Dynamic Contrast-enhanced Magnetic Resonance Imaging Datasets Research Articles

A Physiological-Informed Generative Model for Improving Breast Lesion Classification in Small DCE-MRI Datasets.

In biomedical image processing, Deep Learning (DL) is increasingly exploited in various forms and for diverse purposes. Despite unprecedented results, the huge number of parameters to learn, which necessitates a substantial number of annotated samples, remains a significant challenge. In medical domains, obtaining high-quality labelled datasets is still a challenging task. In recent years, several works have leveraged data augmentation to face this issue, mostly thanks to the introduction of generative models able to produce artificial samples having the same characteristics as the acquired ones. However, we claim that biological principles must be considered in this process, as all medical imaging techniques exploit one or more physical laws or properties directly associated with the physiological characteristics of the tissues under analysis. A notable example is the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), in which the kinetic of the contrast agent (CA) highlights both morphological and physiological aspects. In this paper, we introduce a novel generative approach explicitly relying on Physiologically Based Pharmacokinetic (PBPK) modelling and on an Intrinsic Deforming Autoencoder (DAE) to implement a physiologically-aware data augmentation strategy. As a case of study, we consider breast DCE-MRI. In particular, we tested our proposal on two private and one public datasets with different acquisition protocols, demonstrating that the proposed method significantly improves the performance of several DL-based lesion classifiers.

Automatic semantic segmentation of breast cancer in DCE-MRI using DeepLabV3+ with modified ResNet50

Research on breast cancer segmentation is essential due to its high prevalence as the most common cancer in women and its occurrence in men as well. Breast cancer involves abnormal cell growth in the breast, highlighting the importance of advanced imaging. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is an effective technique for this purpose. Deep learning has significantly influenced medical imaging in recent years, especially in accurately segmenting tumors from MRI images. Two techniques have been proposed for breast tumor segmentation: Dilated ResNet50 (RN50D) and Parallel Layers Added ResNet50 (PLA-RN50). RN50D involves altering the dilation factor of the convolution layer within the residual block of ResNet50. PLA-RN50 entails the integration of parallel layers following the final residual block of the ResNet50 architecture. The modified architectures serve as the backbone for the DeepLabV3+ network. The DeepLabV3+ with RN50D or PLA-RN50D is a powerful and effective architecture that integrates deep feature extraction, multiscale spatial information, and precise segmentation to achieve high accuracy in lesion segmentation for breast DCE-MRI images. The proposed technique is tested on a QIN Breast DCE-MRI dataset comprising 233 images sourced from The Cancer Image Archive. The proposed method achieves a dice score of 0.92. The superior segmentation performance of DeepLabV3+ with PLA-RN50, as compared to its counterparts using ResNet18 and ResNet50, highlights the impactful modifications incorporated in PLA-RN50 for optimizing breast tumor segmentation.

MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a long scanning time, which causes patients' discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables the acquisition of DCE-MRI images without scanning. In order to reduce the time, multi-temporal feature fusion cooperative attention mechanism neural network (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables DCE-MRI image acquisition without scanning. In this paper, we propose multi temporal feature fusing neural network with Co-attention (MTFN) for DCE-MRI Synthesis, in which the Co-attention module can fully fuse the features of the first and third temporal image to obtain the hybrid features. The Co-attention explore long-range dependencies, not just relationships between pixels. Therefore, the hybrid features are more helpful to generate the eighth temporal images. We conduct experiments on the private breast DCE-MRI dataset from hospitals and the multi modal Brain Tumor Segmentation Challenge2018 dataset (BraTs2018). Compared with existing methods, the experimental results of our method show the improvement and our method can generate more realistic images. In the meanwhile, we also use synthetic images to classify the molecular typing of breast cancer that the accuracy on the original eighth time-series images and the generated images are 89.53% and 92.46%, which have been improved by about 3%, and the classification results verify the practicability of the synthetic images. The results of subjective evaluation and objective image quality evaluation indicators show the effectiveness of our method, which can obtain comprehensive and useful information. The improvement of classification accuracy proves that the images generated by our method are practical.

Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) can be used as a non-invasive method for the assessment of myocardial perfusion. The acquired images can be utilised to analyse the spatial extent and severity of myocardial ischaemia (regions with impaired microvascular blood flow). In the present paper, we propose a novel generalisable spatio-temporal hierarchical Bayesian model (GST-HBM) to automate the detection of ischaemic lesions and improve the in silico prediction accuracy by systematically integrating spatio-temporal context information. We present a computational inference procedure with an adequate trade-off between accuracy and computational efficiency, whereby model parameters are sampled from the posterior distribution with Gibbs sampling, while lower-level hyperparameters are selected using model selection strategies based on the Watanabe Akaike information criterion (WAIC). We have assessed our method on both synthetic (in silico) data with known gold-standard and 12 sets of clinical first-pass myocardial perfusion DCE-MRI datasets. We have also carried out a comparative performance evaluation with four established alternative methods: Gaussian mixture model (GMM), opening and closing operations based on Gaussian mixture model (GMMC&Omax), Markov random field constrained Gaussian mixture model (GMM-MRF) and model-based hierarchical Bayesian model (M-HBM). Our results show that the proposed GST-HBM method achieves much higher in silico prediction accuracy than the established alternative methods. Furthermore, this method appears to provide a more robust delineation of ischaemic lesions in datasets affected by spatially variant noise.

Hybrid feature fusion neural network integrating transformer for DCE-MRI super resolution

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is an essential medical imaging technique that provides multiple time-series images of the same anatomical structures and function without causing ionizing radiation. However, due to the physical nature of the DCE-MRI process, the scan time can be as long as tens of minutes, which severely affects the patient experience. Therefore, reducing scanning time has become a hot research topic, which super-resolution reconstruction from post-processing of the resulting low-resolution images to obtain high-resolution images. However, some existing methods exhibit general limitations in capturing long-range dependencies and loss of localized details. In this paper, we propose a Hybrid Feature Fusion Neural Network Integrating Transformer for DCE-MRI Super Resolution Reconstruction, we call it HybridF. HybridF combines Transformer and CNN, where both global dependency and low-level spatial details can be efficiently captured. Moreover, where a Hybrid Fusion Block (HFB) is proposed to effectively aggregate multi-level features from Transformer and CNN. The method is evaluated by the data collection of clinical DCE-MRI and public IXI dataset. The DCE-MRI dataset consists of 212 cases of patients images. The reconstruction quality of our network is higher than that of the SRCNN, EDSR and RCAN method (＋2.24 dB, ＋2.79, ＋1.09). It is demonstrated that our network outperforms state-of-the art approaches it will contribute to the development of DCE-MRI super-resolution reconstruction.

BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown high sensitivity to diagnose breast cancer. However, few computer-aided algorithms focus on employing DCE-MR images for breast cancer diagnosis due to the lack of publicly available DCE-MRI datasets. To address this issue, our work releases a new DCE-MRI dataset called BreastDM for breast tumor segmentation and classification. In particular, a dataset of 232 patients selected with DCE-MR images for benign and malignant cases is established. Each case consists of three types of sequences: pre-contrast, post-contrast, and subtraction sequences. To show the difficulty of breast DCE-MRI tumor image segmentation and classification tasks, benchmarks are achieved by state-of-the-art image segmentation and classification algorithms, including conventional hand-crafted based methods and recently-emerged deep learning-based methods. More importantly, a local–global cross attention fusion network (LG-CAFN) is proposed to further improve the performance of breast tumor images classification. Specifically, LG-CAFN achieved the highest accuracy (88.20%, 83.93%) and AUC value (0.9154,0.8826) in both groups of experiments. Extensive experiments are conducted to present strong baselines based on various typical image segmentation and classification algorithms. Experiment results also demonstrate the superiority of the proposed LG-CAFN to other breast tumor images classification methods. The related dataset and evaluation codes are publicly available at smallboy-code/Breast-cancer-dataset.

Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods.

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has taken on a significant and increasing role in diagnostic procedures and treatments for patients who suffer from chronic kidney disease. Careful segmentation of kidneys from DCE-MRI scans is an essential early step towards the evaluation of kidney function. Recently, deep convolutional neural networks have increased in popularity in medical image segmentation. To this end, in this paper, we propose a new and fully automated two-phase approach that integrates convolutional neural networks and level set methods to delimit kidneys in DCE-MRI scans. We first develop two convolutional neural networks that rely on the U-Net structure (UNT) to predict a kidney probability map for DCE-MRI scans. Then, to leverage the segmentation performance, the pixel-wise kidney probability map predicted from the deep model is exploited with the shape prior information in a level set method to guide the contour evolution towards the target kidney. Real DCE-MRI datasets of 45 subjects are used for training, validating, and testing the proposed approach. The valuation results demonstrate the high performance of the two-phase approach, achieving a Dice similarity coefficient of 0.95 ± 0.02 and intersection over union of 0.91 ± 0.03, and 1.54 ± 1.6 considering a 95% Hausdorff distance. Our intensive experiments confirm the potential and effectiveness of that approach over both UNT models and numerous recent level set-based methods.

Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201.

In order to achieve better performance, artificial intelligence is used in breast cancer diagnosis. In this study, we evaluated the efficacy of different fine-tuning strategies of deep transfer learning (DTL) based on the DenseNet201 model to differentiate malignant from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We collected 4260 images of benign lesions and 4140 images of malignant lesions of the breast pertaining to pathologically confirmed cases. The benign and malignant groups was randomly divided into a training set and a testing set at a ratio of 9:1. A DTL model based on the DenseNet201 model was established, and the effectiveness of 4 fine-tuning strategies (S0: strategy 0, S1: strategy; S2: strategy; and S3: strategy) was compared. Additionally, DCE-MRI images of 48 breast lesions were selected to verify the robustness of the model. Ten images were obtained for each lesion. The classification was considered correct if more than 5 images were correctly classified. The metrics for model performance evaluation included accuracy (Ac) in the training and testing sets, precision (Pr), recall rate (Rc), f1 score (f1), and area under the receiver operating characteristic curve (AUROC) in the validation set. The Ac of the 4 fine-tuning strategies reached 100.00% in the training set. The S2 strategy exhibited good convergence in the testing set. The Ac of S2 was 98.01% in the testing set, which was higher than those of S0 (93.10%), S1 (90.45%), and S3 (93.90%). The average classification Pr, Rc, f1, and AUROC of S2 in the validation set were (89.00%, 80.00%, 0.81, and 0.79, respectively) higher than those of S0 (76.00%, 67.00%, 0.69, and 0.65, respectively), S1 (60.00%, 60.00%, 0.60, 0.66, and respectively), and S3 (77.00%, 73.00%, 0.74, 0.72, respectively). The degree of coincidence between S2 and the histopathological method for differentiating between benign and malignant breast lesions was high (κ = 0.749). The S2 strategy can improve the robustness of the DenseNet201 model in relatively small breast DCE-MRI datasets, and this is a reliable method to increase the Ac of discriminating benign from malignant breast lesions on DCE-MRI.

A hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme for breast cancer segmentation based on DCE-MRI.

Automatically and accurately annotating tumor in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which provides a noninvasive in vivo method to evaluate tumor vasculature architectures based on contrast accumulation and washout, is a crucial step in computer-aided breast cancer diagnosis and treatment. However, it remains challenging due to the varying sizes, shapes, appearances and densities of tumors caused by the high heterogeneity of breast cancer, and the high dimensionality and ill-posed artifacts of DCE-MRI. In this paper, we propose a hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme that integrates pharmacokinetics prior and feature refinement to generate sufficiently adequate features in DCE-MRI for breast cancer segmentation. The pharmacokinetics prior expressed by time intensity curve (TIC) is incorporated into the scheme through objective function called dynamic contrast-enhanced prior (DCP) loss. It contains contrast agent kinetic heterogeneity prior knowledge, which is important to optimize our model parameters. Besides, we design a spatial fusion module (SFM) embedded in the scheme to exploit intra-slices spatial structural correlations, and deploy a spatial-kinetic fusion module (SKFM) to effectively leverage the complementary information extracted from spatial-kinetic space. Furthermore, considering that low spatial resolution often leads to poor image quality in DCE-MRI, we integrate a reconstruction autoencoder into the scheme to refine feature maps in an unsupervised manner. We conduct extensive experiments to validate the proposed method and show that our approach can outperform recent state-of-the-art segmentation methods on breast cancer DCE-MRI dataset. Moreover, to explore the generalization for other segmentation tasks on dynamic imaging, we also extend the proposed method to brain segmentation in DSC-MRI sequence. Our source code will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/DCEDuDoFNet.

Three-dimensional breast tumor segmentation on DCE-MRI with a multilabel attention-guided joint-phase-learning network.

Accurate breast and tumor segmentations from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is vital in breast disease diagnosis. Here, we propose a novel attention-guided joint-phase-learning network for multilabel segmentation including the breast and tumors simultaneously and automatically. Instead of common multichannel inputs, our novel network consists of five separated streams designed for extracting comprehensive features for each DCE-MRI phase to fully use the dynamic intensity of enhanced images. A new time-signal intensity map was designed based on the DCE-MRI pixel-by-pixel values and added as an additional stream to reflect breast tumor dynamic variations. The multiple streams were fused in a fully connected layer to integrate the comprehensive tumor information. Weighted-loss was applied to the multilabel strategy to highlight breast tumor segmentation. In addition, the net applies the self-attention module with grid-based attention coefficients based on a global feature vector to emphasize breast regions and suppress irrelevant non-breast tissue features. We trained our method on 144 DCE-MRI datasets acquired from Philips and achieved mean Dice coefficients of 0.92 and 0.86 for breast and tumor segmentations that were superior to common networks with multichannel structures. The model was extended to an independent test set with 59 cases from two different MRI machines and achieved a Dice coefficient of 0.83 for breast tumor segmentation, which illustrates the robustness of our framework. The automatically generated masks can improve the accuracy and time of diagnosis of malignant and benign breast tumors. Qualitative comparisons illustrate that the proposed method has high precision and generalizability.

A Clifford Analytic Signal-Based Breast Lesion Segmentation Method for 4D Spatial-Temporal DCE-MRI Sequences

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used for lesion detection in breast cancer diagnosis for its capability to provide spatial-temporal information. However, the massive and complex 4D spatial-temporal DCE-MRI data make the diagnosis process lengthy and error-prone. Moreover, normal fibroglandular tissue is occasionally enhanced through background parenchymal enhancement (BPE), which can degrade the performance of current algorithms. We propose a new method using a 3D Clifford analytic signal (CAS) approach for breast lesion segmentation of DCE-MRI data. A 2D temporal image is constructed from all the 2D DCE-MRI slices at different scanning time points on a given transverse plane, according to the CAS approach. Then, a 3D Clifford temporal image (CTI) is constructed by successively stacking temporal images. The proposed CTI can distinguish lesion regions both visually and quantitatively compared to the traditional DCE-MRI subtraction image. Finally, we employ a fully convolutional network (FCN) model for breast lesion segmentation using the CTI as one of the inputs. Experimental results on an independent public dataset (TCIA QIN breast DCE-MRI) and a private household breast DCE-MRI dataset (TBD) show that the proposed method can achieve superior performance over current methods, both qualitatively and quantitatively.

Clinical and Quantitative MRI Perfusion Analysis of Osteoid Osteomas Before and After Microwave Ablation.

Evaluation of osteoid osteoma (OO) perfusion characteristics pre and post microwave ablation (MWA) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with clinical follow-up. Patients with OO underwent T1-weighted 3D DCE-MRI at 3.0T pre and post MWA. Signal intensity (SI) and perfusion of lesions was measured. Patients answered a questionnaire for clinical follow-up. A total of 24 patients were included (mean age of 28 years; nine females); 21 DCE-MRI datasets were of sufficient quality for analysis. The mean SI of lesions, plasma flow, volume of distribution and pain level all decreased statistically significantly after ablation. Quantitative MRI-perfusion analysis allows for objective treatment evaluation after MWA of OO, plasma flow and volume of distribution seem to be reliable indicators for successful thermoablation. MWA is an effective treatment option for OO with a low risk profile and significant pain reduction after treatment.

Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.

Volume fractions of DCE-MRI parameter as early predictor of histologic response in soft tissue sarcoma: A feasibility study

ObjectiveTo find early predictors of histologic response in soft tissue sarcoma through volume transfer constant (Ktrans) analysis based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and methods11 Patients with soft tissue sarcoma of the lower extremity that underwent preoperative chemoradiotherapy followed by limb salvage surgery were included in this retrospective study. For each patient, DCE-MRI data sets were collected before and two weeks after therapy initiation, and histologic tumor cell necrosis rate (TCNR) was reported at surgery. The DCE-MRI volumes were aligned by registration. Then, the aligned volumes were used to obtain the Ktrans variation map. Accordingly, three sub-volumes (with increased, decreased or unchanged Ktrans) were defined and identified, and fractions of the sub-volumes, denoted as F+, F- and F0, respectively, were calculated. The predictive ability of volume fractions was determined by using area under a receiver operating characteristic curve (AUC). Linear regression analysis was performed to investigate the relationship between TCNR and volume fractions. In addition, the Ktrans values of the sub-volumes were compared. ResultsThe AUC for F- (0.896) and F0 (0.833) were larger than that for change of tumor longest diameter ΔD (0.625) and the change of mean KtransΔKtrans¯ (0.792). Moreover, the regression results indicated that TCNR was directly proportional to F0 (R2=0.75, P=0.0003), while it was inversely proportional to F- (R2=0.77, P=0.0002). However, TCNR had relatively weak linear relationship with ΔKtrans¯ (R2=0.64, P=0.0018). Additionally, TCNR did not have linear relationship with DD (R2=0.16, P=0.1246). ConclusionThe volume fraction F- and F0 have potential as early predictors of soft tissue sarcoma histologic response.

