David Allen Langan
General Electric
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by David Allen Langan.
Proceedings of SPIE | 2009
Xiaoye Wu; David Allen Langan; Dan Xu; Thomas M. Benson; Jed Douglas Pack; Andrea Schmitz; Eric J. Tkaczyk; Jaynne Leverentz; Paul Licato
In a conventional X-ray CT system, where an object is scanned with a selected incident x-ray spectrum, or kVp, the reconstructed images only approximate the linear X-ray attenuation coefficients of the imaged object at an effective energy of the incident X-ray beam. The errors are primarily the result of beam hardening due to the polychromatic nature of the X-ray spectrum. Modem clinical CT scanners can reduce this error by a process commonly referred to as spectral calibration. Spectral calibration linearizes the measured projection value to the thickness of water. However, beam hardening from bone and contrast agents can still induce shading and streaking artifacts and cause CT number inaccuracies in the image. In this paper, we present a dual kVp scanning method, where during the scan, the kVp is alternately switching between target low and high preset values, typically 80kVp and 140 kVp, with a period less than 1ms. The measured projection pairs are decomposed into the density integrals of two basis materials in projection space. The reconstructed density images are further processed to obtain monochromatic attenuation coefficients of the object at any desired energy. Energy levels yielding optimized monochromatic images are explored, and their analytical representations are derived.
Proceedings of SPIE | 2009
Dan Xu; David Allen Langan; Xiaoye Wu; Jed Douglas Pack; Thomas M. Benson; J. Eric Tkaczky; Andrea Schmitz
Recently there has been significant interest in dual energy CT imaging with several acquisition methods being actively pursued. Here we investigate fast kVp switching where the kVp alternates between low and high kVp every view. Fast kVp switching enables fine temporal registration, helical and axial acquisitions, and full field of view. It also presents several processing challenges. The rise and fall of the kVp, which occurs during the view integration period, is not instantaneous and complicates the measurement of the effective spectrum for low and high kVp views. Further, if the detector digital acquisition system (DAS) and generator clocks are not fully synchronous, jitter is introduced in the kVp waveform relative to the view period. In this paper we develop a method for estimation of the resulting spectrum for low and high kVp views. The method utilizes static kVp acquisitions of air with a small bowtie filter as a basis set. A fast kVp acquisition of air with a small bowtie filter is performed and the effective kVp is estimated as a linear combination of the basis vectors. The effectiveness of this method is demonstrated through the reconstruction of a water phantom acquired with a fast kVp acquisition. The impact of jitter due to the generator and detector DAS clocks is explored via simulation. The error is measured relative to spectrum variation and material decomposition accuracy.
Archive | 2011
Naveen Chandra; David Allen Langan
This chapter provides an overview of the GE Discovery CT750HD dual energy imaging capability known as gemstone spectral imaging (GSI). The CT750HD is a single X-ray source system that employs fast kVp switching for dual energy acquisitions. This acquisition method enables precise temporal registration of the dual-energy sinograms, projection-based material decomposition, and delivers a full 50 cm material decomposition scan field of view. The subsystem technologies employed to achieve the dual energy acquisitions are detailed in the discussion of system design. Calibration of fast kVp switching data, material decomposition, and visualization of the resulting images are covered in the image reconstruction and post processing sections. The chapter closes with GSI implementation in the context of challenging diagnostic applications.
OE/LASE '90, 14-19 Jan., Los Angeles, CA | 1990
David So Keung Chan; David Allen Langan; Daniel Arthur Staver
The detection of small targets in infrared (IR) clutter is a problem of critical importance to Infrared Search and Track (IRST) systems. This paper presents techniques for analyzing and improving the detection performance of IRST systems. Only spatial, or single-frame, processing will be addressed. For clutter with spatially slowly varying statistics, the approach is based on linear filtering. Models of target and cluttter are developed and used to analyze matched filter performance and sensitivity. This sensitivity analysis is used to improve filter bank design. A clutter classification scheme which can separate clutter of different types is presented. Finally, to improve system performance in the presence of large intensity gradients, such as cloud edges, an improved adaptive threshold scheme is presented.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
international conference on image processing | 1994
David Allen Langan; James W. Modestino; Jun Zhang
Image segmentation is an important processing stage in many image analysis problems. Often this must be done in an unsupervised fashion in that training data is not available. A major obstacle in such applications is the determination of the number of distinct regions present in an image. This problem, called the cluster validation problem, remains essentially unsolved. We investigate the cluster validation problem associated with the use of a previously developed unsupervised segmentation algorithm based upon the expectation-maximization (EM) algorithm. We consider several well-known information-theoretic criteria (ICs) as candidate solutions. We show that these criteria generally provide inappropriate results. As an alternative we propose a model-fitting technique in which the complete data log-likelihood functional is modeled as an exponential function in the number of classes acting, and the class estimate is related to the rise time. This new validation technique is shown to be robust and outperform the ICs in our experiments.<<ETX>>
