Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation

• Published in: Radiologia medica Vol. 127; no. 10; pp. 1098 - 1105
• Main Authors: Zerunian, Marta, Pucciarelli, Francesco, Caruso, Damiano, Polici, Michela, Masci, Benedetta, Guido, Gisella, De Santis, Domenico, Polverari, Daniele, Principessa, Daniele, Benvenga, Antonella, Iannicelli, Elsa, Laghi, Andrea
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 01-10-2022; Springer Nature B.V
• Subjects: Abdominal Radiology; Algorithms; Artificial intelligence; Diagnostic Radiology; Diffusion coefficient; Diffusion rate; Image acquisition; Image analysis; Image contrast; Image enhancement; Image quality; Imaging; Interventional Radiology; Liver; Machine learning; Medicine; Medicine & Public Health; Neuroradiology; Qualitative analysis; Quality assessment; Radiology; Scanning; Signal to noise ratio; Ultrasound; Image quality; Sequences optimization; Artificial intelligence; Scanning time
• ISSN: 1826-6983; 0033-8362; 1826-6983
• DOI: 10.1007/s11547-022-01539-9

Purpose
To compare liver MRI with AIR Recon Deep Learning™(ARDL) algorithm applied and turned-off (NON-DL) with conventional high-resolution acquisition (NAÏVE) sequences, in terms of quantitative and qualitative image analysis and scanning time.
Material and methods
This prospective study included fifty consecutive volunteers (31 female, mean age 55.5 ± 20 years) from September to November 2021. 1.5 T MRI was performed and included three sets of images: axial single-shot fast spin-echo (SSFSE) T2 images, diffusion-weighted images(DWI) and apparent diffusion coefficient(ADC) maps acquired with both ARDL and NAÏVE protocol; the NON-DL images, were also assessed. Two radiologists in consensus drew fixed regions of interest in liver parenchyma to calculate signal-to-noise-ratio (SNR) and contrast to-noise-ratio (CNR). Subjective image quality was assessed by two other radiologists independently with a five-point Likert scale. Acquisition time was recorded.
Results
SSFSE T2 objective analysis showed higher SNR and CNR for ARDL vs NAÏVE, ARDL vs NON-DL(all
P
 < 0.013). Regarding DWI, no differences were found for SNR with ARDL vs NAÏVE and, ARDL vs NON-DL (all
P
 > 0.2517).CNR was higher for ARDL vs NON-DL(
P
 = 0.0170), whereas no differences were found between ARDL and NAÏVE(
P
 = 1). No differences were observed for all three comparisons, in terms of SNR and CNR, for ADC maps (all
P
 > 0.32).
Qualitative analysis for all sequences showed better overall image quality for ARDL with lower truncation artifacts, higher sharpness and contrast (all
P
 < 0.0070) with excellent inter-rater agreement (
k
 ≥ 0.8143). Acquisition time was lower in ARDL sequences compared to NAÏVE (SSFSE T2 = 19.08 ± 2.5 s vs. 24.1 ± 2 s and DWI = 207.3 ± 54 s vs. 513.6 ± 98.6 s, all
P
 < 0.0001).
Conclusion
ARDL applied on upper abdomen showed overall better image quality and reduced scanning time compared with NAÏVE protocol.