Pulmonary Transit Time Can Be Accurately Quantified From Non‐Arterial Input Function Series in First‐Pass Cardiac Perfusion MRI

TL;DR

This study shows that pulmonary transit time can be accurately measured using non-arterial images in cardiac MRI, without needing arterial input functions.

Contribution

The study introduces centroid-based methods for accurate PTT estimation from non-AIF images in cardiac perfusion MRI.

Findings

Centroid-based methods showed low bias and narrower limits of agreement compared to peak-to-peak timing.

Both AUC and curve-fitting centroid methods provided reliable PTT estimates from non-AIF images.

Results were consistent across radial perfusion and qPerf datasets.

Abstract

Previous studies suggested that arterial‐input‐function (AIF) images are necessary to avoid signal saturation for pulmonary transit time (PTT) measurements. This study challenges that notion by investigating whether PTT can be accurately measured using blood pool signals from myocardial enhancement (non‐AIF) images during resting first‐pass perfusion MRI.

This retrospective study included 108 patients, 47 scanned using a research radial perfusion sequence, and 61 scanned with a prototype Cartesian quantitative perfusion (qPerf) sequence with inline PTT calculation. An automated pipeline was developed to compute PTT measurements from blood pool signals extracted from both AIF and non‐AIF images, as well as from gadolinium concentration curves of the AIF images, using three methods: peak‐to‐peak timing, area‐under‐the‐curve (AUC) derived centroids, and curve‐fitting derived centroids.

Peak‐to‐peak PTT showed low bias (0.10–0.56 s; [1.4%–7.4%] of the mean), wide limits of agreement (5.71–7.42 s; [74.5%–109.8%]). In contrast, centroid‐based methods, using both AUC and curve‐fitting approaches, consistently yielded low bias (< 0.49 s; < 6.3%), narrower limits of agreement (1.83–2.84 s; 25.2%–36.8%). These findings indicate that centroid methods offer more precise and reliable PTT estimation across both radial perfusion and qPerf datasets.

PTT can be derived accurately from non‐AIF images using either the AUC or curve‐fitting centroid‐to‐centroid method.

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