A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases.
A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases.
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用于改进全身扩散加权成像数据评估和前列腺癌骨转移评估的多室扩散模型。
DOI：
10.1148/rycan.210115
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Dale,AndersM
中科院分区：
文献类型：
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作者：
Conlin,ChristopherC;Feng,ChristineH;Digma,LeonardinoA;Rodríguez-Soto,AnaE;Kuperman,JoshuaM;Rakow-Penner,Rebecca;Karow,DavidS;White,NathanS;Seibert,TylerM;Hahn,MichaelE;Dale,AndersM
目的开发全身弥散加权成像 (DWI) 的多室信号模型，并将其应用于体内研究正常组织和转移性前列腺癌骨病变的弥散特性。 材料和方法这项前瞻性研究 (ClinicalTrials.gov: NCT03440554) 包括 139 名前列腺癌男性（平均年龄，70 岁 ± 9 [SD]）。 将具有 2 至 4 个组织区室的多区室模型与全身扫描的 DWI 数据进行拟合，以确定最佳的区室扩散系数。计算贝叶斯信息准则（BIC）和模型拟合残差来量化模型复杂性和拟合优度。最佳模型（具有最低 BIC）的扩散系数用于计算区室信号贡献图。 在这些信号贡献图和传统 DWI 扫描上测量骨病变与正常骨骼的信号强度比 (SIR)，并使用配对测试 (α = .05) 进行比较。使用两个样本测试（α = .05）来比较病变和正常骨骼之间的隔室信号分数。结果从四隔室模型中观察到最低的 BIC，最佳隔室扩散系数为 0、1.1 × 10−3、2.8 × 10-3 和 >3.0 ×10-2mm2/sec。该模型的拟合残差显着低于传统的表观扩散系数映射 (P< .001)。模型室 1 和 2 的信号贡献图上的骨病变 SIR 显着高于传统 DWI 扫描 (P< .008)。来自隔室 2、3 和 4 的信号分数在转移性骨病灶和正常骨组织之间也存在显着差异 (P≤ .02)。 结论 四隔室模型最好地描述了全身扩散特性。该模型的房室信号贡献可用于检查前列腺癌骨受累情况。关键词：全身 MRI、扩散加权成像、限制谱成像、扩散信号模型、骨转移、前列腺癌临床试验注册号NCT03440554© RSNA，2023 另请参阅本期 Margolis 的评论。
PurposeTo develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo.Materials and MethodsThis prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using pairedttests (α = .05). Two-samplettests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone.ResultsLowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10−3, 2.8 × 10-3, and >3.0 ×10-2mm2/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P< .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P< .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P≤ .02).ConclusionThe four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement.Keywords:Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate CancerClinical trial registration no. NCT03440554© RSNA, 2023See also commentary by Margolis in this issue.
影响因子：
1.3
作者：
Karunamuni, Roshan A.;Kuperman, Joshua;White, Nathan S.
通讯作者：
White, Nathan S.
对莱斯信号噪声对扩散张量成像估计的 b 相关影响进行几何分析，并确定最佳 b 值。
影响因子：
2.5
作者：
Taylor,PaulA;Biswal,Bharat
通讯作者：
Biswal,Bharat
影响因子：
3.3
作者：
Vidic, Igor;Egnell, Liv;Goa, Pal Erik
通讯作者：
Goa, Pal Erik