Identifying patterns from the neuroimaging recordings of brain activity related to the unobservable psychological or mental state of an individual can be treated as a unsupervised pattern recognition problem. The main challenges, however, for such an analysis of fMRI data are: a) defining a physiologically meaningful feature-space for representing the spatial patterns across time; b) dealing with the high-dimensionality of the data; and c) robustness to the various artifacts and confounds in the fMRI time-series. In this paper, we present a network-aware feature-space to represent the states of a general network, that enables comparing and clustering such states in a manner that is a) meaningful in terms of the network connectivity structure; b)computationally efficient; c) low-dimensional; and d) relatively robust to structured and random noise artifacts. This feature-space is obtained from a spherical relaxation of the transportation distance metric which measures the cost of trans- porting “mass” over the network to transform one function into another. Through theoretical and empirical assessments, we demonstrate the accuracy and efficiency of the approximation, especially for large problems.
This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down.
In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.
PURPOSE: To develop and evaluate image registration methodology for automated re-identification of tumor-suspicious foci from preprocedural MR exams during MR-guided transperineal prostate core biopsy. MATERIALS AND METHODS: A hierarchical approach for automated registration between planning and intra-procedural T2-weighted prostate MRI was developed and evaluated on the images acquired during 10 consecutive MR-guided biopsies. Registration accuracy was quantified at image-based landmarks and by evaluating spatial overlap for the manually segmented prostate and sub-structures. Registration reliability was evaluated by simulating initial mis-registration and analyzing the convergence behavior. Registration precision was characterized at the planned biopsy targets. RESULTS: The total computation time was compatible with a clinical setting, being at most 2 min. Deformable registration led to a significant improvement in spatial overlap of the prostate and peripheral zone contours compared with both rigid and affine registration. Average in-slice landmark registration error was 1.3 ± 0.5 mm. Experiments simulating initial mis-registration resulted in an estimated average capture range of 6 mm and an average in-slice registration precision of ±0.3 mm. CONCLUSION: Our registration approach requires minimum user interaction and is compatible with the time constraints of our interventional clinical workflow. The initial evaluation shows acceptable accuracy, reliability and consistency of the method.
James A Shackleford, Nadya Shusharina, Joost Verberg, Guy Warmerdam, Brian Winey, Markus Neuner, Philipp Steininger, Amelia Arbisser, and Polina Golland. 10/2012. “Plastimatch 1.6 - Current Capabilities and Future Directions.” Int Conf Med Image Comput Comput Assist Interv. Workshop on Image-Guidance and Multimodal Dose Planning in Radiation Therapy. 15 (WS).Abstract
Open-source software provides an economic benefit by reducing duplicated development effort, and advances science knowledge by fostering a culture of reproducible experimentation. This paper describes recent advances in the Plastimatch open software suite, which implements a broad set of useful tools for research and practice in radiotherapy and medical imaging. The focus of this paper is to highlight recent advancements, including 2D-3D registration, GPU-accelerated mutual information, analytic regularization of B-spline registration, automatic 3D feature detection and feature matching, and radiotherapy plan evaluation tools.
Transrectal ultrasound (TRUS) facilitates intra-treatment delineation of the prostate gland (PG) to guide insertion of brachytherapy seeds, but the prostate substructure and apex are not always visible which may make the seed placement sub-optimal. Based on an elastic model of the prostate created from MRI, where the prostate substructure and apex are clearly visible, we use a Bayesian approach to estimate the posterior distribution on deformations that aligns the pre-treatment MRI with intra-treatment TRUS. Without apex information in TRUS, the posterior prediction of the location of the prostate boundary, and the prostate apex boundary in particular, is mainly determined by the pseudo stiffness hyper-parameter of the prior distribution. We estimate the optimal value of the stiffness through likelihood maximization that is sensitive to the accuracy as well as the precision of the posterior prediction at the apex boundary. From a data-set of 10 pre- and intra-treatment prostate images with ground truth delineation of the total PG, 4 cases were used to establish an optimal stiffness hyper-parameter when 15% of the prostate delineation was removed to simulate lack of apex information in TRUS, while the remaining 6 cases were used to cross-validate the registration accuracy and uncertainty over the PG and in the apex.
This software framework brings a set of input volumes from pediatric brains into alignment. Therefore, the notion of pair-wise image registration is extended to group-wise alignment, which allows to find correspondence among a whole group of data sets instead of just two of them. Moreover, it simultaneously brings the set of input volumes into alignment, with every member of the population approaching the group's central tendency at the same time.
