比较深度学习加速全身 (WB) 与传统扩散序列的图像质量。连续 50 名骨髓癌患者接受了 WB-MRI。两位专家将深度分辨增强 (DRB) 加速扩散加权成像 (DWI) 序列（采集时间：6:42 分钟）的轴向 b900 s/mm2 和相应的最大强度投影 (MIP) 与传统序列（采集时间）进行了比较：14分钟）。读者使用李克特量表评估配对图像的噪声、伪影、信号脂肪抑制和病变明显性，也表达了他们的整体主观偏好。对正常组织和癌症病灶的信噪比和对比噪声比（SNR 和 CNR）以及表观扩散系数（ADC）值进行统计比较。总体而言，放射科医生更喜欢轴向 DRB b900 和/或相应的 MIP 图像近 80% 的患者，尤其是体重指数较高的患者 (BMI > 25kg/m2)。在定性评估中，56-100% 的病例首选轴向 DRB 图像（首选/强烈首选），而 52-96% 的病例首选 DRB MIP 图像。所有正常组织中的 DRB-SNR/CNR 均较高 (p<<0.05)。对于癌症病变，DRB-SNR 较高 (p<0.001)，但 CNR 没有差异。大脑和腰肌的 DRB-ADC 值显着较高，但癌症病变则不然（平均差：53μm2/s）。类间相关系数分析显示出良好到极好的一致性（95% CI 0.75-0.93）。DRB 序列产生更高质量的轴向 DWI，从而改善 MIP 并显着减少采集时间。然而，需要考虑正常组织 ADC 值的差异。深度学习加速扩散序列可在减少采集时间的情况下生成高质量的轴向图像和 MIP。这一进步可以使更多地采用全身 MRI 来评估骨髓癌患者。深度学习重建可以将 WB 扩散序列的采集时间缩短 50% 以上。在近 80% 的病例中，放射科医生更喜欢 DRB 图像，因为在体重指数较高的患者中，伪影较少，背景信号抑制得到改善，信噪比较高，并且病灶更加明显。 DRB 图像中的癌症病变扩散率与传统序列没有不同。© 2024。作者，获得欧洲放射学会的独家许可。

To compare the image quality of deep learning accelerated whole-body (WB) with conventional diffusion sequences.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.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).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.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.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.© 2024. The Author(s), under exclusive licence to European Society of Radiology.