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.

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.

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.

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).

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.

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.

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.