Since the introduction of diffusion weighted imaging (DWI) as a method for examining neural connectivity, its accuracy has not been formally evaluated. In this study, we directly compared connections that were visualized using injected neural tract tracers (WGA-HRP) with those obtained using in-vivo diffusion tensor imaging (DTI) tractography. First, we injected the tracer at multiple sites in the brain of a macaque monkey; second, we reconstructed the histological sections of the labeled fiber tracts in 3D; third, we segmented and registered the fibers (somatosensory and motor tracts) with the anatomical in-vivo MRI from the same animal; and last, we conducted fiber tracing along the same pathways on the DTI data using a classical diffusion tracing technique with the injection sites as seeds. To evaluate the performance of DTI fiber tracing, we compared the fibers derived from the DTI tractography with those segmented from the histology. We also studied the influence of the parameters controlling the tractography by comparing Dice superimposition coefficients between histology and DTI segmentations. While there was generally good visual agreement between the two methods, our quantitative comparisons reveal certain limitations of DTI tractography, particularly for regions at remote locations from seeds. We have thus demonstrated the importance of appropriate settings for realistic tractography results.
We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that employs a multinomial model of joint intensities with parameters for which we only have a prior distribution. To obtain an MAP estimate of the aligning transformation alone, we treat the multinomial parameters as nuisance parameters, and marginalize them out. If the prior on those is uninformative, the marginalization leads to registration by minimization of joint entropy. With an informative prior, the marginalization leads to minimization of the entropy of the data pooled with pseudo observations from the prior. In addition, we show that the marginalized objective function can be optimized by the Expectation-Maximization (EM) algorithm, which yields a simple and effective iteration for solving entropy-based registration problems. Experimentally, we demonstrate the effectiveness of the resulting EM iteration for rapidly solving a challenging intra-operative registration problem.
Using direct cortical stimulation to map language function during awake craniotomy is a well-described and useful technique. However, the optimum neuropsychological tasks to use have not been detailed. We used both functional MRI (fMRI) and direct cortical stimulation to compare the sensitivity of two behavioral paradigms, number counting and object naming, in the demonstration of eloquent cortical language areas. Fifteen patients with left hemisphere lesions and seven healthy control subjects participated. Patients had both preoperative fMRI at 3 T and direct cortical stimulation. Patients and controls performed object naming and number counting during fMRI at 3 T. Laterality indices were calculated from the fMRI maps for the Number-counting>Object-naming and Object-naming>Number-counting contrasts. The same number-counting and object-naming paradigms were tested during awake craniotomy and assessed for sensitivity to speech disruption. In all patients during intraoperative cortical stimulation, speech disruption occurred at more sites during object naming than during number counting. Subtle speech errors were only elicited with the object-naming paradigm, whereas only speech arrest and/or hypophonia were measured using the number counting paradigm. In both patients and controls, fMRI activation maps demonstrated greater left lateralization for object naming as compared to number counting in both frontal and temporal language areas. Number counting resulted in a more bihemispheric distribution of activations than object naming. Both cortical stimulation testing and fMRI suggest that automated speech tasks such as number counting may not fully engage putative language networks and therefore are not optimal for language localization for surgical planning.
A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires point correspondence determined along the fiber pathways. This knowledge is also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, and point correspondences along the trajectories. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. Probabilistic assignment of the trajectories to clusters is controlled by imposing a minimum threshold on the membership probabilities, to remove outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.
Geometric models of white matter architecture play an increasing role in neuroscientific applications of diffusion tensor imaging, and the most popular method for building them is fiber tractography. For some analysis tasks, however, a compelling alternative may be found in the first and second derivatives of diffusion anisotropy. We extend to tensor fields the notion from classical computer vision of ridges and valleys, and define anisotropy creases as features of locally extremal tensor anisotropy. Mathematically, these are the loci where the gradient of anisotropy is orthogonal to one or more eigenvectors of its Hessian. We propose that anisotropy creases provide a basis for extracting a skeleton of the major white matter pathways, in that ridges of anisotropy coincide with interiors of fiber tracts, and valleys of anisotropy coincide with the interfaces between adjacent but distinctly oriented tracts. The crease extraction algorithm we present generates high-quality polygonal models of crease surfaces, which are further simplified by connected-component analysis. We demonstrate anisotropy creases on measured diffusion MRI data, and visualize them in combination with tractography to confirm their anatomic relevance.
PURPOSE: To assess the feasibility of dynamic MRI of swallowing in a seated position using an open-configuration MRI scanner, and to compare its capacity for motion analysis around the pharyngeal wall with that of videofluorography.
