A classical neural tract tracer, WGA-HRP, was injected at multiple sites within the brain of a macaque monkey. Histological sections of the labeled fiber tracts were reconstructed in 3D, and the fibers were segmented and registered with the anatomical post-mortem MRI from the same animal. Fiber tracing along the same pathways was performed on the DTI data using a classical diffusion tracing technique. The fibers derived from the DTI were compared with those segmented from the histology in order to evaluate the performance of DTI fiber tracing. While there was generally good agreement between the two methods, our results reveal certain limitations of DTI tractography, particularly at regions of fiber tract crossing or bifurcation.
The segmentation of newborn brain MRI is important for assessing and directing treatment options for premature infants at risk for developmental disorders, abnormalities, or even death. Segmentation of infant brain MRI is particularly challenging when compared with the segmentation of images acquired from older children and adults. We sought to develop a fully automated segmentation strategy and present here a Bayesian approach utilizing an atlas of priors derived from previous segmentations and a new scheme for automatically selecting and iteratively refining classifier training data using the STAPLE algorithm. Results have been validated by comparison to hand-drawn segmentations.
This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.
The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a "ground truth" or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare to segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labeling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, amongst others, surface, distance transform or level set representations of segmentations, and can be used to assess whether or not a rater consistently over-estimates or under-estimates the position of a boundary.
The ability to effectively identify eloquent cortex in close proximity to brain tumours is a critical component of surgical planning prior to resection. The use of electrocortical stimulation testing (ECS) during awake neurosurgical procedures remains the gold standard for mapping functional areas, yet the preoperative use of non-invasive brain imaging techniques such as fMRI are gaining popularity as supplemental surgical planning tools. In addition, the intraoperative three-dimensional display of fMRI findings co-registered to structural imaging data maximizes the utility of the preoperative mapping for the surgeon. Advances in these techniques have the potential to limit the size and duration of craniotomies as well as the strain placed on the patient, but more research accurately demonstrating their efficacy is required. In this paper, we demonstrate the integration of preoperative fMRI within a neuronavigation system to aid in surgical planning, as well as the integration of these fMRI data with intraoperative ECS mapping results into a three-dimensional dataset for the purpose of cross-validation.
Two image reconstruction methods currently dominate parallel MR imaging: SENSE and GRAPPA. While both seek to reconstruct images from subsampled multi-channel MRI data, there exist fundamental differences between the two. In particular, SENSE reconstructs an image of the excited spin-density directly whereas GRAPPA reconstructs estimates of the fully sampled raw coil data and then combines them to obtain an image. In this work we show that these differences can be exploited such that each method can compliment the other. In the case of SENSE, which requires an estimate of the coil sensitivity map before reconstruction, one can use GRAPPA to improve the coil sensitivity estimates. Alternatively, using coil sensitivity estimates and the SENSE reconstruction equations, one can improve the GRAPPA reconstruction parameter estimation. Together, these approaches can provide higher image quality than either method alone.
A method is presented to validate the segmentation of computed tomography (CT) image sequences, and im prove the accuracy and efficiency of the subsequent registration of the 3D surfaces that are reconstructed from the segmented slices. The method compares the shapes of contours extracted from neighborhoods of slices in CT stacks of tibias. The bone is first segmented by an automatic segmentation technique, and the bone contour for each slice is parameterized as a 1-D function of normalized arc length versus inscribed angle. These functions are represented as vectors within a K-dimensional space comprising the first K amplitude coefficients of their Fourier Descriptors. The similarity or coherency of neighboring contours is measured by comparing statistical properties of their vector representations within this space. Experimentation has demonstrated this technique to be very effective at automatically identifying low coherency segmentations, the removal of which significantly improved the accuracy and time efficiency of the registration of 3-D bone surface models.
