioning effects such as misalignment, changed radiant exposure conditions, and different fields-of-view. Approach: The proposed framework involves the use of a coordinate-based neural network to represent the displacement field between pairs of PAT volumetric images. A loss function based on normalized cross correlation and Frangi vesselness feature extraction at multiple scales was implemented. We refer to our image registration framework as MUVINN-reg, which stands for multiscale vesselness-based image registration using neural networks. The approach was tested on a longitudinal dataset of healthy volunteer breast PAT images acquired with the hybrid photoacoustic-ultrasound Photoacoustic Mammoscope 3 imaging system. The registration performance was also tested under unfavorable repositioning conditions such as intentional mispositioning, and variation in breast-supporting cup size between measurements. Results: A total of 13 pairs of repeated PAT scans were included in this study. MUVINN-reg showed excellent performance in co-registering each pair of images. The proposed framework was shown to be robust to image intensity shifts and field-of-view changes. Furthermore, MUVINN-reg could align vessels at imaging depths greater than 4 cm. Conclusions: The proposed framework will enable the use of PAT for quantitative and reproducible monitoring of disease progression and treatment response.

Original languageEnglish
Pages (from-to)S11515
JournalJournal of biomedical optics
Volume29
Issue numberS1
DOIs
Publication statusPublished - 13 Jan 2024

Keywords

  • breast imaging
  • coordinate based neural network
  • image registration
  • longitudinal imaging
  • photoacoustic imaging
  • UT-Gold-D

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