The uncertainty (validity and reliability) of three electrothermometers in therapeutic modality research

Context: Data from electrothermometers are used to determine therapeutic modality usage, but the value of experimental results is only as good as the data collected.Objective: To determine the reliability and validity of 3 electrothermometers from 2 manufacturers.Design: A 3 × 4 × 17 factorial with repeated measures on 2 factors. Independent variables were trial (1, 2, 3), thermometer (mercury thermometer, Iso-Thermex calibrated from −50°C to 50°C, Iso-Thermex calibrated from −20°C to 80°C, and Datalogger), and time (17).Setting: Human Performance Research Center.Intervention(s): Eighteen thermocouples were inserted through the wall of a foamed polystyrene cooler, and 6 were connected to each of the 3 electrothermometers. The cooler was positioned on a stir plate and filled with room-temperature water (18.4°C). A mercury thermometer was immersed into the water bath. Measurements of the water bath were taken every 10 seconds for three 3-minute trials.Main Outcome Measure(s): The temperature variability of 3 electrothermometers was taken from a calibrated mercury thermometer.Results: The Iso-Thermex electrothermometers did not differ statistically from each other in uncertainty (validity error ± reliability error = 0.06°C ± 0.03°C ± 0.03°C ± 0.02°C, P < .05), but both differed from the Datalogger (0.64°C ± 0.20°C, P < .05). The Datalogger temperature was consistently higher than the mercury thermometer temperature.Conclusions: The Iso-Thermex electrothermometers were more stable than the Datalogger, and values were within the published uncertainty (±0.1°C) when used with PT-6 thermocouples. The Datalogger we used had an uncertainty of measurement greater than that indicated in the user''s manual (∼±0.52°C). Uncertainty of ±0.84°C can significantly influence the interpretation of results when intramuscular temperature changes are usually less than 5°C.     

Assessment of diffusion parameters by intravoxel incoherent motion MRI in head and neck squamous cell carcinoma

The objectives of this study were to assess the diffusion parameters derived from intravoxel incoherent motion (IVIM) MRI in head and neck squamous cell carcinoma (HNSCC) and to investigate the agreement between different methods of tumor delineation and two numerical methods to extract the perfusion fraction f. Thirty‐seven untreated patients with histopathologically confirmed primary HNSCC were included retrospectively in the study. The entire volume of the primary tumor was outlined on diffusion‐weighted images using co‐registered morphological images as a guide to the tumor location. Apparent diffusion coefficient (ADC) and IVIM diffusion parameters were estimated considering the largest tumor section as well as the entire tumor volume. A bi‐exponential fit was implemented to extract f, D (pure diffusion coefficient) and D* (pseudo‐diffusion coefficient). A second simplified method, based on an asymptotic extrapolation, was used to determine f. The agreement between ADC and IVIM diffusion parameters derived from the delineation of single and multiple slices, and between the two f estimations, was assessed by Bland–Altman plots. The inter‐slice variability of ADC and IVIM diffusion parameters was evaluated. The Kruskal–Wallis test was used to investigate whether the tumor location had a statistically significant influence on the values of the parameters. Comparing the tumor delineation methods, a better accordance was found for ADC and D, with a mean percentage difference of less than 2%. Larger discrepancies were found for f and D*, with mean differences of 4.5% and 5.5%, respectively. When comparing the two f estimation methods, small mean differences were found (<3.5%), suggesting that the two methods may be considered as equivalent for the assessment of f in our patient population. The observed ADC and IVIM diffusion parameters were dependent on the anatomic site of the lesion, carcinoma of the nasopharynx showing more homogeneous and dissimilar estimations than other HNSCCs. Copyright © 2013 John Wiley & Sons, Ltd.     

