Jonghye Woo

Quantitative Analysis of Myocardial Perfusion SPECT Anatomically Guided by Coregistered 64-Slice Coronary CT Angiography

Sequential testing by coronary CT angiography (CTA) and myocardial perfusion SPECT (MPS) obtained on stand-alone scanners may be needed to diagnose coronary artery disease in equivocal cases. We have developed an automated technique for MPS–CTA registration and demonstrate its utility for improved MPS quantification by guiding the coregistered physiologic (MPS) with anatomic CTA information. Methods: Automated registration of MPS left ventricular (LV) surfaces with CTA coronary trees was accomplished by iterative minimization of voxel differences between presegmented CTA volumes and motion-frozen MPS data. Studies of 35 sequential patients (26 men; mean age, 67 ± 12 y) with 64-slice coronary CTA, MPS, and available results of the invasive coronary angiography performed within 3 mo were retrospectively analyzed. Three-dimensional coronary vessels and CTA slices were extracted and fused with quantitative MPS results mapped on LV surfaces and MPS coronary regions. Automatically coregistered CTA images and extracted trees were used to correct the MPS contours and to adjust the standard vascular region definitions for MPS quantification. Results: Automated coregistration of MPS and coronary CTA had the success rate of 96% as assessed visually; the average errors were 4.3 ± 3.3 mm in translation and 1.5 ± 2.6 degrees in rotation on stress and 4.2 ± 3.1 mm in translation and 1.7 ± 3.2 degrees in rotation on rest. MPS vascular region definition was adjusted in 17 studies, and LV contours were adjusted in 11 studies using coregistered CTA images as a guide. CTA-guided myocardial perfusion analysis, compared with standard MPS analysis, resulted in improved area under the receiver-operating-characteristic (ROC) curves for the detection of right coronary artery (RCA) and left circumflex artery (LCX) lesions (0.84 ± 0.08 vs. 0.70 ± 0.11 for LCX, P = 0.03, and 0.92 ± 0.05 vs. 0.75 ± 0.09 for RCA, P = 0.02). Conclusion: Software image coregistration of stand-alone coronary CTA and MPS obtained on separate scanners can be performed rapidly and automatically, allowing CTA-guided contour and vascular territory adjustment on MPS for improved quantitative MPS analysis.

Heart chambers and whole heart segmentation techniques: review

Computer-aided segmentation of cardiac images obtained by various modalities plays an important role and is a prerequisite for a wide range of cardiac applications by facilitating the delineation of anatomical regions of interest. Numerous computerized methods have been developed to tackle this problem. Recent studies employ sophisticated techniques using available cues from cardiac anatomy such as geometry, visual appearance, and prior knowledge. In addition, new minimization and computational methods have been adopted with improved computational speed and robustness. We provide an overview of cardiac segmentation techniques, with a goal of providing useful advice and references. In addition, we describe important clinical applications, imaging modalities, and validation methods used for cardiac segmentation.

Reconstruction of High-Resolution Tongue Volumes From MRI

Magnetic resonance images of the tongue have been used in both clinical studies and scientific research to reveal tongue structure. In order to extract different features of the tongue and its relation to the vocal tract, it is beneficial to acquire three orthogonal image volumes-e.g., axial, sagittal, and coronal volumes. In order to maintain both low noise and high visual detail and minimize the blurred effect due to involuntary motion artifacts, each set of images is acquired with an in-plane resolution that is much better than the through-plane resolution. As a result, any one dataset, by itself, is not ideal for automatic volumetric analyses such as segmentation, registration, and atlas building or even for visualization when oblique slices are required. This paper presents a method of superresolution volume reconstruction of the tongue that generates an isotropic image volume using the three orthogonal image volumes. The method uses preprocessing steps that include registration and intensity matching and a data combination approach with the edge-preserving property carried out by Markov random field optimization. The performance of the proposed method was demonstrated on 15 clinical datasets, preserving anatomical details and yielding superior results when compared with different reconstruction methods as visually and quantitatively assessed.

Multimodal Registration via Mutual Information Incorporating Geometric and Spatial Context

Multimodal image registration is a class of algorithms to find correspondence from different modalities. Since different modalities do not exhibit the same characteristics, finding accurate correspondence still remains a challenge. To deal with this, mutual information (MI)-based registration has been a preferred choice as MI is based on the statistical relationship between both volumes to be registered. However, MI has some limitations. First, MI-based registration often fails when there are local intensity variations in the volumes. Second, MI only considers the statistical intensity relationships between both volumes and ignores the spatial and geometric information about the voxel. In this work, we propose to address these limitations by incorporating spatial and geometric information via a 3D Harris operator. In particular, we focus on the registration between a high-resolution image and a low-resolution image. The MI cost function is computed in the regions where there are large spatial variations such as corner or edge. In addition, the MI cost function is augmented with geometric information derived from the 3D Harris operator applied to the high-resolution image. The robustness and accuracy of the proposed method were demonstrated using experiments on synthetic and clinical data including the brain and the tongue. The proposed method provided accurate registration and yielded better performance over standard registration methods.