MATERIALS AND METHODS: Six healthy individuals (four women and two men, mean age = 31.4 +/- 7.5 years) were examined with an open-configuration MRI system using a fast spoiled gradient-recalled echo (SPGR) sequence. Dynamic imaging was performed while the subjects were in a seated position after they swallowed oral contrast medium from a cup. An oral and maxillofacial radiologist measured the motion of six structures: the hyoid bone (HB), larynx (LX), upper oropharynx (UOP), lower oropharynx (LOP), pharyngoesophageal segment (PES) behind the vocal folds, and upper esophagus (ESO). The measured motions were compared with reported values from videofluorography-based observations.
RESULTS: Open-configuration MRI depicted the anatomic structures related to swallowing (lip, tongue, soft palate, mandible, pharynx, HB, LX, and PES), and the course of the mylohyoid muscle (MM). The vertical and anteroposterior displacements of these structures did not differ significantly from those measured by videofluorography.
CONCLUSION: Dynamic imaging of swallowing using open-configuration MRI provides image information comparable to that obtained from videofluorography.
OBJECTIVE: The objective of our study was to report the occurrence of benign inflammatory nodules that develop in or near applicator tracks after percutaneous radiofrequency ablation and cryoablation of renal tumors.
CONCLUSION: Benign inflammatory nodules occur rarely after percutaneous ablation of renal tumors and may mimic tumor seeding of the applicator track.
The logarithm of the odds ratio (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology, as an alternative representation of probabilities. Here, we use LogOdds to place probabilistic atlases in a linear vector space. This representation has several useful properties for medical imaging. For example, it not only encodes the shape of multiple anatomical structures but also captures some information concerning uncertainty. We demonstrate that the resulting vector space operations of addition and scalar multiplication have natural probabilistic interpretations. We discuss several examples for placing label maps into the space of LogOdds. First, we relate signed distance maps, a widely used implicit shape representation, to LogOdds and compare it to an alternative that is based on smoothing by spatial Gaussians. We find that the LogOdds approach better preserves shapes in a complex multiple object setting. In the second example, we capture the uncertainty of boundary locations by mapping multiple label maps of the same object into the LogOdds space. Third, we define a framework for non-convex interpolations among atlases that capture different time points in the aging process of a population. We evaluate the accuracy of our representation by generating a deformable shape atlas that captures the variations of anatomical shapes across a population. The deformable atlas is the result of a principal component analysis within the LogOdds space. This atlas is integrated into an existing segmentation approach for MR images. We compare the performance of the resulting implementation in segmenting 20 test cases to a similar approach that uses a more standard shape model that is based on signed distance maps. On this data set, the Bayesian classification model with our new representation outperformed the other approaches in segmenting subcortical structures.
RATIONALE AND OBJECTIVES: We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented.
MATERIALS AND METHODS: The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms.
RESULTS: We demonstrate the validation of the novel segmentation algorithm within the proposed framework. We show its performance and compare it with existent segmentation. The image datasets and software are available at http://www.brain-tumor-repository.org/.
CONCLUSIONS: We present an Internet resource that provides access to MR brain tumor image data and segmentation that can be openly used by the research community. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results.
We propose a new white matter atlas creation method that learns a model of the common white matter structures present in a group of subjects. We demonstrate that our atlas creation method, which is based on group spectral clustering of tractography, discovers structures corresponding to expected white matter anatomy such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, and corona radiata. The white matter clusters are augmented with expert anatomical labels and stored in a new type of atlas that we call a high-dimensional white matter atlas. We then show how to perform automatic segmentation of tractography from novel subjects by extending the spectral clustering solution, stored in the atlas, using the Nystrom method. We present results regarding the stability of our method and parameter choices. Finally we give results from an atlas creation and automatic segmentation experiment. We demonstrate that our automatic tractography segmentation identifies corresponding white matter regions across hemispheres and across subjects, enabling group comparison of white matter anatomy.
Organ motion compensation in image-guided therapy is an active area of research. However, there has been little research on motion tracking and compensation in magnetic resonance imaging (MRI)-guided therapy. In this paper, we present a method to track a moving organ in MRI and control an active mechanical device for motion compensation. The method proposed is based on MRI navigator echo tracking enhanced by Kalman filtering for noise robustness. We also developed an extrapolation scheme to resolve any discrepancies between tracking and device control sampling rates. The algorithm was tested in a simulation study using a phantom and an active mechanical tool holder. We found that the method is feasible to use in a clinical MRI scanner with sufficient accuracy (0.36 mm to 1.51 mm depending on the range of phantom motion) and is robust to noise. The method proposed may be useful in MRI-guided targeted therapy, such as focused ultrasound therapy for a moving organ.
