DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-12-01 Epub Date: 2024-08-08 DOI:10.1007/s10334-024-01189-0
Ouri Cohen, Soudabeh Kargar, Sungmin Woo, Alberto Vargas, Ricardo Otazo
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Abstract

Introduction: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption.

Methods: A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor.

Results: The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%.

Conclusion: The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

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DCE-Qnet:动态对比增强(DCE)磁共振成像的深度网络量化。
导言:动态对比增强(DCE)-MRI 的定量化有可能提供有价值的临床信息,但在临床应用中建立可靠的药代动力学模型仍是一项挑战:动态造影剂增强(DCE)-MRI 的定量分析有望提供有价值的临床信息,但健全的药代动力学建模仍是临床采用的一项挑战:方法:使用帕克动脉输入函数,在扩展托夫斯模型得出的模拟 DCE-MRI 信号上训练了一个名为 DCE-Qnet 的 7 层神经网络。网络训练结合了 B1 不均匀性,以估计灌注(Ktrans、vp、ve)、组织 T1 弛豫、质子密度和栓子到达时间(BAT)。与传统的非线性最小二乘拟合法(NLSQ)相比,该方法的准确性在数字模型中进行了测试。在十名健康受试者身上进行了活体测试。宫颈和子宫肌层的感兴趣区用于计算受试者之间的变异性。对一名宫颈癌患者进行了临床实用性验证。测试-重测实验用于评估肿瘤参数图的再现性:结果:在模型中,DCE-Qnet 重建优于 NLSQ。根据参数的不同,健康宫颈的变异系数(CV)在 5% 到 51% 之间。尽管方法不同,但肿瘤中的参数值与之前的研究一致。肿瘤中的变异系数在 1% 到 47% 之间:结论:建议的方法可通过一次采集提供全面的 DCE-MRI 定量。DCE-Qnet 无需进行单独的 T1 扫描或 BAT 处理,因此每次扫描可缩短 10 分钟,量化结果也更准确。
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来源期刊
CiteScore
4.60
自引率
0.00%
发文量
58
审稿时长
>12 weeks
期刊介绍: MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include: advances in materials, hardware and software in magnetic resonance technology, new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine, study of animal models and intact cells using magnetic resonance, reports of clinical trials on humans and clinical validation of magnetic resonance protocols.
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