Natalia V Korobova, Nienke P M Wassenaar, Marian A Troelstra, Eric M Schrauben, Oliver J Gurney-Champion
{"title":"校正建模径向,螺旋和螺旋桨动态对比度增强数据:时间平均扩展Tofts。","authors":"Natalia V Korobova, Nienke P M Wassenaar, Marian A Troelstra, Eric M Schrauben, Oliver J Gurney-Champion","doi":"10.1002/mrm.30514","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Dynamic contrast-enhanced sequences (e.g. spiral, radial, PROPELLER MRI) often rely on oversampling the center of k-space. Instead of the discrete snapshots obtained by Cartesian sampling, oversampling the k-space center results in time-averaging of the signal. We hypothesize that these time-averaged signals decrease the accuracy of pharmacokinetic modeling and propose a model that accounts for this effect.</p><p><strong>Theory and methods: </strong>To test our hypothesis, a modified extended Tofts model tailored to accommodate time-averaged signals is proposed. Simulated Monte Carlo experiments were conducted to compare the performance of the modified model with the conventional model. Additionally, to validate the findings in vivo, models were fitted to pseudo-spiral variable-density dynamic contrast-enhanced MRI scans of pancreatic cancer patients reconstructed at 4, 8, 10, and 15 s/frame.</p><p><strong>Results: </strong>The simulations demonstrated that for time-averaged acquisitions, our modified extended Tofts model provided more accurate and precise results than conventional models. Additionally, by integrating signals, some information on high temporal behavior was recovered. Particularly, at long acquisitions (15 s/frame), variable-density sampling with the modified model outperformed conventional discrete sampling. In vivo experiments confirmed these findings, as the corrected model showed more consistent estimates of parameters <math> <semantics> <mrow><msub><mi>v</mi> <mi>p</mi></msub> </mrow> <annotation>$$ {v}_p $$</annotation></semantics> </math> and <math> <semantics> <mrow><msub><mi>v</mi> <mi>e</mi></msub> </mrow> <annotation>$$ {v}_e $$</annotation></semantics> </math> over the tested sampling frequencies, highlighting its potential to improve accuracy in clinical settings.</p><p><strong>Conclusion: </strong>Our study demonstrates that time-averaged signals lead to decreased accuracy and precision in pharmacokinetic modeling when ignored. We suggest using our corrected pharmacokinetic model when performing dynamic contrast-enhanced with variable-density acquisitions, especially for dynamic scan times that are 8 s and longer.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A correction for modeling radial, spiral, and PROPELLER dynamic contrast-enhanced data: Time-averaged extended Tofts.\",\"authors\":\"Natalia V Korobova, Nienke P M Wassenaar, Marian A Troelstra, Eric M Schrauben, Oliver J Gurney-Champion\",\"doi\":\"10.1002/mrm.30514\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Dynamic contrast-enhanced sequences (e.g. spiral, radial, PROPELLER MRI) often rely on oversampling the center of k-space. 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引用次数: 0
摘要
目的:动态对比增强序列(如螺旋、径向、螺旋桨MRI)通常依赖于k空间中心的过采样。与笛卡尔采样获得的离散快照不同,k空间中心的过采样导致信号的时间平均。我们假设这些时间平均信号降低了药代动力学建模的准确性,并提出了一个模型来解释这种影响。理论和方法:为了验证我们的假设,我们提出了一个改进的扩展Tofts模型,以适应时间平均信号。通过模拟蒙特卡罗实验,比较了改进模型与常规模型的性能。此外,为了在体内验证研究结果,将模型拟合到以4,8,10和15 s/帧重建的胰腺癌患者的伪螺旋变密度动态对比增强MRI扫描中。结果:模拟结果表明,对于时间平均采集,改进的扩展Tofts模型比传统模型提供了更准确和精确的结果。此外,通过对信号的整合,恢复了一些高时间行为的信息。特别是,在长时间采集(15秒/帧)时,使用改进模型的变密度采样优于传统的离散采样。体内实验证实了这些发现,因为校正后的模型在测试的采样频率上显示出更一致的参数v p $$ {v}_p $$和ve $$ {v}_e $$,突出了其在临床环境中提高准确性的潜力。结论:我们的研究表明,忽略时间平均信号会导致药代动力学建模的准确性和精密度下降。我们建议在使用可变密度采集进行动态对比增强时使用校正后的药代动力学模型,特别是在动态扫描时间为8秒或更长时。
A correction for modeling radial, spiral, and PROPELLER dynamic contrast-enhanced data: Time-averaged extended Tofts.
Purpose: Dynamic contrast-enhanced sequences (e.g. spiral, radial, PROPELLER MRI) often rely on oversampling the center of k-space. Instead of the discrete snapshots obtained by Cartesian sampling, oversampling the k-space center results in time-averaging of the signal. We hypothesize that these time-averaged signals decrease the accuracy of pharmacokinetic modeling and propose a model that accounts for this effect.
Theory and methods: To test our hypothesis, a modified extended Tofts model tailored to accommodate time-averaged signals is proposed. Simulated Monte Carlo experiments were conducted to compare the performance of the modified model with the conventional model. Additionally, to validate the findings in vivo, models were fitted to pseudo-spiral variable-density dynamic contrast-enhanced MRI scans of pancreatic cancer patients reconstructed at 4, 8, 10, and 15 s/frame.
Results: The simulations demonstrated that for time-averaged acquisitions, our modified extended Tofts model provided more accurate and precise results than conventional models. Additionally, by integrating signals, some information on high temporal behavior was recovered. Particularly, at long acquisitions (15 s/frame), variable-density sampling with the modified model outperformed conventional discrete sampling. In vivo experiments confirmed these findings, as the corrected model showed more consistent estimates of parameters and over the tested sampling frequencies, highlighting its potential to improve accuracy in clinical settings.
Conclusion: Our study demonstrates that time-averaged signals lead to decreased accuracy and precision in pharmacokinetic modeling when ignored. We suggest using our corrected pharmacokinetic model when performing dynamic contrast-enhanced with variable-density acquisitions, especially for dynamic scan times that are 8 s and longer.
期刊介绍:
Magnetic Resonance in Medicine (Magn Reson Med) is an international journal devoted to the publication of original investigations concerned with all aspects of the development and use of nuclear magnetic resonance and electron paramagnetic resonance techniques for medical applications. Reports of original investigations in the areas of mathematics, computing, engineering, physics, biophysics, chemistry, biochemistry, and physiology directly relevant to magnetic resonance will be accepted, as well as methodology-oriented clinical studies.