Detailed measurements of water diffusion within the prostate over an extended b-factor range were performed to assess whether the standard assumption of monoexponential signal decay is appropriate in this organ. From nine men undergoing prostate MR staging examinations at 1.5 T, a single 10-mm-thick axial slice was scanned with a line scan diffusion imaging sequence in which 14 equally spaced b factors from 5 to 3,500 s/mm(2) were sampled along three orthogonal diffusion sensitization directions in 6 min. Due to the combination of long scan time and limited volume coverage associated with the multi-b-factor, multidirectional sampling, the slice was chosen online from the available T2-weighted axial images with the specific goal of enabling the sampling of presumed noncancerous regions of interest (ROIs) within the central gland (CG) and peripheral zone (PZ). Histology from prescan biopsy (n=9) and postsurgical resection (n=4) was subsequently employed to help confirm that the ROIs sampled were noncancerous. The CG ROIs were characterized from the T2-weighted images as primarily mixtures of glandular and stromal benign prostatic hyperplasia, which is prevalent in this population. The water signal decays with b factor from all ROIs were clearly non-monoexponential and better served with bi- vs. monoexponential fits, as tested using chi(2)-based F test analyses. Fits to biexponential decay functions yielded intersubject fast diffusion component fractions in the order of 0.73+/-0.08 for both CG and PZ ROIs, fast diffusion coefficients of 2.68+/-0.39 and 2.52+/-0.38 microm(2)/ms and slow diffusion coefficients of 0.44+/-0.16 and 0.23+/-0.16 um(2)/ms for CG and PZ ROIs, respectively. The difference between the slow diffusion coefficients within CG and PZ was statistically significant as assessed with a Mann-Whitney nonparametric test (P<.05). We conclude that a monoexponential model for water diffusion decay in prostate tissue is inadequate when a large range of b factors is sampled and that biexponential analyses are better suited for characterizing prostate diffusion decay curves.
Previously, activation of vesicular transport in the brain microvasculature was shown to be one of the mechanisms of focused ultrasound-induced blood-brain barrier (BBB) opening. In the present study, we aimed to estimate the rate of the transendothelial vesicular traffic after focused ultrasound sonication in the rabbit brain, using ultrastructural morphometry and horseradish peroxidase (HRP) as a tracer. In the capillaries, the mean endothelial pinocytotic densities (the number of HRP-containing vesicles per microm(2) of the cell cytoplasm) were 0.9 and 1.05 vesicles/microm(2) 1 h after sonication with ultrasound frequencies of 0.69 and 0.26 MHz, respectively. In the arterioles, these densities were 1.63 and 2.43 vesicles/microm(2), values 1.8 and 2.3 times higher. In control locations, the densities were 0.7 and 0.14 vesicles/microm(2) for capillaries and arterioles, respectively. A small number of HRP-positive vesicles were observed in the venules. Focal delivery of HRP tracer was also observed in light microscopy. The results indicate that the precapillary microvessels play an important role in macromolecular transcytoplasmic traffic through the ultrasound-induced BBB modulation, which should be considered in the future development of trans-BBB drug delivery strategies.
MR diffusion tensor imaging (DTI) of the brain and spine provides a unique tool for both visualizing directionality and assessing intactness of white matter fiber tracts in vivo. At the spatial resolution of clinical MRI, much of primate white matter is composed of interdigitating fibers. Analyses based on an assumed single diffusion tensor per voxel yield important information about the average diffusion in the voxel but fail to reveal structure in the presence of crossing tracts. Until today, all clinical scans assume only one tensor, causing potential serious errors in tractography. Since high angular resolution imaging remains, so far, untenable for routine clinical use, a method is proposed whereby the single-tensor field is augmented with additional information gleaned from standard clinical DTI. The method effectively resolves two distinct tract directions within voxels, in which only two tracts are assumed to exist. The underlying constrained two-tensor model is fitted in two stages, utilizing the information present in the single-tensor fit. As a result, the necessary MRI time can be drastically reduced when compared with other approaches, enabling widespread clinical use. Upon evaluation in simulations and application to in vivo human brain DTI data, the method appears to be robust and practical and, if correctly applied, could elucidate tract directions at critical points of uncertainty.
The development of large-aperture multiple-source transducer arrays for ultrasound transmission through the human skull has demonstrated the possibility of controlled and substantial acoustic energy delivery into the brain parenchyma without the necessitation of a craniotomy. The individual control of acoustic parameters from each ultrasound source allows for the correction of distortions arising from transmission through the skull bone and also opens up the possibility for electronic steering of the acoustic focus within the brain. In addition, the capability to adjust the frequency of insonation at different locations on the skull can have an effect on ultrasound transmission. To determine the efficacy and applicability of a multiple-frequency approach with such a device, this study examined the frequency dependence of ultrasound transmission in the range of 0.6-1.4 MHz through a series of 17 points on four ex vivo human skulls. Effects beyond those that are characteristic of frequency-dependent attenuation were examined. Using broadband pulses, it was shown that the reflected spectra from the skull revealed information regarding ultrasound transmission at specific frequencies. A multiple-frequency insonation with optimized frequencies over the entirety of five skull specimens was found to yield on average a temporally brief 230% increase in the transmitted intensity with an 88% decrease in time-averaged intensity transmission within the focal volume. This finding demonstrates a potential applicability of a multiple-frequency approach in transcranial ultrasound transmission.
