Image quality of whole-body diffusion MR images comparing deep-learning accelerated and conventional sequences

Abstract

Objectives: To compare the image quality of deep learning accelerated whole-body (WB) with conventional diffusion sequences.

Methods: Fifty consecutive patients with bone marrow cancer underwent WB-MRI. Two experts compared axial b900 s/mm² and the corresponding maximum intensity projections (MIP) of deep resolve boost (DRB) accelerated diffusion-weighted imaging (DWI) sequences (time of acquisition: 6:42 min) against conventional sequences (time of acquisition: 14 min). Readers assessed paired images for noise, artefacts, signal fat suppression, and lesion conspicuity using Likert scales, also expressing their overall subjective preference. Signal-to-noise and contrast-to-noise ratios (SNR and CNR) and the apparent diffusion coefficient (ADC) values of normal tissues and cancer lesions were statistically compared.

Results: Overall, radiologists preferred either axial DRB b900 and/or corresponding MIP images in almost 80% of the patients, particularly in patients with a high body-mass index (BMI > 25 kg/m²). In qualitative assessments, axial DRB images were preferred (preferred/strongly preferred) in 56-100% of cases, whereas DRB MIP images were favoured in 52-96% of cases. DRB-SNR/CNR was higher in all normal tissues (p < 0.05). For cancer lesions, the DRB-SNR was higher (p < 0.001), but the CNR was not different. DRB-ADC values were significantly higher for the brain and psoas muscles, but not for cancer lesions (mean difference: + 53 µm²/s). Inter-class correlation coefficient analysis showed good to excellent agreement (95% CI 0.75-0.93).

Conclusion: DRB sequences produce higher-quality axial DWI, resulting in improved MIPs and significantly reduced acquisition times. However, differences in the ADC values of normal tissues need to be considered.

Clinical relevance statement: Deep learning accelerated diffusion sequences produce high-quality axial images and MIP at reduced acquisition times. This advancement could enable the increased adoption of Whole Body-MRI for the evaluation of patients with bone marrow cancer.

Key points: Deep learning reconstruction enables a more than 50% reduction in acquisition time for WB diffusion sequences. DRB images were preferred by radiologists in almost 80% of cases due to fewer artefacts, improved background signal suppression, higher signal-to-noise ratio, and increased lesion conspicuity in patients with higher body mass index. Cancer lesion diffusivity from DRB images was not different from conventional sequences.

Keywords: Artificial intelligence; Cancer; Deep-learning; Diffusion-weighted imaging; Magnetic resonance imaging.