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Automated Ultrasound Scanning on a Dual-Modality Breast Imaging System : Coverage and Motion Issues and Solutions

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      Mutual information for automated unwarping of rat brain autoradiographs.

      An automated multimodal warping based on mutual information metric (MI) as a mapping cost function is demonstrated. Mutual information (I) is calculated from a two-dimensional (2D) gray scale histogram of an image pair, and MI (= -I) provides a matching cost function which can be effective in registration of two- or three-dimensional data sets independent of modality. Most histological image data, though information rich and high resolution, present nonlinear deformations due to the specimen sectioning and need reconstitution into deformation-corrected volumes prior to geometric mapping to an anatomical volume for spatial analyses. Section alignment via automatic 2D registrations employing MI as a global cost function and thin-plate-spline (TPS) warping is applied to deoxy-D-[14C]glucose autoradiographic image slices of a rat brain with video reference images of the uncut block face to reconstitute a cerebral glucose metabolic volume data. Unlike the traditional feature-based TPS warping algorithms, initial control points are defined independently from feature landmarks. Registration quality using automated multimodal image warping is validated by comparing MIs of the image pair registered by automated affine registration and manual warping method. The MI proves to be a robust objective matching cost function effective for automatic multimodality warping for 2D data sets and can be readily applied to volume registrations.
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        3D spatial compounding of ultrasound images using image-based nonrigid registration.

        Medical ultrasound images are often distorted enough to significantly limit resolution during compounding (i.e., summation of images from multiple views). A new, volumetric image registration technique has been used successfully to enable high spatial resolution in three-dimensional (3D) spatial compounding of ultrasound images. Volumetric ultrasound data were acquired by scanning a linear matrix array probe in the elevational direction in a focal lesion phantom and in a breast in vivo. To obtain partly uncorrelated views, the volume of interest was scanned at five different transducer tilt angles separated by 4 degrees to 6 degrees. Pairs of separate views were registered by an automatic procedure based on a mutual information metric, using global full affine and thin-plate spline warping transformations. Registration accuracy was analyzed automatically in the phantom data, and manually in vivo, yielding average registration errors of 0.31 mm and 0.65 mm, respectively. In the vicinity of the warping control points, registrations obtained with warping transformations were significantly more accurate than full affine registrations. Compounded images displayed the expected reduction in speckle noise and increase in contrast-to-noise ratio (CNR), as well as better delineation of connective tissues and reduced shadowing. Compounding also revealed some apparent low contrast lobulations that were not visible in the single-sweep images. Given expected algorithmic and hardware enhancements, nonrigid, image-based registration shows great promise for reducing tissue motion and refraction artifacts in 3D spatial compounding.
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          Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy.

          To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard. Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25-86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating data were analyzed with the receiver operating characteristic (ROC) curve. Without CAD, the five radiologists had an average area under the ROC curve (A(z)) of 0.83 (range, 0.81-0.87). With CAD, the average A(z) increased significantly (P = .006) to 0.90 (range, 0.86-0.93). When a 2% likelihood of malignancy was used as the threshold for biopsy recommendation, the average sensitivity of radiologists increased from 96% to 98% with CAD, while the average specificity for this data set decreased from 22% to 19%. If a biopsy recommendation threshold could be chosen such that sensitivity would be maintained at 96%, specificity would increase to 45% with CAD. Use of a computer algorithm may improve radiologists' accuracy in distinguishing malignant from benign breast masses on 3D US volumetric images. (c) RSNA, 2007.
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            Author and article information

            Journal
            Journal of Ultrasound in Medicine
            Wiley
            02784297
            May 2007
            May 2007
            May 01 2007
            : 26
            : 5
            : 645-655
            10.7863/jum.2007.26.5.645
            © 2007

            http://doi.wiley.com/10.1002/tdm_license_1

            http://onlinelibrary.wiley.com/termsAndConditions

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