Guided by empirically established connections between clinically important tissue properties and diffusion tensor parameters, we introduce a framework for decomposing variations in diffusion tensors into changes in shape and orientation. Tensor shape and orientation both have three degrees-of-freedom, spanned by invariant gradients and rotation tangents, respectively. As an initial demonstration of the framework, we create a tunable measure of tensor difference that can selectively respond to shape and orientation. Second, to analyze the spatial gradient in a tensor volume (a third-order tensor), our framework generates edge strength measures that can discriminate between different neuroanatomical boundaries, as well as creating a novel detector of white matter tracts that are adjacent yet distinctly oriented. Finally, we apply the framework to decompose the fourth-order diffusion covariance tensor into individual and aggregate measures of shape and orientation covariance, including a direct approximation for the variance of tensor invariants such as fractional anisotropy.
Our earlier study indicated that functional magnetic resonance imaging (fMRI)-based detection and feedback of regional cortical activity from the auditory area enabled a group of individuals to increase the level of activation mediated by auditory attention during sound stimulation. The long-term ability to maintain an increased level of cortical activation, extending to a time period of a few weeks, however, has not been investigated. We used real-time fMRI to confirm the utility of fMRI in forming a basis for the regulation of brain function to increase the activation in the auditory areas, and demonstrated that the learned ability could be retained after a 2-week period, with additional involvement of an attention-related neural network.
The purpose of this study is to examine the feasibility of simultaneous Magnetic Resonance Imaging (MRI) and Ultrasound (US) imaging in visualizing anatomical structures and functions in human carotid arteries. US has high frame rate in visualizing dynamic changes while high resolution MRI is capable of capturing volumetric structures with the best tissue contrast. Concurrent multi-modal image acquisition allows fusion of US Doppler flow measurement with volumetric MRI. We present a method for acquiring MR images in a known orientation with respect to US image by passive fiducial tracking and demonstrate concurrent real-time imaging in the right Common Carotid Artery (CCA) in both modalities. Preliminary results suggest that US and MRI can operate concurrently with proper shielding. Dispensability measurements are feasible on both modalities at the co-incident plane.
A minimalist numerical model of white matter is presented, the objective of which is to help provide a biological basis for improved diffusion tensor imaging (DTI) analysis. Water diffuses, relaxes, and exchanges in three compartments-intracellular, extracellular, and myelin sheath. Exchange between compartments is defined so as to depend on the diffusion coefficients and the compartment sizes. Based on the model, it is proposed that an additive "baseline tensor" that correlates with intraaxonal water volume be included in the computation. Anisotropy and tortuosity calculated from such analysis may correspond better to tract ultrastructure than if calculated without the baseline. According to the model, reduced extracellular volume causes increased baseline and reduced apparent diffusion. Depending on the pulse sequence, reduced permeability can cause an increase in both the baseline and apparent diffusion.
Multisubject statistical analyses of diffusion tensor images in regions of specific white matter tracts have commonly measured only the mean value of a scalar invariant such as the fractional anisotropy (FA), ignoring the spatial variation of FA along the length of fiber tracts. We propose to instead perform tract-based morphometry (TBM), or the statistical analysis of diffusion MRI data in an anatomical tract-based coordinate system. We present a method for automatic generation of white matter tract arc length parameterizations, based on learning a fiber bundle model from tractography from multiple subjects. Our tract-based coordinate system enables TBM for the detection of white matter differences in groups of subjects. We present example TBM results from a study of interhemispheric differences in FA.
In algorithms for processing diffusion tensor images, two common ingredients are interpolating tensors, and measuring the distance between them. We propose a new class of interpolation paths for tensors, termed geodesic-loxodromes, which explicitly preserve clinically important tensor attributes, such as mean diffusivity or fractional anisotropy, while using basic differential geometry to interpolate tensor orientation. This contrasts with previous Riemannian and Log-Euclidean methods that preserve the determinant. Path integrals of tangents of geodesic-loxodromes generate novel measures of over-all difference between two tensors, and of difference in shape and in orientation.
We present an algorithm to generate samples from probability distributions on the space of curves. We view a traditional curve evolution energy functional as a negative log probability distribution and sample from it using a Markov chain Monte Carlo (MCMC) algorithm. We define a proposal distribution by generating smooth perturbations to the normal of the curve and show how to compute the transition probabilities to ensure that the samples come from the posterior distribution. We demonstrate some advantages of sampling methods such as robustness to local minima, better characterization of multi-modal distributions, access to some measures of estimation error, and ability to easily incorporate constraints on the curve.