The introduction and development, over the last three decades, of magnetic resonance (MR) imaging and MR spectroscopy technology for in vivo studies of the human brain represents a truly remarkable achievement, with enormous scientific and clinical ramifications. These effectively non-invasive techniques allow for studies of the anatomy, the function and the metabolism of the living human brain. They have allowed for new understandings of how the healthy brain works and have provided insights into the mechanisms underlying multiple disease processes which affect the brain. Different MR techniques have been developed for studying anatomy, function and metabolism. The primary focus of this review is to describe these different methodologies and to briefly review how they are being employed to more fully appreciate the intricacies associated with the organ, which most distinctly differentiates the human species from the other animal forms on earth.
To retrospectively evaluate the four-year experience of a quality assurance method for a MRI-guided focused ultrasound system that uses temperature maps acquired during heating in an ultrasound/MRI phantom. This quality assurance method was performed before 148 clinical uterine fibroid thermal ablation treatments. The stability of the peak temperature rise, the targeting accuracy, the shape of the heated zone, and the noise level in the imaging was evaluated. The peak temperature rise was mostly stable for the first three years. An increase in heating was observed when the system was replaced after year three. Detection of this increase was taken into account in the subsequent clinical treatments. A small secondary hotspot was detected by the temperature maps and was seen to be resolved after system calibration. The average standard deviation in unheated regions of the phantom in the temperature maps was 0.5 +/- 0.2 degrees C; it was less than 1 degrees C in all but one procedure. The average initial targeting error was 2.8 +/- 1.8 and 2.8 +/- 2.1 mm in two radial directions and 7.7 +/- 2.9 mm along the ultrasound beam direction. The width of the heating profile was consistent over the four years. This simple method to evaluate the performance appeared to be sensitive to small changes in system performance, which was adequately stable over a four-year time period.
We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term). The robustness of the algorithm is achieved through the introduction of an outliers rejection step in this gradual registration process. We ensure the validity of the deformation by the use of a biomechanical model of the brain specific to the patient, discretized with the finite element method. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift up to 13 mm.
In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.
This paper presents a novel approach that three-dimensionally visualizes and evaluates stenoses in human coronary arteries by using harmonic skeletons. A harmonic skeleton is the center line of a multi-branched tubular surface extracted based on a harmonic function, which is the solution of the Laplace equation. This skeletonization method guarantees smoothness and connectivity and provides a fast and straightforward way to calculate local cross-sectional areas of the arteries, and thus provides the possibility to localize and evaluate coronary artery stenosis, which is a commonly seen pathology in coronary artery disease.
We review our experience using an open 0.5-T magnetic resonance (MR) interventional unit to guide procedures in the prostate. This system allows access to the patient and real-time MR imaging simultaneously and has made it possible to perform prostate biopsy and brachytherapy under MR guidance. We review MR imaging of the prostate and its use in targeted therapy, and describe our use of image processing methods such as image registration to further facilitate precise targeting. We describe current developments with a robot assist system being developed to aid radioactive seed placement.
This paper presents a new algorithm for non-rigid registration between two doubly-connected regions. Our algorithm is based on harmonic analysis and the theory of optimal mass transport. It assumes an underlining continuum model, in which the total amount of mass is exactly preserved during the transformation of tissues. We use a finite element approach to numerically implement the algorithm.
Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer (PCa). Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient and accurate. Our goal was to develop an open source software platform to support evaluation, refinement and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface, and interchange of image segmentation and registration components to assess their effect on the processing time, precision and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and 10 prospective tpMRgBx cases. Mean Landmark Registration Error (LRE) for retrospective evaluation was 1.88 ±2.63 mm and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ±2.40 min, and BTE of 2.40 ±0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.
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.