In vivo co-registered hybrid-contrast imaging by successive photoacoustic tomography and magnetic resonance imaging

Abstract

Magnetic resonance imaging (MRI) and photoacoustic tomography (PAT) are two advanced imaging modalities that offer two distinct image contrasts: MRI has a multi-parameter contrast mechanism that provides excellent anatomical soft tissue contrast, whereas PAT is capable of mapping tissue physiological metabolism and exogenous contrast agents with optical specificity. Attempts have been made to integrate these two modalities, but rigid and reliable registration of the images for in vivo imaging is still challenging. In this paper, we present a complete hardware-software solution for the successive acquisition and co-registration of PAT and MRI images in in vivo animal studies. Based on commercial PAT and MRI scanners, our solution includes a 3D-printed dual-modality animal imaging bed, a spatial image co-registration algorithm with bi-model markers, and a robust modality switching protocol for in vivo imaging studies. Using the proposed solution, we successfully demonstrated co-registered hybrid-contrast PAT-MRI imaging that simultaneously display multi-scale anatomical, functional and molecular characteristics on healthy and cancerous living mice. Week-long longitudinal dual-modality imaging of tumor development reveals information on size, border, vascular pattern, blood oxygenation, and molecular probe metabolism of the tumor micro-environment at the same time. Additionally, by incorporating soft-tissue information in the co-registered MRI image, we further show that PAT image quality could be enhanced by MRI-guided light fluence correction. The proposed methodology holds the promise for a wide range of pre-clinical research applications that benefit from the PAT-MRI dual-modality image contrast.

Multimodal Contrast Agents for Optoacoustic Brain Imaging in Small Animals

“…Co-registration and post-processing of small-animal neuroimage datasets acquired sequentially using OA imaging and other modalities have been performed for the region/volume of interest analysis ( Attia et al, 2016 ; Ren et al, 2019 ). For this, manual/semi-automatic atlas–based analysis and algorithms have been developed ( Ren et al, 2019 ; Ren et al, 2021 ; Zhang et al, 2021b ). Further studies to develop a deep learning–based method for fully automatic segmentation and registration are needed, for example, between OA/MRI or OA/CT brain imaging data and for position-dependent light fluence correction hold great promise ( Sarah et al, 2019 ; Waterhouse et al, 2019 ; Ni et al, 2020a ; Dean-Ben et al, 2020 ; Hu et al, 2021 ).…”

“…Further studies to develop a deep learning–based method for fully automatic segmentation and registration are needed, for example, between OA/MRI or OA/CT brain imaging data and for position-dependent light fluence correction hold great promise ( Sarah et al, 2019 ; Waterhouse et al, 2019 ; Ni et al, 2020a ; Dean-Ben et al, 2020 ; Hu et al, 2021 ). Additionally, bimodal animal holder ( Gehrung et al, 2020 ; Zhang et al, 2021b ) or concurrent imaging acquisition OA tomography–MRI, OA–fluorescence confocal microscopy, and OA tomography–fluorescence imaging have already been developed ( Chen et al, 2017 ; Zhang et al, 2018b ; Liu et al, 2019b ; Ren et al, 2021 ; Zhang et al, 2021c ; Dadkhah and Jiao, 2021 ; Deán-Ben et al, 2021 ). Further development in synchronized OA-MR platforms for small-animal brain imaging for simultaneous detection will further improve the workflow ( Ren et al, 2021 ).…”

Pixel-wise reconstruction of tissue absorption coefficients in photoacoustic tomography using a non-segmentation iterative method

“…The SBIC method first segments the object contours in the PAT image and assumes a uniform value [ Fig. 1 (d)], and then iteratively calculate the value to minimize the error between the product and the un-corrected PAT image [32] , [33] . It can be expressed as [ Fig.…”

“…Recently, Pattyn et al proposed to use co-registered US images to segment phantom images [24] . Zhang et al proposed to use co-registered MR images to obtain a more refined segmentation of animal organs to improve LF estimation [33] . These attempts focused on improving segmentation efficiency and accuracy.…”

Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics

“…Sequential-mode multimodal imaging with OAT-CT and OAT-MRI has also been reported ( 74 – 79 ). Coregistration of images sequentially acquired with OA and other methods is performed for volume-of-interest analysis using dedicated algorithms ( 80 , 81 ), either software-based ( 82 – 85 ) or a hardware-assisted protocol based on stable bimodal imaging support and a rigorous data acquisition procedure ( 81 , 86 ). Similar to FMT-MRI and PET-MRI hybrid systems, the combination of OAT and MRI is highly restricted by the limited space inside the MRI bore and electromagnetic interference mainly caused by radiofrequency (RF) coils.…”