Assessment of accuracy and repeatability of quantitative parameter mapping in MRI.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiological Physics and Technology Pub Date : 2024-12-01 Epub Date: 2024-08-28 DOI:10.1007/s12194-024-00836-4
Yuya Hirano, Kinya Ishizaka, Hiroyuki Sugimori, Yo Taniguchi, Tomoki Amemiya, Yoshitaka Bito, Kohsuke Kudo
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引用次数: 0

Abstract

We aimed to evaluate the accuracy and repeatability of the T1, T2*, and proton density (PD) values obtained by quantitative parameter mapping (QPM) using the ISMRM/NIST MRI system phantom and compared them with computer simulations. We compared the relaxation times and PD obtained through QPM with the reference values of the ISMRM/NIST MRI system phantom and conventional methods. Furthermore, we evaluated the presence or absence of influences other than noise in T1 and T2* values obtained by QPM by comparing the obtained coefficient of variation (CV) with simulation results. The T1, T2*, and PD values by QPM showed a strong correlation with the measured values and the referenced values. The simulated CVs of QPM calculated for each sphere showed similar trends to those of the actual scans.

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评估磁共振成像定量参数绘图的准确性和可重复性。
我们的目的是评估利用 ISMRM/NIST MRI 系统模型通过定量参数绘图 (QPM) 获得的 T1、T2* 和质子密度 (PD) 值的准确性和可重复性,并将其与计算机模拟进行比较。我们将通过 QPM 获得的弛豫时间和 PD 与 ISMRM/NIST MRI 系统模型和传统方法的参考值进行了比较。此外,我们还通过比较 QPM 获得的变异系数 (CV) 与模拟结果,评估了 QPM 获得的 T1 和 T2* 值中是否存在噪音以外的影响因素。QPM 得出的 T1、T2* 和 PD 值与测量值和参考值有很强的相关性。为每个球体计算的 QPM 模拟变异系数与实际扫描的趋势相似。
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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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Acknowledgment. Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy. Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy. Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography. Assessment of accuracy and repeatability of quantitative parameter mapping in MRI.
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