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

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.