目的:比较深度学习加速全身(WB)与常规扩散序列的图像质量。
方法:连续50例骨髓癌患者接受WB-MRI检查。两位专家比较了轴向b900s/mm2和相应的深分辨率增强(DRB)加速扩散加权成像(DWI)序列(采集时间:6:42分钟)与常规序列(采集时间:14分钟)的最大强度投影(MIP)。读者评估配对图像的噪声,人工制品,信号脂肪抑制,和使用李克特量表的病变显著性,也表达了他们的整体主观偏好。统计比较正常组织和癌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。
结果:总体而言,在几乎80%的患者中,放射科医生首选轴向DRBb900和/或相应的MIP图像,特别是高体重指数(BMI>25kg/m2)的患者。在定性评估中,在56-100%的病例中,轴向DRB图像是首选(首选/强烈首选),而DRBMIP图像在52-96%的病例中受到青睐。所有正常组织中DRB-SNR/CNR均较高(p<0.05)。对于癌症病变,DRB-SNR较高(p<0.001),但CNR并没有什么不同。大脑和腰大肌的DRB-ADC值明显更高,但不适用于癌症病变(平均差:+53µm2/s)。类间相关系数分析显示良好至优异的一致性(95%CI0.75-0.93)。
结论:DRB序列产生更高质量的轴向DWI,从而改善MIP并显著减少采集时间。然而,需要考虑正常组织ADC值的差异。
结论:深度学习加速扩散序列以减少的采集时间产生高质量的轴向图像和MIP。这种进步可以使全身MRI用于评估骨髓癌患者的更多采用。
结论:深度学习重建能够使WB扩散序列的采集时间减少50%以上。在近80%的病例中,由于伪影较少,放射科医生更喜欢DRB图像。改进的背景信号抑制,更高的信噪比,体重指数较高的患者的病变显著性增加。来自DRB图像的癌症病变扩散率与常规序列没有不同。
OBJECTIVE: 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).
CONCLUSIONS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.