Deep learning-based attenuation map generation and correction; could it be useful clinically?

Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ[Formula: see text]SPECT collimator

BACKGROUND

The use of CT images for attenuation correction of myocardial perfusion imaging with single photon emission computer tomography (SPECT) increases diagnostic confidence. However, acquiring a CT image registered to a SPECT image is often not possible because most scanners are SPECT-only. It is possible to approximate attenuation maps using deep learning, but this has not yet been shown to work for a SPECT scanner with an IQ[Formula: see text]SPECT collimator. This study investigates whether it is possible to approximate attenuation maps from non-attenuation-corrected (nAC) reconstructions acquired with a SPECT scanner equipped with an IQ[Formula: see text]SPECT collimator.

METHODS

Attenuation maps and reconstructions were acquired retrospectively for 150 studies. A U-Net was trained to predict attenuation maps from nAC reconstructions using the conditional generative adversarial network framework. Predicted attenuation maps are compared to real attenuation maps using the normalized mean absolute error (NMAE). Attenuation-corrected reconstructions were computed, and the resulting polar maps were compared by pixel and by average perfusion per segment using the absolute percent error (APE). The training and evaluation code is available at https://gitlab.ub.uni-bielefeld.de/thuxohl/mu-map .

RESULTS

Predicted attenuation maps are similar to real attenuation maps, achieving an NMAE of 0.020±0.007. The same is true for polar maps generated from reconstructions with predicted attenuation maps compared to CT-based attenuation maps. Their pixel-wise absolute distance is 3.095±3.199, and the segment-wise APE is 1.155±0.769.

CONCLUSIONS

It is feasible to approximate attenuation maps from nAC reconstructions acquired by a scanner with an IQ[Formula: see text]SPECT collimator using deep learning.

Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2022: single photon emission computed tomography

In this review, we will summarize a selection of articles on single-photon emission computed tomography published in the Journal of Nuclear Cardiology in 2022. The aim of this review is to concisely recap major advancements in the field to provide the reader a glimpse of the research published in the journal over the last year. This review will place emphasis on myocardial perfusion imaging using single-photon emission computed tomography summarizing advances in the field including in prognosis, non-perfusion variables, attenuation compensation, machine learning and camera design. It will also review nuclear imaging advances in amyloidosis, left ventricular mechanical dyssynchrony, cardiac innervation, and lung perfusion. We encourage interested readers to go back to the original articles, and editorials, for a comprehensive read as necessary but hope that this yearly review will be helpful in reminding readers of articles they have seen and attracting their attentions to ones they have missed.