Validation of fast diffusion kurtosis MRI for imaging acute ischemia in a rodent model of stroke

Diffusion‐weighted imaging (DWI) captures ischemic tissue that is likely to infarct, and has become one of the most widely used acute stroke imaging techniques. Diffusion kurtosis imaging (DKI) has lately been postulated as a complementary MRI method to stratify the heterogeneously damaged DWI lesion. However, the conventional DKI acquisition time is relatively long, limiting its use in the acute stroke setting. Recently, a fast kurtosis mapping method has been demonstrated in fixed brains and control subjects. The fast DKI approach provides mean diffusion and kurtosis measurements under substantially reduced scan time, making it amenable to acute stroke imaging. Because it is not practical to obtain and compare different means of DKI to test whether the fast DKI method can reliably detect diffusion and kurtosis lesions in acute stroke patients, our study investigated its diagnostic value using an animal model of acute stroke, a critical step before fast DKI acquisition can be routinely applied in the acute stroke setting. We found significant correlation, per voxel, between the diffusion and kurtosis coefficients measured using the fast and conventional DKI protocols. In acute stroke rats, the two DKI methods yielded diffusion and kurtosis lesions that were in good agreement. Importantly, substantial kurtosis–diffusion lesion mismatch was observed using the conventional (26 ± 13%, P < 0.01) and fast DKI methods (23 ± 8%, P < 0.01). In addition, regression analysis showed that the kurtosis–diffusion lesion mismatches obtained using conventional and fast DKI methods were substantially correlated (R² = 0.57, P = 0.02). Our results confirmed that the recently proposed fast DKI method is capable of capturing heterogeneous diffusion and kurtosis lesions in acute ischemic stroke, and thus is suitable for translational applications in the acute stroke clinical setting. Copyright © 2014 John Wiley & Sons, Ltd.     

A multi‐Gaussian model for apparent diffusion coefficient histogram analysis of Wilms’ tumour subtype and response to chemotherapy

Wilms’ tumours (WTs) are large heterogeneous tumours, which typically consist of a mixture of histological cell types, together with regions of chemotherapy‐induced regressive change and necrosis. The predominant cell type in a WT is assessed histologically following nephrectomy, and used to assess the tumour subtype and potential risk. The purpose of this study was to develop a mathematical model to identify subregions within WTs with distinct cellular environments in vivo, determined using apparent diffusion coefficient (ADC) values from diffusion‐weighted imaging (DWI). We recorded the WT subtype from the histopathology of 32 tumours resected in patients who received DWI prior to surgery after pre‐operative chemotherapy had been administered. In 23 of these tumours, DWI data were also available prior to chemotherapy. Histograms of ADC values were analysed using a multi‐Gaussian model fitting procedure, which identified ‘subpopulations’ with distinct cellular environments within the tumour volume. The mean and lower quartile ADC values of the predominant viable tissue subpopulation (ADC_1MEAN, ADC_1LQ), together with the same parameters from the entire tumour volume (ADC_0MEAN, ADC_0LQ), were tested as predictors of WT subtype. ADC_1LQ from the multi‐Gaussian model was the most effective parameter for the stratification of WT subtype, with significantly lower values observed in high‐risk blastemal‐type WTs compared with intermediate‐risk stromal, regressive and mixed‐type WTs (p < 0.05). No significant difference in ADC_1LQ was found between blastemal‐type and intermediate‐risk epithelial‐type WTs. The predominant viable tissue subpopulation in every stromal‐type WT underwent a positive shift in ADC_1MEAN after chemotherapy. Our results suggest that our multi‐Gaussian model is a useful tool for differentiating distinct cellular regions within WTs, which helps to identify the predominant histological cell type in the tumour in vivo. This shows potential for improving the risk‐based stratification of patients at an early stage, and for guiding biopsies to target the most malignant part of the tumour. Copyright © 2015 John Wiley & Sons, Ltd.     

Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy

Measurements of tumor apparent diffusion coefficient (ADC), volume and diameter in assessing the response of patients with locally advanced breast cancer (LABC) (n = 56) undergoing neoadjuvant chemotherapy (NACT) at four time periods (before treatment and after three cycles of NACT) were carried out at 1.5 T using diffusion-weighted imaging (DWI) and MRI. Ten benign tumors and 15 controls were also investigated. The MR tumor response was compared with the clinical response. Mean ADC before treatment of malignant breast tissue was significantly lower than that of controls, disease-free contralateral tissue of the patients, and benign lesions, and gradually increased during the course of NACT. Analysis of the percentage change in ADC, volume and diameter after each cycle of NACT between clinical responders and non-responders showed that the change in ADC after the first cycle was statistically significant compared with volume and diameter, indicating its potential in assessing early response. After the third cycle, the sensitivity for differentiating responders from non-responders was 89% for volume and diameter and 68% for ADC, and the respective specificities were 50%, 70% and 100%. A sensitivity of 84% (specificity of 60% with an accuracy of 76%) was achieved when all three variables were taken together to predict the response. A cut-off value of ADC was also calculated using receiver operator characteristics analysis to discriminate between normal, benign and malignant breast tissue. Similarly, a cut-off value for ADC, volume and diameter was obtained after the second and third cycles of NACT to predict tumor response. The results show that ADC is more useful for predicting early tumor response to NACT than morphological variables, suggesting its potential in effective treatment management.     

基于蒙特卡罗仿真的脑内部组织水分子自扩散模型研究

水分子自扩散特性的研究不仅对脑内部组织生理解剖结构信息的获取具有极其重要的意义,而且对于某些药物在脑内扩散传递过程的研究也有重要参考价值。从脑内部组织具有的生理结构特点出发,总结水分子自扩散模型,并利用蒙特卡罗仿真计算不同结构条件下的水分子自扩散系数。蒙特卡罗仿真计算结果与Latour扩散模型计算结果比较表明,随着扩散加权时间的延长,两个结果越来越接近,进一步验证了Latour模型是一个长扩散加权时间模型。     

Quantitative prediction of ischemic stroke tissue fate

Accurate prediction of ischemic tissue fate could aid clinical decision-making in the treatment of acute stroke. We investigated predictions of tissue fate for three (30-min, 60-min and permanent) stroke models in rats. Quantitative cerebral blood flow (CBF), apparent diffusion coefficient (ADC) and spin-spin relaxation time constant (T(2)) were acquired during the acute phase and at the end point followed by histological examination. Probability-of-infarct profiles based on ADC and CBF data were constructed using a training dataset. Probability-of-infarct maps were predicted using only acute stroke data from a separate experimental dataset, revealing the likelihood of future infarction. Performance measures of sensitivity and specificity showed accurate predictions. Sensitivities (mean +/- SD) for the 30-min, 60-min and permanent stroke were, respectively, 82 +/- 6%, 82 +/- 7%, and 86 +/- 4%, specificities were 83 +/- 5%, 86 +/- 5%, and 89 +/- 6%, and the areas under the receiver operating curve were 87 +/- 3%, 90 +/- 4%, and 93 +/- 3%. Importantly, to improve prediction accuracy, we took into account regional susceptibility to infarction. Spatial frequency-of-infarct maps were constructed and predictions were made by taking the weighted average of the probability-of-infarct map and spatial frequency-of-infarct map. The optimal weighting coefficient of spatial frequency-of-infarct was small (10%) for the permanent occlusion group but surprisingly large (40%) for the reperfusion groups, indicating that regional susceptibility of infarction was important for accurate prediction in reperfusion stroke. We concluded that the likelihood of cerebral infarction in rats can be accurately predicted and that accounting for regional susceptibility of infarct further improves prediction accuracy. Predictive models have the potential to provide a valuable quantitative framework for clinicians to consider different stroke treatment options. Copyright (c) 2008 John Wiley & Sons, Ltd.     

