Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

To begin, open images followed by masks in the desired image visualization software to verify that the image and mask orientation match for all CT, Proton, and Xenon files. Then, save the image DICOMS and single label masks as NIfTI files in the same folder as the reg. py file.

For CT Xenon MRI registration, open the reg. py file in the desired Python computing environment setup. If using a virtual environment, set the number of central processing units, number of threads, and RAM as desired or as available in the computing environment.

Next, set the desired transformation and interpolation, followed by the fixed and moving image. Run reg. py in the Python computing environment.

Once the registration is complete, proceed for evaluation. Keeping the ct. nii image as the base image, open ventilation warp.nii.

gz as another image and overlay it on the CT image with the desired color map. Review the overlap of the xenon MR image with the CT image in all image planes to evaluate the visual alignment of landmarks such as the carina and lung boundaries. Registration results showed good alignment of all lung boundaries for the healthy participant.

In the three participants with chronic obstructive pulmonary disease, there was good alignment of lung boundaries were available ranging from diffuse ventilation abnormalities, upper lobe ventilation abnormalities with absent apical lung boundaries and lower lobe ventilation abnormalities with absent diaphragmatic lung boundaries.