Non-Parametric Bayesian Registration (NParBR) of Body Tumors in DCE-MRI Data.

The identification of tumors in the internal organs of chest, abdomen, and pelvis anatomic regions can be performed with the analysis of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) data. The contrast agent is accumulated differently by pathologic and healthy tissues and that results in a temporally varying contrast in an image series. The internal organs are also subject to potentially extensive movements mainly due to breathing, heart beat, and peristalsis. This contributes to making the analysis of DCE-MRI datasets challenging as well as time consuming. To address this problem we propose a novel pairwise non-rigid registration method with a Non-Parametric Bayesian Registration (NParBR) formulation. The NParBR method uses a Bayesian formulation that assumes a model for the effect of the distortion on the joint intensity statistics, a non-parametric prior for the restored statistics, and also applies a spatial regularization for the estimated registration with Gaussian filtering. A minimally biased intra-dataset atlas is computed for each dataset and used as reference for the registration of the time series. The time series registration method has been tested with 20 datasets of liver, lungs, intestines, and prostate. It has been compared to the B-Splines and to the SyN methods with results that demonstrate that the proposed method improves both accuracy and efficiency.

Semi-automatic lung segmentation of DCE-MRI data sets of 2-year old children after congenital diaphragmatic hernia repair: Initial results

In congenital diaphragmatic hernia (CDH), lung hypoplasia and secondary pulmonary hypertension are the major causes of death and severe disability. Based on new therapeutic strategies survival rates could be improved to up to 80%. However, after surgical repair of CDH, long-term follow-up of these pediatric patients is necessary. In this, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides insights into the pulmonary microcirculation and might become a tool within the routine follow-up program of CDH patients. However, whole lung segmentation from DCE-MRI scans is tedious and automated procedures are warranted. Therefore, in this study, an approach to semi-automated lung segmentation is presented. Segmentation of the lung is obtained by calculating the cross correlation and the area under curve between all voxels in the data set and a reference region-of-interest (ROI), here the arterial input function (AIF). By applying an upper and lower threshold to the obtained maps and intersecting these, a final segmentation is reached. This approach was tested on twelve DCE-MRI data sets of 2-year old children after CDH repair. Segmentation accuracy was evaluated by comparing obtained automatic segmentations to manual delineations using the Dice overlap measure. Optimal thresholds for the cross correlation were 0.5/0.95 and 0.1/0.5 for the area under curve, respectively. The ipsilateral (left) lung showed reduced segmentation accuracy compared to the contralateral (right) lung. Average processing time was about 1.4s per data set. Average Dice score was 0.7±0.1 for the whole lung. In conclusion, initial results are promising. By our approach, whole lung segmentation is possible and a rapid evaluation of whole lung perfusion becomes possible. This might allow for a more detailed analysis of lung hypoplasia of children after CDH.

Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis.

To evaluate heterogeneity within tumor subregions or "habitats" via textural kinetic analysis on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the classification of two clinical prognostic features; 1) estrogen receptor (ER)-positive from ER-negative tumors, and 2) tumors with four or more viable lymph node metastases after neoadjuvant chemotherapy from tumors without nodal metastases. Two separate volumetric DCE-MRI datasets were obtained at 1.5T, comprised of bilateral axial dynamic 3D T1 -weighted fat suppressed gradient recalled echo-pulse sequences obtained before and after gadolinium-based contrast administration. Representative image slices of breast tumors from 38 and 34 patients were used for ER status and lymph node classification, respectively. Four tumor habitats were defined based on their kinetic contrast enhancement characteristics. The heterogeneity within each habitat was quantified using textural kinetic features, which were evaluated using two feature selectors and three classifiers. Textural kinetic features from the habitat with rapid delayed washout yielded classification accuracies of 84.44% (area under the curve [AUC] 0.83) for ER and 88.89% (AUC 0.88) for lymph node status. The texture feature, information measure of correlation, most often chosen in cross-validations, measures heterogeneity and provides accuracy approximately the same as with the best feature set. Heterogeneity within habitats with rapid washout is highly predictive of molecular tumor characteristics and clinical behavior.

