MRI of tarantulas: morphological and perfusion imaging

This paper describes a study performed to evaluate the feasibility of using a 1.5-T whole-body magnetic resonance imaging (MRI) equipment, in combination with pharmacokinetic modeling, to obtain in vivo information about the morphology and perfusion of tarantulas (Eurypelma californicum). MRI was performed on three tarantulas using spin-echo sequences for morphological imaging and a rapid spoiled gradient-echo sequence for dynamic imaging during and after contrast medium (CM; Gd-DTPA) injection. Signal enhancement in dynamic measurements was evaluated with a pharmacokinetic two-compartment model. Spin-echo images showed morphological structures well. Dynamic images were of sufficient quality and allowed a model analysis of CM kinetics, which provides information about regional perfusion. In conclusion, morphological and dynamic contrast-enhanced MRI of tarantulas is feasible with a conventional clinical scanner. Studies of this kind are therefore possible without a dedicated high-field animal scanner.     

Region of interest and pixel-by-pixel analysis of dynamic contrast enhanced magnetic resonance imaging parameters and time-intensity curve shapes: a comparison in chondroid tumors

Dynamic contrast enhanced (DCE) MRI is widely acknowledged to be a helpful tool in the diagnosis and differentiation of tumors. In common clinical settings, the dynamic changes described by the time-intensity curves (TICs) are evaluated to find patterns of atypical tissue behavior, i.e., areas characterized by rapid contrast wash-in and wash-out. Despite the ease of this approach, there is no consensus about the specificity of the TIC shapes in discriminating tumor grades. We explore a new way of looking at TICs, where these are not averaged over a selected region of interest (ROI), but rendered pixel-by-pixel. In this way, the characteristic of the tissue is not given as a single TIC classification but as a distribution of the different TIC patterns. We applied this method in a group of patients with chondroid tumors and compared its outcome with the outcome of the standard ROI-based averaged TIC analysis. Furthermore, we focused on the problem of ROI selection in these tumors and how this affects the outcome of the TIC analysis. Finally, we investigated what relationship exists between the "standard" DCE-MRI parameter maximum enhancement (ME) and the TIC shape. CONCLUSIONS: We demonstrate that, where the ROI approach fails to show the presence of areas of rapid contrast wash-in and wash-out, the pixel-by-pixel approach reveals the coexistence of a heterogeneous pattern of TIC shapes. Secondly, we point out the differences in the DCE MRI parameters and tumor volume that can result when selecting the tumor based on DCE parameter maps or post-contrast T1-weighted images. Finally, we show that ME maps and TIC shape maps highlight different tissue areas and, therefore, the use of the ME maps is not appropriate for the correct identification of areas of atypical TICs.     

基于活动轮廓模型和影像组学的乳腺癌LVI状态预测

针对乳腺癌患者术前LVI状态预测问题，提出了活动轮廓模型和影像组学相结合的计算机辅助分析方法.首先，提出一种基于后验概率和模糊速度函数的活动轮廓模型方法来完成乳腺癌DCE-MRI图像分割.通过在小波域下构建基于后验概率的活动轮廓模型的区域项，同时利用模糊速度函数构建活动轮廓模型的边界项，可以提高乳腺癌病灶分割的准确性.其次，提取形态、灰度、纹理等图像特征，利用集成分类器随机森林方法构造LVI状态的预测模型.实验结果表明，所构建的模型对乳腺癌患者LVI状态具有较好的预测能力.     

A variational approach to image registration in dynamic contrast-enhanced MRI of the human kidney

Kidney function can be accessed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measurements which yield spatially resolved maps of physiological parameters like perfusion or filtration. The motion of the kidneys during the scan is a dominant limitation of the measurement quality, and image registration is necessary for accurate quantification. We analyzed the feasibility of applying an algorithm, originally developed for multimodal registration, to kidney perfusion time series. The algorithm uses a variational calculation scheme to align the images. In four out of five data sets, kidney motion could be reduced to below the spatial resolution of the images of 1.6 mm while preserving the enhancement pattern of kidney perfusion. Fitting a pharmacokinetic model to the data showed an average reduction of the Akaike fit error of 10% for the registered data, suggesting more stable parameters. We conclude that this image registration algorithm is feasible for correcting kidney motion in renal DCE-MRI.     

Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor. Modified fuzzy c-means clustering was used to identify the most representative kinetic curve of the whole segmented tumor, which was then characterized by using conventional curve analysis or pharmacokinetic model. The 3-D morphological features including shape features (compactness, margin, and ellipsoid fitting) and texture features (based on the grey level co-occurrence matrix) of the segmented tumor were obtained to characterize the lesion. One hundred and thirty-two biopsy-proven lesions (63 benign and 69 malignant) were used to evaluate the performance of the proposed computer-aided system for breast MRI. Five combined features including rate constant (k_ep), volume of plasma (v_p), energy (G₁), entropy (G₂), and compactness (C₁), had the best performance with an accuracy of 91.67% (121/132), sensitivity of 91.30% (63/69), specificity of 92.06% (58/63), and A_z value of 0.9427. Combining the kinetic and morphological features of 3-D breast MRI is a potentially useful and robust algorithm when attempting to differentiate benign and malignant lesions.     

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: Preliminary results

Purpose

To evaluate whether semi-quantitative analysis of high temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) acquired early in treatment can predict the response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy (NAC).

Materials and Methods

As part of an IRB-approved prospective study, 21 patients with LABC provided informed consent and underwent high temporal resolution 3 T DCE-MRI before and after 1 cycle of NAC. Using measurements performed by two radiologists, the following parameters were extracted for lesions at both examinations: lesion size (short and long axes, in both early and late phases of enhancement), radiologist's subjective assessment of lesion enhancement, and percentages of voxels within the lesion demonstrating progressive, plateau, or washout kinetics. The latter data were calculated using two filters, one selecting for voxels enhancing ≥ 50% over baseline and one for voxels enhancing ≥ 100% over baseline. Pretreatment imaging parameters and parameter changes following cycle 1 of NAC were evaluated for their ability to discriminate patients with an eventual pathological complete response (pCR).

Results

All 21 patients completed NAC followed by surgery, with 9 patients achieving a pCR. No pretreatment imaging parameters were predictive of pCR. However, change after cycle 1 of NAC in percentage of voxels demonstrating washout kinetics with a 100% enhancement filter discriminated patients with an eventual pCR with an area under the receiver operating characteristic curve (AUC) of 0.77. Changes in other parameters, including lesion size, did not predict pCR.

Conclusion

Semi-quantitative analysis of high temporal resolution DCE-MRI in patients with LABC can discriminate patients with an eventual pCR after one cycle of NAC.     

Assessment of extravascular extracellular space fraction in human melanoma xenografts by DCE-MRI and kinetic modeling

Tumor aggressiveness and response to therapy are influenced by the extravascular extracellular space fraction (EESF) of the malignant tissue. The EESF may, therefore, be an important prognostic parameter for cancer patients. The aim of this study was to investigate whether gadopentetate dimeglumine (Gd-DTPA)-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to assess the EESF of tumors. Amelanotic human melanoma xenografts (A-07, R-18) were used as preclinical models of human cancer. Images of E.F (E is the initial extraction fraction of Gd-DTPA and F is perfusion) and lambda (the partition coefficient of Gd-DTPA) were obtained by Kety analysis of DCE-MRI data. Our study was based on the hypothesis that lambda is governed by the EESF and is not influenced significantly by microvascular density (MVD) or blood perfusion. To test this hypothesis, we searched for correlations between lambda and E.F, MVD or EESF by comparing lambda images with E.F images, histological preparations from the imaged tissue and the radial heterogeneity in EESF obtained by invasive imaging. Positive correlations were found between lambda and EESF. Thus, median lambda was larger in A-07 tumors than in R-18 tumors by a factor of 4.2 (P<.00001), consistent with the histological observation that EESF is approximately fourfold larger in A-07 tumors than in R-18 tumors. The radial heterogeneity in lambda in A-07 and R-18 tumors was almost identical to the radial heterogeneity in EESF. Moreover, lambda was larger in tissue regions with high EESF than in tissue regions with low EESF in A-07 tumors (P=.048). On the other hand, significant correlations between lambda and MVD or E.F could not be detected. Consequently, Kety analysis of Gd-DTPA-based DCE-MRI series of xenografted tumors provides lambda images that primarily reflect the EESF of the tissue.     

基于双侧TIC定量特征的乳腺肿瘤良恶性鉴别

基于DCE-MRI提出了一种利用双侧乳腺对称区域的TIC定量特征识别乳腺肿瘤良恶性的方法.使用三维区域生长算法提取乳腺的病灶区,基于病灶区及其对侧乳腺对应的ROI的TIC曲线分别提取29个特征,并定义双侧差异特征参数,经SFFS方法筛选后得到7个有效特征.使用SVM进行特征训练,基于交叉验证方法得到分类结果.本研究随机选取回顾性病例112例(良性67例,恶性45例),得到肿瘤良恶性平均分类准确率为88.39%.实验结果表明:此方法对乳腺肿瘤的良恶性鉴别有较高的准确率,对辅助医生进行乳腺病变组织的良恶性鉴别具有重要价值.