Impact of AIR™ Recon DL on magnetic resonance imaging-based quantitative brain structure measurements

We aimed to evaluate how the AIR™ Recon DL algorithm influences magentic resonance imaging (MRI) quality and quantitative brain morphometry relative to conventional reconstruction (CR). Seventy-four healthy adults underwent 3D T1-weighted MRI reconstructed with CR and AIR™ Recon DL. Image quality was rated by two neuroradiologists (κ = 0.74-0.97). Voxel-based morphometry assessed total, gray matter (GM), white matter (WM), and cerebrospinal (CSF) volumes; surface-based morphometry analyzed cortical thickness, sulcal depth, fractal dimension, and gyrification across 148 regions. Hippocampal volumes were extracted using the Neuromorphometrics atlas. Reconstruction times were compared. AIR™ Recon DL significantly improved image quality (reduced noise and artifacts, P < 0.001) but introduced systematic morphometric shifts-smaller total and WM volumes, larger GM and CSF volumes, and widespread regional thickness increases (effect sizes d ≈ 0.3-0.5). Hippocampal volumes increased bilaterally (ΔL = +0.15 mL, +3.97%; ΔR = +0.15 mL, +3.88%; both P < 0.05). Mean reconstruction time was longer for deep learning-based reconstruction (11.6 ± 1.6 s) than CR (9.9 ± 1.4 s; Δ = +1.7 s, P < 0.001). AIR™ Recon DL enhances image quality but causes modest, systematic volumetric biases. Harmonizing reconstruction methods is essential for reliable morphometric comparisons in neuropsychiatric imaging.