Automatic 3D registration of dynamic stress and rest 82Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction

PURPOSE The authors aimed to develop an image-based registration scheme to detect and correct patient motion in stress and rest cardiac positron emission tomography (PET)/CT images. The patient motion correction was of primary interest and the effects of patient motion with the use of flurpiridaz F 18 and (82)Rb were demonstrated. METHODS The authors evaluated stress/rest PET myocardial perfusion imaging datasets in 30 patients (60 datasets in total, 21 male and 9 female) using a new perfusion agent (flurpiridaz F 18) (n = 16) and (82)Rb (n = 14), acquired on a Siemens Biograph-64 scanner in list mode. Stress and rest images were reconstructed into 4 ((82)Rb) or 10 (flurpiridaz F 18) dynamic frames (60 s each) using standard reconstruction (2D attenuation weighted ordered subsets expectation maximization). Patient motion correction was achieved by an image-based registration scheme optimizing a cost function using modified normalized cross-correlation that combined global and local features. For comparison, visual scoring of motion was performed on the scale of 0 to 2 (no motion, moderate motion, and large motion) by two experienced observers. RESULTS The proposed registration technique had a 93% success rate in removing left ventricular motion, as visually assessed. The maximum detected motion extent for stress and rest were 5.2 mm and 4.9 mm for flurpiridaz F 18 perfusion and 3.0 mm and 4.3 mm for (82)Rb perfusion studies, respectively. Motion extent (maximum frame-to-frame displacement) obtained for stress and rest were (2.2 ± 1.1, 1.4 ± 0.7, 1.9 ± 1.3) mm and (2.0 ± 1.1, 1.2 ±0 .9, 1.9 ± 0.9) mm for flurpiridaz F 18 perfusion studies and (1.9 ± 0.7, 0.7 ± 0.6, 1.3 ± 0.6) mm and (2.0 ± 0.9, 0.6 ± 0.4, 1.2 ± 1.2) mm for (82)Rb perfusion studies, respectively. A visually detectable patient motion threshold was established to be ≥2.2 mm, corresponding to visual user scores of 1 and 2. After motion correction, the average increases in contrast-to-noise ratio (CNR) from all frames for larger than the motion threshold were 16.2% in stress flurpiridaz F 18 and 12.2% in rest flurpiridaz F 18 studies. The average increases in CNR were 4.6% in stress (82)Rb studies and 4.3% in rest (82)Rb studies. CONCLUSIONS Fully automatic motion correction of dynamic PET frames can be performed accurately, potentially allowing improved image quantification of cardiac PET data.

Segmentation of tongue muscles from super-resolution magnetic resonance images

Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and swallowing problems.

Non-Rigid Ultrasound Image Registration Based on Intensity and Local Phase Information

A non-rigid ultrasound image registration method is proposed in this work using the intensity as well as the local phase information under a variational framework. One application of this technique is to register two consecutive images in an ultrasound image sequence. Although intensity is the most widely used feature in traditional ultrasound image registration algorithms, speckle noise and lower image resolution make the registration process difficult. By integrating the intensity and the local phase information, we can find and track the non-rigid transformation of each pixel under diffeomorphism between the source and target images. Experiments using synthetic and cardiac images of in vivo mice and human subjects are conducted to demonstrate the advantages of the proposed method.

Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI

Dynamic MRI has been widely used to track the motion of the tongue and measure its internal deformation during speech and swallowing. Accurate segmentation of the tongue is a prerequisite step to define the target boundary and constrain the tracking to tissue points within the tongue. Segmentation of 2D slices or 3D volumes is challenging because of the large number of slices and time frames involved in the segmentation, as well as the incorporation of numerous local deformations that occur throughout the tongue during motion. In this paper, we propose a semi-automatic approach to segment 3D dynamic MRI of the tongue. The algorithm steps include seeding a few slices at one time frame, propagating seeds to the same slices at different time frames using deformable registration, and random walker segmentation based on these seed positions. This method was validated on the tongue of five normal subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of a total of 130 volumes showed an average dice similarity coefficient (DSC) score of 0.92 with less segmented volume variability between time frames than in manual segmentations.