鼻咽癌颈部淋巴结转移MR多b值DWI成像研究分析

目的：研究在鼻咽癌(nasopharyngeal carcinoma,NPC)颈部淋巴结转移的评价中1.5T MR多b值扩散加权成像(diffusion weighted imaging,DWI)的应用价值。方法：对良性淋巴结增大病人15例及鼻咽癌病人37例进行常规MR及多b值DWI检查,对不同b值的DWI图像质量进行比较。对不同b值下良、恶性淋巴结ADC值的ROC曲线进行记录。结果：b=800 s/mm2时鼻咽部变形小,图像背景抑制充分,伪影少,周围软组织与病灶具有较好的对比度,小淋巴结显示清楚;鼻咽癌、良性淋巴结及颈部转移性淋巴结的ADC值随着b值增大均呈下降趋势,8种b值下转移性淋巴结与鼻咽癌原发灶向比较,ADC值差异无统计学差异(P>0.05)。而良性淋巴结与转移性淋巴结相比较,ADC值差异均有统计学差异(P<0.05);b=800 s/mm2时对良恶性淋巴结的鉴定效果最好,灵敏度为100%,特异度为83.2%。结论：1.5T MR扩散加权成像(DWI)技术能有效鉴别淋巴结性质,b值取800 s/mm2时,DWI图像具有较好的质量,且对良恶性淋巴结的鉴定诊断效果最好,可在临床鼻咽癌颈部淋巴结转移的诊断中推广应用。     

Multi‐parametric MRI characterization of healthy human thigh muscles at 3.0 T – relaxation,magnetization transfer,fat/water,and diffusion tensor imaging

Muscle diseases commonly have clinical presentations of inflammation, fat infiltration, fibrosis, and atrophy. However, the results of existing laboratory tests and clinical presentations are not well correlated. Advanced quantitative MRI techniques may allow the assessment of myo‐pathological changes in a sensitive and objective manner. To progress towards this goal, an array of quantitative MRI protocols was implemented for human thigh muscles; their reproducibility was assessed; and the statistical relationships among parameters were determined. These quantitative methods included fat/water imaging, multiple spin‐echo T₂ imaging (with and without fat signal suppression, FS), selective inversion recovery for T₁ and quantitative magnetization transfer (qMT) imaging (with and without FS), and diffusion tensor imaging. Data were acquired at 3.0 T from nine healthy subjects. To assess the repeatability of each method, the subjects were re‐imaged an average of 35 days later. Pre‐testing lifestyle restrictions were applied to standardize physiological conditions across scans. Strong between‐day intra‐class correlations were observed in all quantitative indices except for the macromolecular‐to‐free water pool size ratio (PSR) with FS, a metric derived from qMT data. Two‐way analysis of variance revealed no significant between‐day differences in the mean values for any parameter estimate. The repeatability was further assessed with Bland–Altman plots, and low repeatability coefficients were obtained for all parameters. Among‐muscle differences in the quantitative MRI indices and inter‐class correlations among the parameters were identified. There were inverse relationships between fractional anisotropy (FA) and the second eigenvalue, the third eigenvalue, and the standard deviation of the first eigenvector. The FA was positively related to the PSR, while the other diffusion indices were inversely related to the PSR. These findings support the use of these T₁, T₂, fat/water, and DTI protocols for characterizing skeletal muscle using MRI. Moreover, the data support the existence of a common biophysical mechanism, water content, as a source of variation in these parameters. Copyright © 2014 John Wiley & Sons, Ltd.     

Tractography of the optic radiation: a repeatability and reproducibility study

Our main objective was to evaluate the repeatability and reproducibility of optic radiation (OR) reconstruction from diffusion MRI (dMRI) data. 14 adults were scanned twice with the same 60‐direction dMRI sequence. Peaks in the diffusion profile were estimated with the single tensor (ST), Q‐ball (QSH) and persistent angular structure (PAS) methods. Segmentation of the OR was performed by two experimenters with probabilistic tractography based on a manually drawn region‐of‐interest (ROI) protocol typically employed for OR segmentation, with both standard and extended sets of ROIs. The repeatability and reproducibility were assessed by calculating the intra‐class correlation coefficient (ICC) of intra‐ and inter‐rater experiments, respectively. ICCs were calculated for commonly used dMRI metrics (FA, MD, AD, RD) and anatomical dimensions of the optic radiation (distance from Meyer's loop to the temporal pole, ML‐TP), as well as the Dice similarity coefficient (DSC) between the raters’ OR segmentation. Bland–Altman plots were also calculated to investigate bias and variability in the reproducibility measurements. The OR was successfully reconstructed in all subjects by both raters. The ICC was found to be in the good to excellent range for both repeatability and reproducibility of the dMRI metrics, DSC and ML‐TP distance. The Bland–Altman plots did not show any apparent systematic bias for any quantities. Overall, higher ICC values were found for the multi‐fiber methods, QSH and PAS, and for the standard set of ROIs. Considering the good to excellent repeatability and reproducibility of all the quantities investigated, these findings support the use of multi‐fiber OR reconstruction with a limited number of manually drawn ROIs in clinical applications utilizing either OR microstructure characterization or OR dimensions, as is the case in neurosurgical planning for temporal lobectomy. Copyright © 2015 John Wiley & Sons, Ltd.     