Dynamic Contrast-Enhanced MRI of Mouse Liver: A Feasibility Study Using a Dual-Input Two-Compartment Tracer Kinetic Model

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been widely applied to evaluate microcirculatory parameters in clinical settings. However, pre-clinical studies involving DCE-MRI of small animals remain challenging with the requirement for high spatial and temporal resolution for quantitative tracer kinetic analysis. This study illustrates the feasibility of applying a high temporal resolution (2 s) protocol for liver imaging in mice by analyzing the DCE-MRI datasets of mice liver with a dual-input two-compartment tracer kinetic model. Phantom studies were performed to validate the T1 estimates derived by the proposed protocol before applying it in mice studies. The DCE-MRI datasets of mice liver were amendable to tracer kinetic analysis using a dual-input two-compartment model. Estimated micro-circulatory parameters were consistent with liver physiology, indicating viability of applying the technique for pre-clinical drug developments.

Evaluation of Neuroendocrine Liver Metastases

The objective of this study was to evaluate the correlation between dynamic gadoxetic acid-enhanced magnetic resonance imaging parameters and specific uptake values (SUVs) derived from ¹⁸fluorodeoxyglucose (¹⁸F-FDG) and ⁶⁸Ga-DOTA-Tyr(3)-octreotate (⁶⁸Ga-DOTATATE) positron emission tomography/computed tomography (PET/CT) in patients with liver metastases of neuroendocrine neoplasms. A total of 42 patients with hepatic metastases of neuroendocrine neoplasms were prospectively enrolled and underwent both dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and PET/CT, using either ¹⁸F-FDG or ⁶⁸Ga-DOTATATE as tracer. The DCE-MRI was performed at 3 T with gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid acquiring 48 slices every 2.2 seconds for 5 minutes. Three regions of interest (ROIs) representing the liver background and up to 3 ROIs representing metastatic liver tissue were coregistered in the PET/CT and in the DCE-MRI data sets. For each patient, a dedicated dual-inlet, 2-compartment uptake model was fitted to the enhancement curves of DCE-MRI ROIs and perfusion parameters were calculated. Lesion-to-background ratios of SUVs were correlated with corresponding lesion-to-background ratios of the perfusion parameters arterial plasma flow, venous plasma flow, total plasma flow, extracellular mean transit time, extracellular volume, arterial flow fraction, intracellular uptake rate, and hepatic uptake fraction using the Spearman coefficient. Whereas the lesion-to-background ratios of arterial plasma flow and arterial flow fraction of liver metastases correlated negatively with the lesion-to-background ratios of SUV(mean) derived from ⁶⁸Ga-DOTATATE PET/CT (r = -0.54, P < 0.001; r = -0.39, P < 0.001, respectively), they correlated positively with the lesion-to-background ratios of SUV(mean) derived from ¹⁸F-FDG-PET/CT (r = 0.51, P < 0.05; r = 0.68, P < 0.01, respectively). The lesion-to-background ratios of the DCE-MRI parameters extracellular mean transit time and extracellular volume correlated very weakly with the lesion-to-background ratios of SUV(mean) from ⁶⁸Ga-DOTATATE PET/CT, whereas venous plasma flow, total plasma flow, hepatic uptake fraction, and intracellular uptake rate showed no correlation between DCE-MRI and PET/CT. Both ⁶⁸Ga-DOTATATE and ¹⁸fluorodeoxyglucose PET/CT partially correlate with MRI perfusion parameters from the dual-inlet, 2-compartment uptake model. The results indicate that the paired imaging methods deliver complementary functional information.

Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering

To automatically and robustly detect the arterial input function (AIF) with high detection accuracy and low computational cost in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, we developed an automatic AIF detection method using an accelerated version (Fast-AP) of affinity propagation (AP) clustering. The validity of this Fast-AP-based method was proved on two DCE-MRI datasets, i.e., rat kidney and human head and neck. The detailed AIF detection performance of this proposed method was assessed in comparison with other clustering-based methods, namely original AP and K-means, as well as the manual AIF detection method. Both the automatic AP- and Fast-AP-based methods achieved satisfactory AIF detection accuracy, but the computational cost of Fast-AP could be reduced by 64.37-92.10% on rat dataset and 73.18-90.18% on human dataset compared with the cost of AP. The K-means yielded the lowest computational cost, but resulted in the lowest AIF detection accuracy. The experimental results demonstrated that both the AP- and Fast-AP-based methods were insensitive to the initialization of cluster centers, and had superior robustness compared with K-means method. The Fast-AP-based method enables automatic AIF detection with high accuracy and efficiency.