international conference on acoustics, speech, and signal processing | 1992
David Allen Langan; Karl J. Molnar; James W. Modestino; Jun Zhang
The application of a Markov random field (MRF) state model in an expectation-maximization (EM)-based approach to unsupervised image segmentation is investigated. In the calculation of the marginal distribution of the state field, it is shown that the use of the expected state values for interacting pixel sites in the computation of the MRF energy function may be interpreted as a mean-field approximation. The implications of calculating a self-consistent expectation of the state field are considered. EM convergence criteria are considered, and a criterion based upon divergence is proposed. Experimental results based on synthetic data illustrate the performance advantage of the mean-field approximation and the computational advantage of using self-consistent expectations.<<ETX>>
Proceedings of SPIE | 2010
Mukta C. Joshi; David Allen Langan; D. S. Sahani; A. Kambadakone; S. Aluri; K. Procknow; Xiaoye Wu; Rahul Bhotika; Darin Okerlund; Naveen M. Kulkarni; Dan Xu
The clinical application of Gemstone Spectral ImagingTM, a fast kV switching dual energy acquisition, is explored in the context of noninvasive kidney stone characterization. Utilizing projection-based material decomposition, effective atomic number and monochromatic images are generated for kidney stone characterization. Analytical and experimental measurements are reported and contrasted. Phantoms were constructed using stone specimens extracted from patients. This allowed for imaging of the different stone types under similar conditions. The stone specimens comprised of Uric Acid, Cystine, Struvite and Calcium-based compositions. Collectively, these stone types span an effective atomic number range of approximately 7 to 14. While Uric Acid and Calcium based stones are generally distinguishable in conventional CT, stone compositions like Cystine and Struvite are difficult to distinguish resulting in treatment uncertainty. Experimental phantom measurements, made under increasingly complex imaging conditions, illustrate the impact of various factors on measurement accuracy. Preliminary clinical studies are reported.
Proceedings of SPIE | 2010
Alberto Santamaria-Pang; Sandeep Dutta; Sokratis Makrogiannis; Amy K. Hara; William Pavlicek; Alvin C. Silva; Brian Thomsen; Scott Robertson; Darin Okerlund; David Allen Langan; Rahul Bhotika
Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions due to similar composition as well as other factors including beam hardening and patient motion. This problem is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions, multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning method for discriminating between cysts and hypodense liver metastases using these monochromatic images. Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion. We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating benign liver cysts and metastases, especially for small lesions.
Proceedings of SPIE | 2009
J. Eric Tkaczyk; David Allen Langan; Xiaoye Wu; Daniel Xu; Thomas M. Benson; Jed Douglas Pack; Andrea Schmitz; Amy K. Hara; William Palicek; Paul Licato; Jaynne Leverentz
Linear discriminate analysis (LDA) is applied to dual kVp CT and used for tissue characterization. The potential to quantitatively model both malignant and benign, hypo-intense liver lesions is evaluated by analysis of portal-phase, intravenous CT scan data obtained on human patients. Masses with an a priori classification are mapped to a distribution of points in basis material space. The degree of localization of tissue types in the material basis space is related to both quantum noise and real compositional differences. The density maps are analyzed with LDA and studied with system simulations to differentiate these factors. The discriminant analysis is formulated so as to incorporate the known statistical properties of the data. Effective kVp separation and mAs relates to precision of tissue localization. Bias in the material position is related to the degree of X-ray scatter and partial-volume effect. Experimental data and simulations demonstrate that for single energy (HU) imaging or image-based decomposition pixel values of water-like tissues depend on proximity to other iodine-filled bodies. Beam-hardening errors cause a shift in image value on the scale of that difference sought between in cancerous and cystic lessons. In contrast, projection-based decomposition or its equivalent when implemented on a carefully calibrated system can provide accurate data. On such a system, LDA may provide novel quantitative capabilities for tissue characterization in dual energy CT.
medical image computing and computer assisted intervention | 2005
James C. Ross; David Allen Langan; Ravindra Mohan Manjeshwar; John Patrick Kaufhold; Joseph John Manak; David L. Wilson
We investigated a method, motion compensated integration (MCI), for enhancing stent Contrast-to-Noise Ratio (CNR) such that stent deployment may be more easily assessed. MCI registers fluoroscopic frames on the basis of stent motion and performs pixel-wise integration to reduce noise. Registration is based on marker balls, high contrast interventional devices which guide the clinician in stent placement. It is assumed that stent motion is identical to that of the marker balls. Detecting marker balls and identifying their centroids with a high degree of accuracy is a non-trivial task. To address the required registration accuracy, in this work we examine MCIs visualization benefit as a function of registration error. We employ adaptive forced choice experiments to quantify human discrimination fidelity. Perception results are contrasted with CNR measurements. For each level of registration inaccuracy investigated, MCI conferred a benefit (p < 0.05) on stent deployment assessment suggesting the technique is tolerant of modest registration error. We also consider the blurring effect of cardiac motion during the x-ray pulse and select frames for integration as a function of cardiac phase imaged.