Geometric feature-based multimodal image registration of contrast-enhanced cardiac CT with gated myocardial perfusion SPECT.

PURPOSE Cardiac computed tomography (CT) and single photon emission computed tomography (SPECT) provide clinically complementary information in the diagnosis of coronary artery disease (CAD). Fused anatomical and physiological data acquired sequentially on separate scanners can be coregistered to accurately diagnose CAD in specific coronary vessels. METHODS A fully automated registration method is presented utilizing geometric features from a reliable segmentation of gated myocardial perfusion SPECT (MPS) volumes, where regions of myocardium and blood pools are extracted and used as an anatomical mask to de-emphasize the inhomogeneities of intensity distribution caused by perfusion defects and physiological variations. A multiresolution approach is employed to represent coarse-to-fine details of both volumes. The extracted voxels from each level are aligned using a similarity measure with a piecewise constant image model and minimized using a gradient descent method. The authors then perform limited nonlinear registration of gated MPS to adjust for phase differences by automatic cardiac phase matching between CT and MPS. For phase matching, they incorporate nonlinear registration using thin-plate-spline-based warping. Rigid registration has been compared with manual alignment (n=45) on 20 stress/rest MPS and coronary CTA data sets acquired from two different sites and five stress CT perfusion data sets. Phase matching was also compared to expert visual assessment. RESULTS As compared with manual alignment obtained from two expert observers, the mean and standard deviation of absolute registration errors of the proposed method for MPS were4.3±3.5, 3.6±2.6, and 3.6±2.1mm for translation and 2.1±3.2°, 0.3±0.8°, and 0.7±1.2° for rotation at site A and 3.8±2.7, 4.0±2.9, and 2.2±1.8mm for translation and 1.1±2.0°, 1.6±3.1°, and 1.9±3.8° for rotation at site B. The results for CT perfusion were 3.0±2.9, 3.5±2.4, and 2.8±1.0mm for translation and 3.0±2.4°, 0.6±0.9°, and 1.2±1.3° for rotation. The registration error shows that the proposed method achieves registration accuracy of less than 1 voxel (6.4×6.4×6.4mm) misalignment. The proposed method was robust for different initializations in the range from -80 to 70, -80 to 70, and -50to50mm in the x-, y-, and z-axes, respectively. Validation results of finding best matching phase showed that best matching phases were not different by more than two phases, as visually determined. CONCLUSIONS The authors have developed a fast and fully automated method for registration of contrast cardiac CT with gated MPS which includes nonlinear cardiac phase matching and is capable of registering these modalities with accuracy<10mm in 87% of the cases.

Investigating contactless high frequency ultrasound microbeam stimulation for determination of invasion potential of breast cancer cells.

In this article, we investigate the application of contactless high frequency ultrasound microbeam stimulation (HFUMS) for determining the invasion potential of breast cancer cells. In breast cancer patients, the finding of tumor metastasis significantly worsens the clinical prognosis. Thus, early determination of the potential of a tumor for invasion and metastasis would significantly impact decisions about aggressiveness of cancer treatment. Recent work suggests that invasive breast cancer cells (MDA‐MB‐231), but not weakly invasive breast cancer cells (MCF‐7, SKBR3, and BT‐474), display a number of neuronal characteristics, including expression of voltage‐gated sodium channels. Since sodium channels are often co‐expressed with calcium channels, this prompted us to test whether single‐cell stimulation by a highly focused ultrasound microbeam would trigger Ca2+ elevation, especially in highly invasive breast cancer cells. To calibrate the diameter of the microbeam ultrasound produced by a 200‐MHz single element LiNbO3 transducer, we focused the beam on a wire target and performed a pulse‐echo test. The width of the beam was ∼17 µm, appropriate for single cell stimulation. Membrane‐permeant fluorescent Ca2+ indicators were utilized to monitor Ca2+ changes in the cells due to HFUMS. The cell response index (CRI), which is a composite parameter reflecting both Ca2+ elevation and the fraction of responding cells elicited by HFUMS, was much greater in highly invasive breast cancer cells than in the weakly invasive breast cancer cells. The CRI of MDA‐MB‐231 cells depended on peak‐to‐peak amplitude of the voltage driving the transducer. These results suggest that HFUMS may serve as a novel tool to determine the invasion potential of breast cancer cells, and with further refinement may offer a rapid test for invasiveness of tumor biopsies in situ. Biotechnol. Bioeng. 2013;110: 2697–2705.