A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described. In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model of Anatomy ontology. The atlas was registered to the end systole template of a previous study of left ventricular remodeling in cardiomyopathy using a diffeomorphic registration algorithm. The previous study used thresholding and visual inspection to locate a region of statistical significance which distinguished patients with ischemic cardiomyopathy from those with nonischemic cardiomyopathy. Using semantic technologies and the deformed annotated atlas, this location was more precisely found. Although this study used only a cardiac atlas, it provides a proof-of-concept that ontologically labeled biomedical atlases of any anatomical structure can be used to automate location-based inferences.
Mild traumatic brain injury (mTBI), also referred to as concussion, remains a controversial diagnosis because the brain often appears quite normal on conventional computed tomography (CT) and magnetic resonance imaging (MRI) scans. Such conventional tools, however, do not adequately depict brain injury in mTBI because they are not sensitive to detecting diffuse axonal injuries (DAI), also described as traumatic axonal injuries (TAI), the major brain injuries in mTBI. Furthermore, for the 15 to 30 % of those diagnosed with mTBI on the basis of cognitive and clinical symptoms, i.e., the "miserable minority," the cognitive and physical symptoms do not resolve following the first 3 months post-injury. Instead, they persist, and in some cases lead to long-term disability. The explanation given for these chronic symptoms, i.e., postconcussive syndrome, particularly in cases where there is no discernible radiological evidence for brain injury, has led some to posit a psychogenic origin. Such attributions are made all the easier since both posttraumatic stress disorder (PTSD) and depression are frequently co-morbid with mTBI. The challenge is thus to use neuroimaging tools that are sensitive to DAI/TAI, such as diffusion tensor imaging (DTI), in order to detect brain injuries in mTBI. Of note here, recent advances in neuroimaging techniques, such as DTI, make it possible to characterize better extant brain abnormalities in mTBI. These advances may lead to the development of biomarkers of injury, as well as to staging of reorganization and reversal of white matter changes following injury, and to the ability to track and to characterize changes in brain injury over time. Such tools will likely be used in future research to evaluate treatment efficacy, given their enhanced sensitivity to alterations in the brain. In this article we review the incidence of mTBI and the importance of characterizing this patient population using objective radiological measures. Evidence is presented for detecting brain abnormalities in mTBI based on studies that use advanced neuroimaging techniques. Taken together, these findings suggest that more sensitive neuroimaging tools improve the detection of brain abnormalities (i.e., diagnosis) in mTBI. These tools will likely also provide important information relevant to outcome (prognosis), as well as play an important role in longitudinal studies that are needed to understand the dynamic nature of brain injury in mTBI. Additionally, summary tables of MRI and DTI findings are included. We believe that the enhanced sensitivity of newer and more advanced neuroimaging techniques for identifying areas of brain damage in mTBI will be important for documenting the biological basis of postconcussive symptoms, which are likely associated with subtle brain alterations, alterations that have heretofore gone undetected due to the lack of sensitivity of earlier neuroimaging techniques. Nonetheless, it is noteworthy to point out that detecting brain abnormalities in mTBI does not mean that other disorders of a more psychogenic origin are not co-morbid with mTBI and equally important to treat. They arguably are. The controversy of psychogenic versus physiogenic, however, is not productive because the psychogenic view does not carefully consider the limitations of conventional neuroimaging techniques in detecting subtle brain injuries in mTBI, and the physiogenic view does not carefully consider the fact that PTSD and depression, and other co-morbid conditions, may be present in those suffering from mTBI. Finally, we end with a discussion of future directions in research that will lead to the improved care of patients diagnosed with mTBI.
Extracting anatomical and functional significant structures renders one of the important tasks for both the theoretical study of the medical image analysis, and the clinical and practical community. In the past, much work has been dedicated only to the algorithmic development. Nevertheless, for clinical end users, a well designed algorithm with an interactive software is necessary for an algorithm to be utilized in their daily work. Furthermore, the software would better be open sourced in order to be used and validated by not only the authors but also the entire community. Therefore, the contribution of the present work is twofolds: first, we propose a new robust statistics based conformal metric and the conformal area driven multiple active contour framework, to simultaneously extract multiple targets from MR and CT medical imagery in 3D. Second, an open source graphically interactive 3D segmentation tool based on the aforementioned contour evolution is implemented and is publicly available for end users on multiple platforms. In using this software for the segmentation task, the process is initiated by the user drawn strokes (seeds) in the target region in the image. Then, the local robust statistics are used to describe the object features, and such features are learned adaptively from the seeds under a non-parametric estimation scheme. Subsequently, several active contours evolve simultaneously with their interactions being motivated by the principles of action and reaction-this not only guarantees mutual exclusiveness among the contours, but also no longer relies upon the assumption that the multiple objects fill the entire image domain, which was tacitly or explicitly assumed in many previous works. In doing so, the contours interact and converge to equilibrium at the desired positions of the desired multiple objects. Furthermore, with the aim of not only validating the algorithm and the software, but also demonstrating how the tool is to be used, we provide the reader reproducible experiments that demonstrate the capability of the proposed segmentation tool on several public available data sets.
Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.
Thalamo-cortical feedback loops play a key role in the processing and coordination of processing and integration of perceptual inputs and outputs, and disruption in this connection has long been hypothesized to contribute significantly to neuropsychological disturbances in schizophrenia. To test this hypothesis, we applied diffusion tensor tractography to 18 patients suffering schizophrenia and 20 control subjects. Fractional anisotropy (FA) was evaluated in the bilateral anterior and posterior limbs of the internal capsule, and correlated with clinical and neurocognitive measures. Patients diagnosed with schizophrenia showed significantly reduced FA bilaterally in the anterior but not the posterior limb of the internal capsule, compared with healthy control subjects. Lower FA correlated with lower scores on tests of declarative episodic memory in the patient group only. These findings suggest that disruptions, bilaterally, in thalamo-cortical connections in schizophrenia may contribute to disease-related impairment in the coordination of mnemonic processes of encoding and retrieval that are vital for efficient learning of new information.
The properties of carbon nanotube (CNT)/polymer composites are strongly dependent on the dispersion and orientation of CNTs in the host matrix. Quantification of the dispersion and orientation of CNTs by means of microstructure observation and image analysis has been demonstrated as a useful way to understand the structure-property relationship of CNT/polymer composites. However, due to the various morphologies and large amount of CNTs in one image, automatic and accurate identification of CNTs has become the bottleneck for dispersion/orientation analysis. To solve this problem, shape identification is performed for each pixel in the filler identification step, so that individual CNTs can be extracted from images automatically. The improved filler identification enables more accurate analysis of CNT dispersion and orientation. The dispersion index and orientation index obtained for both synthetic and real images from model compounds correspond well with the observations. Moreover, these indices help to explain the electrical properties of CNT/silicone composite, which is used as a model compound. This method can also be extended to other polymer composites with high-aspect-ratio fillers.
We propose an automatic approach for segmenting the left atrium from MRI images. In particular, the thoracic aorta is detected and used as a salient feature to find a seed region that lies inside the left atrium. A hybrid energy that combines robust statistics and localized region intensity information is employed to evolve active contours from the seed region to capture the whole left atrium. The experimental results demonstrate the accuracy and robustness of our approach.
The purpose of this study is to evaluate perfusion indices and pharmacokinetic parameters in solitary pulmonary nodules (SPNs). Thirty patients of 34 enrolled with SPNs (15-30 mm) were evaluated in this study. T1 and T2-weighted structural images and 2D turbo FLASH perfusion images were acquired with shallow free breathing. B-spline nonrigid image registration and optimization by χ² test against pharmacokinetic model curve were performed on dynamic contrast-enhanced MRI. This allowed voxel-by-voxel calculation of k(ep) , the rate constant for tracer transport to and from plasma and the extravascular extracellular space. Mean transit time, time-to-peak, initial slope, and maximum enhancement (E(max) ) were calculated from time-intensity curves fitted to a gamma variate function. After blinded data analysis, correlation with tissue histology from surgical resection or biopsy samples was performed. Histologic evaluation revealed 25 malignant and five benign SPNs. All benign SPNs had k(ep) < 1.0 min⁻¹. Nineteen of 25 (76%) malignant SPNs showed k(ep) > 1.0 min⁻¹. Sensitivity to diagnose malignant SPNs at a cutoff of k(ep) = 1.0 min⁻¹ was 76%, specificity was 100%, positive predictive value was 100%, negative predictive value was 45%, and accuracy was 80%. Of all indices studied, k(ep) was the most significant in differentiating malignant from benign SPNs.
