Prof. Michael F. McNitt-Gray Profile

Prof. Michael F. McNitt-Gray

Professor of Radiology at Univ of California, Los Angeles

SPIE Involvement:

Author | Instructor

Publications (70)

Proceedings Article | 3 April 2024 Presentation + Paper

Predicting endobronchial valve treatment response in emphysema patients using the lung fissure integrity score extracted from chest CT scans

Dallas Tada, Grace Kim, Pangyu Teng, Kalyani Vyapari, Ashley Banola, Fereidoun Abtin, Jonathan Goldin, Michael McNitt-Gray, