INTRODUCTION AND METHODS: Compound muscle action potentials (CMAPs) elicited by transcranial magnetic stimulation (TMS) are characterized by enormous variability, even when attempts are made to stimulate the same scalp location. This report describes the results of a comparison of the spatial errors in coil placement and resulting CMAP characteristics using a guided and blind TMS stimulation technique. The former uses a coregistration system, which displays the intersection of the peak TMS induced electric field with the cortical surface. The latter consists of the conventional placement of the TMS coil on the optimal scalp position for activation of the first dorsal interossei (FDI) muscle. RESULTS: Guided stimulation resulted in significantly improved spatial precision for exciting the corticospinal projection to the FDI compared to blind stimulation. This improved precision of coil placement was associated with a significantly increased probability of eliciting FDI responses. Although these responses tended to have larger amplitudes and areas, the coefficient of variation between guided and blind stimulation induced CMAPs did not significantly differ. CONCLUSION: The results of this study demonstrate that guided stimulation improves the ability to precisely revisit previously stimulated cortical loci as well as increasing the probability of eliciting TMS induced CMAPs. Response variability, however, is due to factors other than coil placement.
The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion draws its blood supply and where it drains. During surgery the vessels serve as landmarks and guidelines to the lesion. The more minute the information is, the more precise the navigation and localization of computer guided procedures. Beyond neurosurgery and neurological study, vascular information is also crucial in cardiovascular surgery, diagnosis, and research. This paper addresses the problem of automatic segmentation of complicated curvilinear structures in three-dimensional imagery, with the primary application of segmenting vasculature in magnetic resonance angiography (MRA) images. The method presented is based on recent curve and surface evolution work in the computer vision community which models the object boundary as a manifold that evolves iteratively to minimize an energy criterion. This energy criterion is based both on intensity values in the image and on local smoothness properties of the object boundary, which is the vessel wall in this application. In particular, the method handles curves evolving in 3D, in contrast with previous work that has dealt with curves in 2D and surfaces in 3D. Results are presented on cerebral and aortic MRA data as well as lung computed tomography (CT) data.
This paper describes a unified approach to the detection of point landmarks-whose neighborhoods convey discriminant information-including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application of tensor algebra. Results on both synthetic and real tensor data are presented.
Magnetic resonance (MR) imaging--guided prostate biopsy in a 0.5-T open imager is described, validated in phantom studies, and performed in two patients. The needles are guided by using fast gradient-recalled echo and T2-weighted fast spin-echo images. Surgical navigation software provided T2-weighted images critical to targeting the peripheral zone and the tumor. MR imaging can be used to guide prostate biopsy.
In order to enhance 3D image data from magnetic resonance angiography (MRA), a novel method based on the theory of multidimensional adaptive filtering has been developed. The purpose of the technique is to suppress image noise while enhancing important structures. The method is based on local structure estimation using six 3D orientation selective filters, followed by an adaptive filtering step controlled by the local structure information. The complete filtering procedure requires approximately 3 minutes of computational time on a standard workstation for a 256 x 256 x 64 data set. The method has been evaluated using a mathematical vessel model and in vivo MRA data (both phase contrast and time of flight (TOF)). 3D adaptive filtering results in a better delineation of small blood vessels and efficiently reduces the high-frequency noise. Depending on the data acquisition and the original data type, contrast-to-noise ratio (CNR) improvements of up to 179% (8.9 dB) were observed. 3D adaptive filtering may provide an alternative to prolonging the scan time or using contrast agents in MRA when the CNR is low.
A surgical guidance and visualization system is presented, which uniquely integrates capabilities for data analysis and on-line interventional guidance into the setting of interventional MRI. Various pre-operative scans (T1- and T2-weighted MRI, MR angiography, and functional MRI (fMRI)) are fused and automatically aligned with the operating field of the interventional MR system. Both pre-surgical and intra-operative data may be segmented to generate three-dimensional surface models of key anatomical and functional structures. Models are combined in a three-dimensional scene along with reformatted slices that are driven by a tracked surgical device. Thus, pre-operative data augments interventional imaging to expedite tissue characterization and precise localization and targeting. As the surgery progresses, and anatomical changes subsequently reduce the relevance of pre-operative data, interventional data is refreshed for software navigation in true real time. The system has been applied in 45 neurosurgical cases and found to have beneficial utility for planning and guidance. J. Magn. Reson. Imaging 2001;13:967-975.
OBJECTIVE: A major shortcoming of image-guided navigational systems is the use of preoperatively acquired image data, which does not account for intraoperative changes in brain morphology. The occurrence of these surgically induced volumetric deformations ("brain shift") has been well established. Maximal measurements for surface and midline shifts have been reported. There has been no detailed analysis, however, of the changes that occur during surgery. The use of intraoperative magnetic resonance imaging provides a unique opportunity to obtain serial image data and characterize the time course of brain deformations during surgery. METHODS: The vertically open intraoperative magnetic resonance imaging system (SignaSP, 0.5 T; GE Medical Systems, Milwaukee, WI) permits access to the surgical field and allows multiple intraoperative image updates without the need to move the patient. We developed volumetric display software (the 3D Slicer) that allows quantitative analysis of the degree and direction of brain shift. For 25 patients, four or more intraoperative volumetric image acquisitions were extensively evaluated. RESULTS: Serial acquisitions allow comprehensive sequential descriptions of the direction and magnitude of intraoperative deformations. Brain shift occurs at various surgical stages and in different regions. Surface shift occurs throughout surgery and is mainly attributable to gravity. Subsurface shift occurs during resection and involves collapse of the resection cavity and intraparenchymal changes that are difficult to model. CONCLUSION: Brain shift is a continuous dynamic process that evolves differently in distinct brain regions. Therefore, only serial imaging or continuous data acquisition can provide consistently accurate image guidance. Furthermore, only serial intraoperative magnetic resonance imaging provides an accurate basis for the computational analysis of brain deformations, which might lead to an understanding and eventual simulation of brain shift for intraoperative guidance.
Orientation and shape of the acetabulum were determined by the use of three-dimensional reconstruction of computed tomography (CT) data sets in 22 patients with a total of 30 slipped capital femoral epiphyses. We developed an interactive three-dimensional software program to measure the anteversion and inclination of the acetabulum without projectional and pelvis-tilting errors. Furthermore, we determined the height, width, depth, volume, and surface of the acetabulum as parameters describing the acetabular shape. Comparison of the affected side with the contralateral unaffected hip showed no significant differences for acetabular orientation and shape. The relationship between the degree of the slip and the acetabular orientation was calculated. No correlation was found. Based on the results of this study, we conclude that the slipping of the capital femoral epiphysis has no influence on acetabular development.
A three-dimensional (3D) analysis based on computed tomography was performed to study the 3D geometry of the proximal femur in cases of slipped capital femoral epiphysis (SCFE). For this purpose, new interactive software was developed to analyze hip joint geometry using 3D models without pelvis tilting and projected errors. Twenty-two patients, 8 girls and 14 boys, with a total of 30 slipped capital femoral epiphyses, were reviewed. In the affected hips, we observed a reduced femoral anteversion of 7.0 degrees (vs. 12.7 degrees) and a reduced femoral shaft neck angle of 134.2 degrees (vs. 141.0 degrees). In response to these results, we suggest that an SCFE is associated with reduced femoral anteversion and a reduced femoral shaft neck angle.
An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. The automated method (operator time, 5-10 minutes) allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation (operator time, 3-5 hours), making automated segmentation practical for low-grade gliomas and meningiomas.
Interventional MRI (IMRI) has entered into a new stage in which computer-based techniques play an increasing role in planning, monitoring, and controlling the procedures. The use of interactive imaging, navigational image guidance techniques, and image processing methods is demonstrated in various applications. The integration of intraoperative MRI guidance and computer-assisted surgery will greatly accelerate the clinical utility of image-guided therapy in general and interventional MRI in particular. J. Magn. Reson. Imaging 2001;13:69-77.
OBJECTIVE: To investigate the relationship between magnetic resonance imaging regional lesion burden and cognitive performance in multiple sclerosis (MS) over a 4-year follow-up period. DESIGN: Twenty-eight patients with MS underwent magnetic resonance imaging and took the Brief, Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis at baseline, 1-year, and 4-year follow-up. An automated 3-dimensional lesion detection method was used to identify MS lesions within anatomical regions on proton density T2-weighted images. The relationship between magnetic resonance imaging regional lesion volumes and the Brief, Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis results was examined using regression analyses. RESULTS: At all time points, frontal lesion volume represented the greatest proportion of total lesion volume, and the percentage of white matter classified as lesion was also highest in frontal and parietal regions. On neuropsychological testing, when compared with age- and educational level-matched control subjects, patients with MS showed significant impairment on tests of sustained attention, processing speed, and verbal memory (P<.001). Performance on these measures was negatively correlated with MS lesion volume in frontal and parietal regions at baseline, 1-year, and 4-year follow-up (R = -0.55 to -0.73, P<.001). CONCLUSIONS: Multiple sclerosis lesions show a propensity for frontal and parietal white matter. Lesion burden in these areas was strongly associated with performance on tasks requiring sustained complex attention and working verbal memory. This relationship was consistent over a 4-year period, suggesting that disruption of frontoparietal subcortical networks may underlie the pattern of neuropsychological impairment seen in many patients with MS.
Intraoperative line scan diffusion imaging (LSDI) on a 0.5 Tesla interventional MRI was performed during neurosurgery in three patients. Diffusion trace images were obtained in acute ischemic cases. Scan time per slice was 46 seconds and 94 seconds, respectively, for diffusion tensor images. Diagnosis of acutely developed vascular occlusion was confirmed with follow-up scans. White matter tracts were displayed with the principal eigenvectors and provided guidance for the tumor surgery. In all cases, the diagnostic utility of LSDI was established. J. Magn. Reson. Imaging 2001;13:115-119.
Functional measures have consistently shown prefrontal abnormalities in schizophrenia. However, structural magnetic resonance imaging (MRI) findings of prefrontal volume reduction have been less consistent. In this study, we evaluated prefrontal gray matter volume in first episode (first hospitalized) patients diagnosed with schizophrenia, compared with first episode patients diagnosed with affective psychosis and normal comparison subjects, to determine the presence in and specificity of prefrontal abnormalities to schizophrenia. Prefrontal gray and white matter volumes were measured from first episode patients with schizophrenia (n = 17), and from gender and parental socio-economic status-matched subjects with affective (mainly manic) psychosis (n = 17) and normal comparison subjects (n = 17), age-matched within a narrow age range (18--29 years). Total (left and right) prefrontal gray matter volume was significantly reduced in first episode schizophrenia compared with first episode affective psychosis and comparison subjects. Follow-up analyses indicated significant left prefrontal gray matter volume reduction and trend level reduction on the right. Schizophrenia patients showed 9.2% reduction on the left and 7.7% reduction on the right compared with comparison subjects. White matter volumes did not differ among groups. These data suggest that prefrontal cortical gray matter volume reduction is selectively present at first hospitalization in schizophrenia but not affective psychosis.
BACKGROUND: Magnetic resonance imaging studies in schizophrenia have revealed abnormalities in temporal lobe structures, including the superior temporal gyrus. More specifically, abnormalities have been reported in the posterior superior temporal gyrus, which includes the Heschl gyrus and planum temporale, the latter being an important substrate for language. However, the specificity of the Heschl gyrus and planum temporale structural abnormalities to schizophrenia vs affective psychosis, and the possible confounding roles of chronic morbidity and neuroleptic treatment, remain unclear. METHODS: Magnetic resonance images were acquired using a 1.5-T magnet from 20 first-episode (at first hospitalization) patients with schizophrenia (mean age, 27.3 years), 24 first-episode patients with manic psychosis (mean age, 23.6 years), and 22 controls (mean age, 24.5 years). There was no significant difference in age for the 3 groups. All brain images were uniformly aligned and then reformatted and resampled to yield isotropic voxels. RESULTS: Gray matter volume of the left planum temporale differed among the 3 groups. The patients with schizophrenia had significantly smaller left planum temporale volume than controls (20.0%) and patients with mania (20.0%). Heschl gyrus gray matter volume (left and right) was also reduced in patients with schizophrenia compared with controls (13.1%) and patients with bipolar mania (16.8%). CONCLUSIONS: Compared with controls and patients with bipolar manic psychosis, patients with first-episode schizophrenia showed left planum temporale gray matter volume reduction and bilateral Heschl gyrus gray matter volume reduction. These findings are similar to those reported in patients with chronic schizophrenia and suggest that such abnormalities are present at first episode and are specific to schizophrenia.