Patients in the intensive care unit (ICU) who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the ICU, and also due to pain or other variants of noxious stimuli. While physicians select the agent(s) used for sedation and cardiovascular function, the actual administration of these agents is the responsibility of the nursing staff. If clinical decision support systems and closed-loop control systems could be developed for critical care monitoring and lifesaving interventions as well as the administration of sedation and cardiopulmonary management, the ICU nurse could be released from the intense monitoring of sedation, allowing her/him to focus on other critical tasks. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation. In this paper, we extend the deterministic rule-based expert system for cardiopulmonary management and ICU sedation framework presented in  to a stochastic setting by using probability theory to quantify uncertainty and hence deal with more realistic clinical situations.
For a given cognitive task such as language processing, the location of corresponding functional regions in the brain may vary across subjects relative to anatomy. We present a probabilistic generative model that accounts for such variability as observed in fMRI data. We relate our approach to sparse coding that estimates a basis consisting of functional regions in the brain. Individual fMRI data is represented as a weighted sum of these functional regions that undergo deformations. We demonstrate the proposed method on a language fMRI study. Our method identified activation regions that agree with known literature on language processing and established correspondences among activation regions across subjects, producing more robust group-level effects than anatomical alignment alone.
The nonlocal means (NLM) filter has become a popular approach for denoising medical images due to its excellent performance. However, its heavy computational load has been an important shortcoming preventing its use. NLM works by averaging pixels in nonlocal vicinities, weighting them depending on their similarity with the pixel of interest. This similarity is assessed based on the squared difference between corresponding pixels inside local patches centered at the locations compared. Our proposal is to reduce the computational load of this comparison by checking only a subset of salient features associated to the pixels, which suffice to estimate the actual difference as computed in the original NLM approach. The speedup achieved with respect to the original implementation is over one order of magnitude, and, when compared to more recent NLM improvements for MRI denoising, our method is nearly twice as fast. At the same time, we evidence from both synthetic and in vivo experiments that computing of appropriate salient features make the estimation of NLM weights more robust to noise. Consequently, we are able to improve the outcomes achieved with recent state of the art techniques for a wide range of realistic Signal-to-Noise ratio scenarios like diffusion MRI. Finally, the statistical characterization of the features computed allows to get rid of some of the heuristics commonly used for parameter tuning.
PURPOSE: Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients. METHODS: The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. RESULTS: Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m) ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. CONCLUSION: Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.
Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies.
The Human Placenta Project has focused attention on the need for noninvasive magnetic resonance imaging (MRI)-based techniques to diagnose and monitor placental function throughout pregnancy. The hope is that the management of placenta-related pathologies would be improved if physicians had more direct, real-time measures of placental health to guide clinical decision making. As oxygen alters signal intensity on MRI and oxygen transport is a key function of the placenta, many of the MRI methods under development are focused on quantifying oxygen transport or oxygen content of the placenta. For example, measurements from blood oxygen level-dependent imaging of the placenta during maternal hyperoxia correspond to outcomes in twin pregnancies, suggesting that some aspects of placental oxygen transport can be monitored by MRI. Additional methods are being developed to accurately quantify baseline placental oxygenation by MRI relaxometry. However, direct validation of placental MRI methods is challenging and therefore animal studies and ex vivo studies of human placentas are needed. Here we provide an overview of the current state of the art of oxygen transport and quantification with MRI. We suggest that as these techniques are being developed, increased focus be placed on ensuring they are robust and reliable across individuals and standardized to enable predictive diagnostic models to be generated from the data. The field is still several years away from establishing the clinical benefit of monitoring placental function in real time with MRI, but the promise of individual personalized diagnosis and monitoring of placental disease in real time continues to motivate this effort.
Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William III M Wells, and Sarah Frisken. 10/2019. “On the Applicability of Registration Uncertainty.” In MICCAI 2019, LNCS 11765: Pp. 410-9. Shenzhen, China: Springer.Abstract
Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately calculated registration uncertainty and misplace unwarranted confidence in the alignment solutions, severe consequences may result. For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters. The majority of existing research focuses on trying out different summary statistics as well as means to exploit them. Distinctively, in this paper, we study two rarely examined topics: (1) whether those summary statistics of the transformation distribution most informatively represent the registration uncertainty; (2) Does utilizing the registration uncertainty always be beneficial. We show that there are two types of uncertainties: the transformation uncertainty, Ut, and label uncertainty Ul. The conventional way of using Ut to quantify Ul is inappropriate and can be misleading. By a real data experiment, we also share a potentially critical finding that making use of the registration uncertainty may not always be an improvement.
External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0 ± 3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60 ± 0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.