Fractional Flow Reserve (FFR), the ratio of arterial pressure distal to a coronary lesion to the proximal pressure, is indicative of its hemodynamic significance. This quantity can be determined from invasive measurements made with a catheter, or by using computational methods incorporating models of the the coronary vasculature. One of the inputs needed by a model-based approach for estimating FFR from Computed Tomography Angiography (CTA) images (denoted FFR-CT) is the geometry of the coronary arteries, which requires segmentation of the coronary lumen. Several algorithms have been proposed for coronary lumen segmentation, including the recent application of machine learning techniques. For evaluating these algorithms or for training machine learning algorithms, manual segmentation of the lumen has been considered as ground truth. However, since there is inter-subject variability in manual segmentation, it would be useful to first assess the extent to which this variability affects the predicted FFR values. In the current study, we evaluated the impact of inter-subject variability in manual segmentation on computed FFR, using datasets with three different manual segmentations provided as part of the Rotterdam Coronary Artery Evaluation Framework. FFR was computed using a coronary blood flow model. Our results indicate that variability in manual segmentations on FFR estimates depend on the FFR value. For FFR ≥ 0.97, variability in manual segmentations does not impact FFR estimates, while, for lower FFR values, the variability in manual segmentations leads to significant variability in FFR. The results of this study indicate that researchers should exercise caution when treating manual segmentations as ground truth for estimating FFR from CTA images.
KEYWORDS: Magnetic resonance imaging, Breast, Diagnostics, Data modeling, Tumors, Tumor growth modeling, Temporal resolution, Biopsy, Breast cancer, Data acquisition
Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio (SERmean) was calculated for all lesions, and quantitative pharmacokinetic, parameters Ktrans, kep, and ve, were calculated for the subset with available T1 maps (n=35). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for Ktrans, kep, and SERmean, respectively (p<0.05). At equal 94% sensitivity, the specificity and positive predictive value of SERmean (53% and 63%, respectively) and Ktrans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining SERmean and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using SERmean. SERmean has potential to help reduce unnecessary biopsies resulting from routine breast imaging.
Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT (n=33), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.
KEYWORDS: Brain, Magnetic resonance imaging, In vivo imaging, Neuroimaging, Diffusion tensor imaging, Diffusion, Image resolution, Data acquisition, Tissues, Structural imaging
The common squirrel monkey, Saimiri sciureus, is a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. It is one of the most commonly used South American primates in biomedical research. Unlike its Old World macaque cousins, no digital atlases have described the organization of the squirrel monkey brain. Here, we present a multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. In vivo MRI acquisitions include high resolution T2 structural imaging and low resolution diffusion tensor imaging. Ex vivo MRI acquisitions include high resolution T2 structural imaging and high resolution diffusion tensor imaging. Cortical regions were manually annotated on the co-registered volumes based on published histological sections.
Unlike many other experimental imaging methods, elastography has enjoyed a strong link to the standard diagnostic and interventional evaluation technique of soft tissue palpation. As a result, the initial excitement about elastography quickly translated to clinical use (e.g., [1-3]) which now includes commercially available ultrasound and magnetic resonance (MR) elastography products. However, despite these advances, understanding what these macroscopic clinical-scale tissue measurements indicate with respect to the underlying cellular and tissue-matrix scale phenomena is largely unclear. In this work, we present preliminary data towards a more systematic study of the elasticity biomarker in characterizing cancer for therapeutic design and monitoring. In addition, we demonstrate that we can conduct these studies with techniques that are consistent across both pre-clinical (i.e., mouse) and clinical length scales. The elastography method we use is called modality independent elastography (MIE) [4, 5] and can be described as a highly translatable model-based inverse image-analysis method that reconstructs elasticity images using two acquired image volumes in a pre-post state of deformation. Quantitative phantom results using independent testing methods report an elastic property contrast between the inclusion and background as a 14.9 to 1 stiffness ratio with MIE reconstructing the ratio as 13.1 to 1. Preliminary elasticity reconstructions in murine and human systems are reported and are consistent with literature findings.