一种新的磁共振扩散成像实验参数控制方法

扩散敏感梯度磁度的方向及强度是磁共振扩散成像实验的重要参数，但这二个参数不能由用户通过设备自带的软件设定。本文介绍一种新的方法，通过修改MRI扫描机内部的数据文件，用户可以方便与精确地设定扩散加权成像DWI及扩散张景成像DTI的实验参数，而且可以为MRI扫描机增加新的功能。     

基于机器学习方法的前列腺癌DWI多参数分析及其应用

探究使用机器学习方法,提升对扩散加权成像(DWI)多参数图的前列腺癌(PCa)诊断的准确性。对39例前列腺癌患者、56例良性患者,进行磁共振扩散加权图像的采集,并使用传统单指数模型(Mono)、拉伸指数模型(SEM)、弥散张量成像(DTI)模型、弥散峰度成像(DKI)模型以及体内素不相干运动扩散(IVIM)模型等5种重建模型,得到共计16个参数图,而后对于每一个参数图进行直方图分析,得到相关图像特征后使用机器学习的方法进行分类。 使用支持向量机和随机森林两种分类器对前列腺病变进行良恶性分类,随机森林分类器的AUC值可以达到0.98,具有较高的分类性能。另外,对特征进行重要性排序后,发现DKI参数图是肿瘤分类的重要指标。     

Prediction of pathologic response to neoadjuvant chemotherapy in patients with breast cancer using diffusion‐weighted imaging and MRS

The aim of this study was to determine whether tumor size, MRS parameters and apparent diffusion coefficient (ADC) measurements could be applied to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Ninety patients with breast cancer (median size, 4.5 cm; range, 1.6–9.5 cm) were evaluated with single‐voxel ¹H MRS and dynamic contrast‐enhanced MRI. Diffusion‐weighted imaging was performed in 41 of these patients using a 1.5‐T scanner before and after completion of NAC. Pre‐ and post‐treatment measurements and changes in tumor size, MRS parameters [absolute and normalized total choline‐containing compound (tCho) integral and tCho signal‐to‐noise ratio (SNR)] and ADCs in pCR versus non‐pCR were compared using the nonparametric Mann–Whitney test. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance of each parameter. After NAC, 30 patients (33%) showed pCR and 60 (67%) showed non‐pCR. At pretreatment, ADC was the only significant parameter in differentiating between pCR and non‐pCR [(0.83 ± 0.05) × 10^–3 versus (0.97 ± 0.14) × 10^–3 mm²/s] (p = 0.014). Post‐treatment measurements after completion of NAC and changes in tumor size (both p < 0.001), MRS parameters (p = 0.027 and p = 0.020 for absolute tCho integral, p = 0.036 and p = 0.023 for normalized tCho integral, and p = 0.032 and p = 0.061 for tCho SNR) and ADC (p = 0.003 and p < 0.001) were significantly different between the pCR and non‐pCR groups, except for changes in tCho SNR. In ROC analysis, the areas under the ROC curve (AUCs) of 0.63–0.73 were obtained for tumor size and MRS parameters. AUCs for pre‐ and post‐treatment ADC and changes in ADC were 0.75, 0.80 and 0.96, respectively. The optimal cut‐off of the percentage change in ADC for predicting pCR was 40.7%, yielding 100% sensitivity and 91% specificity. Patients with pCR showed significantly lower pretreatment ADCs than those with non‐pCR. The change in ADC after NAC was the most accurate predictor of pCR. Copyright © 2012 John Wiley & Sons, Ltd.