Traditional ultrasound imaging methods rely on the bandwidth and center frequency of transduction to achieve axial and radial image resolution, respectively. In this study, a new modality for spatially localizing scattering targets in a two-dimensional field is presented. In this method, the bandwidth of field excitation is high, and the center frequency is lowered such that the corresponding wavelengths are substantially larger than the target profiles. Furthermore, full two-dimensional field measurements are obtained with single send-receive sequences, demonstrating a substantial simplification of the traditional scanning techniques. Field reconstruction is based on temporal-spectral cross-correlations between measured backscatter data and a library of region of interest (ROI) backscatter data measured a priori. The transducer design is based upon a wedge-shaped geometry, which was shown to yield spatially frequency-separated bandwidths of up to 156% with center frequencies of 1.38 MHz. Initial results with these send-and-receive transducer parameters and cylindrical reflection targets in a 10-mm x 10-mm ROI demonstrate two-dimensional target localization to within 0.5 mm. Spatial localization of point scatterers is demonstrated for single and multiple scattering sites.
Patient-mounted needle guide devices for percutaneous ablation are vulnerable to patient motion. The objective of this study is to develop and evaluate a software system for an MRI-compatible patient-mounted needle guide device that can adaptively compensate for displacement of the device due to patient motion using a novel image-based automatic device-to-image registration technique. We have developed a software system for an MRI-compatible patient-mounted needle guide device for percutaneous ablation. It features fully-automated image-based device-to-image registration to track the device position, and a device controller to adjust the needle trajectory to compensate for the displacement of the device. We performed: (a) a phantom study using a clinical MR scanner to evaluate registration performance; (b) simulations using intraoperative time-series MR data acquired in 20 clinical cases of MRI-guided renal cryoablations to assess its impact on motion compensation; and (c) a pilot clinical study in three patients to test its feasibility during the clinical procedure. FRE, TRE, and success rate of device-to-image registration were [Formula: see text] mm, [Formula: see text] mm, and 98.3% for the phantom images. The simulation study showed that the motion compensation reduced the targeting error for needle placement from 8.2 mm to 5.4 mm (p < 0.0005) in patients under general anesthesia (GA), and from 14.4 mm to 10.0 mm ([Formula: see text]) in patients under monitored anesthesia care (MAC). The pilot study showed that the software registered the device successfully in a clinical setting. Our simulation study demonstrated that the software system could significantly improve targeting accuracy in patients treated under both MAC and GA. Intraprocedural image-based device-to-image registration was feasible.
PURPOSE: To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen. MATERIALS AND METHODS: The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time. RESULTS: The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor. CONCLUSION: The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
OBJECTIVE: The purpose of this article is to report our intermediate to long-term outcomes with image-guided percutaneous hepatic tumor cryoablation and to evaluate its technical success, technique efficacy, local tumor progression, and adverse event rate. MATERIALS AND METHODS: Between 1998 and 2014, 299 hepatic tumors (243 metastases and 56 primary tumors; mean diameter, 2.5 cm; median diameter, 2.2 cm; range, 0.3-7.8 cm) in 186 patients (95 women; mean age, 60.9 years; range, 29-88 years) underwent cryoablation during 236 procedures using CT (n = 126), MRI (n = 100), or PET/CT (n = 10) guidance. Technical success, technique efficacy at 3 months, local tumor progression (mean follow-up, 2.5 years; range, 2 months to 14.6 years), and adverse event rates were calculated. RESULTS: The technical success rate was 94.6% (279/295). The technique efficacy rate was 89.5% (231/258) and was greater for tumors smaller than 4 cm (93.4%; 213/228) than for larger tumors (60.0%; 18/30) (p < 0.0001). Local tumor progression occurred in 23.3% (60/258) of tumors and was significantly more common after the treatment of tumors 4 cm or larger (63.3%; 19/30) compared with smaller tumors (18.0%; 41/228) (p < 0.0001). Adverse events followed 33.8% (80/236) of procedures and were grade 3-5 in 10.6% (25/236) of cases. Grade 3 or greater adverse events more commonly followed the treatment of larger tumors (19.5%; 8/41) compared with smaller tumors (8.7%; 17/195) (p = 0.04). CONCLUSION: Image-guided percutaneous cryoablation of hepatic tumors is efficacious; however, tumors smaller than 4 cm are more likely to be treated successfully and without an adverse event.
OBJECTIVE: We report nine consecutive percutaneous image-guided cryoablation procedures of head and neck tumors in seven patients (four men and three women; mean age, 68 years; age range, 50-78 years). Ablation of the entire tumor for local control or ablation of a region of tumor for pain relief or preservation of function was achieved in eight of nine procedures. One patient experienced intraprocedural bradycardia, and another developed a neopharyngeal abscess. There were no deaths, permanent neurologic or functional deficits, vascular complications, or adverse cosmetic sequelae due to the procedures. CONCLUSION: Percutaneous image-guided cryoablation offers a potentially less morbid minimally invasive treatment option than salvage head and neck surgery. The complications that we encountered may be avoidable with increased experience. Further work is needed to continue improving the safety and efficacy of cryoablation of head and neck tumors and to continue expanding the use of cryoablation in patients with head and neck tumors that cannot be treated surgically.