Paul F. Hemler

Grey value correlation techniques used for automatic matching of CT and MR brain and spine images

Grey value correlation is generally considered not to be applicable to matching of images of different modalities. In this paper we will demonstrate that, with a simple preprocessing step for the Computed Tomography (CT) images, grey value correlation can be used for matching of Magnetic Resonance Imaging (MRI) with CT images. Two simple schemes are presented for automated 3D matching of MRI and CT neuroradiological images. Both schemes involve grey value correlation of the images in order to determine the matching transformation. In both schemes the preprocessing consists of a simple intensity mapping of the original CT image only. It will be shown that the results are insensitive to considerable changes in the parameters that determine the intensity mapping. Whichever preprocessing step is chosen, the correlation method is robust and accurate. Results, compared with a skin marker-based matching technique, are shown for brain images. Additionally, results are shown for an entirely new application: matching of the cervical spine.

Registration error quantification of a surface‐based multimodality image fusion system

This paper presents a new reference data set and associated quantification methodology to assess the accuracy of registration of computerized tomography (CT) and magnetic-resonance (MR) images. Also described is a new semiautomatic surface-based system for registering and visualizing CT and MR images. The registration error of the system was determined using a reference data set that was obtained from a cadaver in which rigid fiducial tubes were inserted prior to imaging. Registration error was measured as the distance between an analytic expression for each fiducial tube in one image set and transformed samples of the corresponding tube obtained from the other. Registration was accomplished by first identifying surfaces of similar anatomic structures in each image set. A transformation that best registered these structures was determined using a nonlinear optimization procedure. Even though the root-mean-square (rms) distance at the registered surfaces was similar to that reported by other groups, it was found that rms distances for the tubes were significantly larger than the final rms distances between the registered surfaces. It was also found that minimizing rms distance at the surface did not minimize rms distance for the tubes.

Lymph-node segmentation using active contours

Node volume analysis is very important medically. An automatic method of segmenting the node in spiral CT x-ray images is needed to produce accurate, consistent, and efficient volume measurements. The method of active contours (snakes) is proposed here as good solution to the node segmentation problem. Optimum parameterization and search strategies for using a two-dimensional snake to find node cross-sections are described, and an energy normalization scheme which preserves important spatial variations in energy is introduced. Three-dimensional segmentation is achieved without additional operator interaction by propagating the 2D results to adjacent slices. The method gives promising segmentation results on both simulated and real node images.

Radio Frequency Ablation Registration, Segmentation, and Fusion Tool

The radio frequency ablation segmentation tool (RFAST) is a software application developed using the National Institutes of Healths medical image processing analysis and visualization (MIPAV) API for the specific purpose of assisting physicians in the planning of radio frequency ablation (RFA) procedures. The RFAST application sequentially leads the physician through the steps necessary to register, fuse, segment, visualize, and plan the RFA treatment. Three-dimensional volume visualization of the CT dataset with segmented three dimensional (3-D) surface models enables the physician to interactively position the ablation probe to simulate burns and to semimanually simulate sphere packing in an attempt to optimize probe placement. This paper describes software systems contained in RFAST to address the needs of clinicians in planning, evaluating, and simulating RFA treatments of malignant hepatic tissue

Method for Correcting Magnetic Resonance Image Distortion for Frame-Based Stereotactic Surgery, with Preliminary Results

We previously described a technique for correcting patient-specific magnetic field inhomogeneity spatial distortion in magnetic resonance images (MRI), which was not applicable to patients fitted with MRI-compatible stereotactic fiducial frames. Here we describe an improvement to the technique that permits application for these patients. Measurements with a cadaver head show that this method achieves MRI stereotactic localization accuracy of 1 mm.

A Versatile System for Multimodality Image Fusion

This paper presents a versatile system for registering and visualizing computed tomography and magnetic resonance images. The system utilizes a semi-automatic, surface-based registration strategy which has proven useful for registering a number of different anatomical structures. A triangular mesh approximates surfaces in one image set while a set of surface points is used as a surface approximation in the other set. A non-linear optimization procedure determines the transformation that minimizes the total sum-squared perpendicular distance between triangles of the mesh and surface points. This system has been used without modification to successfully register images of the brain, spine and calcaneus.

Nonlinear algorithm for task-specific tomosynthetic image reconstruction

This investigation defines and tests a simple, nonlinear, task-specific method for rapid tomosynthetic reconstruction of radiographic images designed to allow an increase in specificity at the expense of sensitivity. Representative lumpectomy specimens containing cancer from human breasts were radiographed with a digital mammographic machine. Resulting projective data were processed to yield a series of tomosynthetic slices distributed throughout the breast. Five board-certified radiologists compared tomographic displays of these tissues processed both linearly (control) and nonlinearly (test) and ranked them in terms of their perceived interpretability. In another task, a different set of nine observers estimated the relative depths of six holes bored in a solid Lucite block as perceived when observed in three dimensions as a tomosynthesized series of test and control slices. All participants preferred the nonlinearly generated tomosynthetic mammograms to those produced conventionally, with or without subsequent deblurring by means of iterative deconvolution. The result was similar (p less than 0.015) when the hole-depth experiment was performed objectively. We therefore conclude for certain tasks that are unduly compromised by tomosynthetic blurring, the nonlinear tomosynthetic reconstruction method described here may improve diagnostic performance with a negligible increase in cost or complexity.

A three dimensional guidance system for frameless stereotactic neurosurgery

A computer-based tool is being developed to assist neurosurgeons in both preoperative planning and surgery. This tool is called the Stereotaxic Planning and Surgery System (SPSS). SPSS utilizes a localization device coupled with two-dimensional and three-dimensional visualization techniques, and it provides useful information to the surgeon when locating and removing diseased tissue. With SPSS, a new surgical technique referred to as guided frameless stereotaxic surgery is also being developed.<<ETX>>

Geometric problem in medical imaging

In this paper we provide a rigorous mathematical foundation for Tuned- Aperture Computed Tomography, a generalization of standard tomosynthesis that provides a significantly more flexible diagnostic tool. We also describe how the general TACT algorithm simplifies in important special cases, and we investigate the possibility of optimizing the algorithm by reducing the number of fiducial reference points. The key theoretical problem is how to sue information within an x-ray image to discover, after the fact, what the relative positions of the x-ray source, the patient, and the x-ray detector were when the x-ray image was created.

Frameless registration of MR and CT 3D volumetric data sets

In this paper we present techniques for frameless registration of 3D Magnetic Resonance (MR) and Computed Tomography (CT) volumetric data of the head and spine. We present techniques for estimating a 3D affine or rigid transform which can be used to resample the CT (or MR) data to align with the MR (or CT) data. Our technique transforms the MR and CT data sets with spatial filters so they can be directly matched. The matching is done by a direct optimization technique using a gradient based descent approach and a coarse-to-fine control strategy over a 4D pyramid. We present results on registering the head and spine data by matching 3D edges and results on registering cranial ventricle data by matching images filtered by a Laplacian of a Gaussian.<<ETX>>