KEYWORDS: Tumors, Data modeling, Mathematical modeling, Diffusion, Magnetic resonance imaging, Breast, Tumor growth modeling, Mechanics, Image enhancement, In vivo imaging
There is currently a paucity of reliable techniques for predicting the response of breast tumors to neoadjuvant chemotherapy. The standard approach is to monitor gross changes in tumor size as measured by physical exam and/or conventional imaging, but these methods generally do not show whether a tumor is responding until the patient has completed therapy. One promising approach to address this clinical need is to integrate quantitative in vivo imaging data into biomathematical models of tumor growth in order to predict eventual response based on early measurements during therapy. Contrast enhanced and diffusion weighted magnetic resonance imaging data acquired before and after the first cycle of therapy to calibrate a patient-specific response model can be used to predict patient outcome at the conclusion of therapy. We have developed a mathematical modeling approach to optimize key model parameters for the calibration of a patient-specific mechanically coupled reaction-diffusion model of response. We apply the approach to patient data in which tumors were either responsive or non-responsive to neoajuvant chemotherapy and demonstrate changes to the patient-specific model which result in altered growth patterns. Additionally, we show that reconstructed parameter maps exhibit drastic differences between patients with different tumor burden outcomes at the conclusion of therapy, in this case, a 10-fold increase in proliferative capacity is found for a non-responding tumor versus its responsive counterpart. Finally, we show that the mechanically coupled reaction-diffusion growth model, when projected forward, more accurately predicts residual tumor burden than the uncoupled model.
KEYWORDS: Tumors, Image registration, Breast, Magnetic resonance imaging, Detection and tracking algorithms, Tissues, Image resolution, Computer simulations, Mammography, Breast cancer
Although useful for the detection of breast cancers, conventional imaging methods, including mammography and
ultrasonography, do not provide adequate information regarding response to therapy. Dynamic contrast enhanced MRI
(DCE-MRI) has emerged as a promising technique to provide relevant information on tumor status. Consequently,
accurate longitudinal registration of breast MR images is critical for the comparison of changes induced by treatment at
the voxel level. In this study, a nonrigid registration algorithm is proposed to allow for longitudinal registration of breast
MR images obtained throughout the course of treatment. We accomplish this by modifying the adaptive bases algorithm
(ABA) through adding a tumor volume preserving constraint in the cost function. The registration results demonstrate
the proposed algorithm can successfully register the longitudinal breast MR images and permit analysis of the parameter
maps. We also propose a novel validation method to evaluate the proposed registration algorithm quantitatively. These
validations also demonstrate that the proposed algorithm constrains tumor deformation well and performs better than the
unconstrained ABA algorithm.
3D intra- and inter-subject registration of image volumes is important for tasks that include measurements and
quantification of temporal/longitudinal changes, atlas-based segmentation, deriving population averages, or voxel and
tensor-based morphometry. A number of methods have been proposed to tackle this problem but few of them have
focused on the problem of registering whole body image volumes acquired either from humans or small animals. These
image volumes typically contain a large number of articulated structures, which makes registration more difficult than
the registration of head images, to which the vast majority of registration algorithms have been applied. To solve this
problem, we have previously proposed an approach, which initializes an intensity-based non-rigid registration algorithm
with a point based registration technique [1, 2]. In this paper, we introduce new constraints into our non-rigid registration
algorithm to prevent the bones from being deformed inaccurately. Results we have obtained show that the new
constrained algorithm leads to better registration results than the previous one.
The importance of small animal imaging in fundamental and clinical research is growing rapidly. These studies typically involve micro PET, micro MR, and micro CT images as well as optical or fluorescence images. Histological images are also often used to complement and/or validate the in vivo data. As is the case for human studies, automatic registration of these imaging modalities is a critical component of the overall analysis process, but, the small size of the animals and thus the limited spatial resolution of the in vivo images present specific challenges. In this paper, we propose a series of methods and techniques that permit the inter-subject registration of micro MR and histological images. We then compare results obtained by registering directly MR volumes to each other using a non-rigid registration algorithm we have developed at our institution with results obtained by registering first the MR volumes to their corresponding histological volume, which we reconstruct from 2D cross-sections, and then registering histological volumes to each other. We show that the second approach is preferable.
The development of digital microscopy and computational power is providing new opportunities for analyzing the motility of the vesicles (proteins) within living cells. In this paper, an automatic method is developed to segment and track vesicles in large amount of fluorescence images, in order to compute a number of quantitative parameters such as displacement, residence time, binding, or immobile fraction. We present a method that permits the automatic tracking of subcellular structures in long sequences of fluorescence images (up to 100 frames). The method we propose has been tested on 92 data sets totaling 8225 frames.
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