A three-dimensional optical flow method to measure volumetric brain deformation from sequential intraoperative MR images and preliminary clinical results from five cases are reported. Intraoperative MR images were scanned before and after dura opening, twice during tumor resection, and immediately after dura closure. The maximum cortical surface shift measured was 11 mm and subsurface shift was 4 mm. The computed deformation field was most satisfactory when the skin was segmented and removed from the images before the optical flow computation.
A novel method for resampling and enhancing image data using multidimensional adaptive filters is presented. The underlying issue that this paper addresses is segmentation of image structures that are close in size to the voxel geometry. Adaptive filtering is used to reduce both the effects of partial volume averaging by resampling the data to a lattice with higher sample density and to reduce the image noise level. Resampling is achieved by constructing filter sets that have subpixel offsets relative to the original sampling lattice. The filters are also frequency corrected for ansisotropic voxel dimensions. The shift and the voxel dimensions are described by an affine transform and provides a model for tuning the filter frequency functions. The method has been evaluated on CT data where the voxels are in general non cubic. The in-plane resolution in CT image volumes is often higher by a factor of 3-10 than the through-plane resolution. The method clearly shows an improvement over conventional resampling techniques such as cubic spline interpolation and sinc interpolation.
OBJECTIVE: To investigate the relationship between white matter abnormalities and impairment of gait and balance in older persons. METHODS: Quantitative MRI was used to evaluate the brain tissue compartments of 28 older individuals separated into normal and impaired groups on the basis of mobility performance testing using the Short Physical Performance Battery. In addition, individuals were tested on six indices of gait and balance. For imaging data, segmentation of intracranial volume into four tissue classes was performed using template-driven segmentation, in which signal-intensity-based statistical tissue classification is refined using a digital brain atlas as anatomic template. RESULTS: Both decreased white matter volume, which was age-related, and increased white matter signal abnormalities, which were not age-related, were observed in the mobility-impaired group compared with the control subjects. The average volume of white matter signal abnormalities for impaired individuals was nearly double that of control subjects. CONCLUSIONS: This cross-sectional study suggests that decreased white matter volume is age-related, whereas increased white matter signal abnormalities are most likely to occur as a result of disease. Both of these changes are independently associated with impaired mobility in older persons and therefore likely to be additive factors of motor disability.
The goal of the Image Guided Therapy Program, as the name implies, is to develop the use of imaging to guide minimally invasive therapy. The program combines interventional and intraoperative magnetic resonance imaging (MRI) with high-performance computing and novel therapeutic devices. In clinical practice the multidisciplinary program provides for the investigation of a wide range of interventional and surgical procedures. The Signa SP 0.5 T superconducting MRI system (GE Medical Systems, Milwaukee, WI) has a 56-cm-wide vertical gap, allowing access to the patient and permitting the execution of interactive MRI-guided procedures. This system is integrated with an optical tracking system and utilizes flexible surface coils and MRI-compatible displays to facilitate procedures. Images are obtained with routine pulse sequences. Nearly real-time imaging, with fast gradient-recalled echo sequences, may be acquired at a rate of one image every 1.5 s with interactive image plane selection. Since 1994, more than 800 of these procedures, including various percutaneous procedures and open surgeries, have been successfully performed at Brigham and Women's Hospital (Boston, MA).
A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy from medical images. The new algorithm is a form of spatially varying statistical classification, in which an explicit anatomical template is used to moderate the segmentation obtained by statistical classification. The algorithm consists of an iterated sequence of spatially varying classification and nonlinear registration, which forms an adaptive, template moderated (ATM), spatially varying statistical classification (SVC). Classification methods and nonlinear registration methods are often complementary, both in the tasks where they succeed and in the tasks where they fail. By integrating these approaches the new algorithm avoids many of the disadvantages of each approach alone while exploiting the combination. The ATM SVC algorithm was applied to several segmentation problems, involving different image contrast mechanisms and different locations in the body. Segmentation and validation experiments were carried out for problems involving the quantification of normal anatomy (MRI of brains of neonates) and pathology of various types (MRI of patients with multiple sclerosis, MRI of patients with brain tumors, MRI of patients with damaged knee cartilage). In each case, the ATM SVC algorithm provided a better segmentation than statistical classification or elastic matching alone.
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.
Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.
Jean-Jacques Lemaire, Antonio De Salles, Guillaume Coll, Youssef El Ouadih, Rémi Chaix, Jérôme Coste, Franck Durif, Nikos Makris, and Ron Kikinis. 8/2019. “MRI Atlas of the Human Deep Brain.” Front Neurol, 10, Pp. 851.Abstract
Mastering detailed anatomy of the human deep brain in clinical neurosciences is challenging. Although numerous pioneering works have gathered a large dataset of structural and topographic information, it is still difficult to transfer this knowledge into practice, even with advanced magnetic resonance imaging techniques. Thus, classical histological atlases continue to be used to identify structures for stereotactic targeting in functional neurosurgery. Physicians mainly use these atlases as a template co-registered with the patient's brain. However, it is possible to directly identify stereotactic targets on MRI scans, enabling personalized targeting. In order to help clinicians directly identify deep brain structures relevant to present and future medical applications, we built a volumetric MRI atlas of the deep brain (MDBA) on a large scale (infra millimetric). Twelve hypothalamic, 39 subthalamic, 36 telencephalic, and 32 thalamic structures were identified, contoured, and labeled. Nineteen coronal, 18 axial, and 15 sagittal MRI plates were created. Although primarily designed for direct labeling, the anatomic space was also subdivided in twelfths of AC-PC distance, leading to proportional scaling in the coronal, axial, and sagittal planes. This extensive work is now available to clinicians and neuroscientists, offering another representation of the human deep brain ([https://hal.archives-ouvertes.fr/] [hal-02116633]). The atlas may also be used by computer scientists who are interested in deciphering the topography of this complex region.
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (US) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy US. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. To improve accuracy of registration, we use high-dimensional texture attributes instead of image intensities and propose to replace the standard difference-based attribute matching with correlation-based attribute matching. We also present a strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images. We optimize key parameters across independent MR-iUS brain tumor datasets acquired at three different institutions, with a total of 43 tumor patients and 758 corresponding landmarks to validate the registration algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, our algorithm was able to reduce landmark errors prior to registration in three data sets (5.37 ± 4.27, 4.18 ± 1.97 and 6.18 ± 3.38 mm, respectively) to a consistently low level (2.28 ± 0.71, 2.08 ± 0.37 and 2.24 ± 0.78 mm, respectively). Our algorithm is compared to 15 other algorithms that have been previously tested on MR-iUS registration and it is competitive with the state-of-the-art on multiple datasets. We show that our algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). We further characterized landmark errors according to brain regions and tumor types, a topic so far missing in the literature. We found that landmark errors were higher in high-grade than low-grade glioma patients, and higher in tumor regions than in other brain regions.
PURPOSE: In image-guided surgery for glioma removal, neurosurgeons usually plan the resection on images acquired before surgery and use them for guidance during the subsequent intervention. However, after the surgical procedure has begun, the preplanning images become unreliable due to the brain shift phenomenon, caused by modifications of anatomical structures and imprecisions in the neuronavigation system. To obtain an updated view of the resection cavity, a solution is to collect intraoperative data, which can be additionally acquired at different stages of the procedure in order to provide a better understanding of the resection. A spatial mapping between structures identified in subsequent acquisitions would be beneficial. We propose here a fully automated segmentation-based registration method to register ultrasound (US) volumes acquired at multiple stages of neurosurgery. METHODS: We chose to segment sulci and falx cerebri in US volumes, which remain visible during resection. To automatically segment these elements, first we trained a convolutional neural network on manually annotated structures in volumes acquired before the opening of the dura mater and then we applied it to segment corresponding structures in different surgical phases. Finally, the obtained masks are used to register US volumes acquired at multiple resection stages. RESULTS: Our method reduces the mean target registration error (mTRE) between volumes acquired before the opening of the dura mater and during resection from 3.49 mm (± 1.55 mm) to 1.36 mm (± 0.61 mm). Moreover, the mTRE between volumes acquired before opening the dura mater and at the end of the resection is reduced from 3.54 mm (± 1.75 mm) to 2.05 mm (± 1.12 mm). CONCLUSION: The segmented structures demonstrated to be good candidates to register US volumes acquired at different neurosurgical phases. Therefore, our solution can compensate brain shift in neurosurgical procedures involving intraoperative US data.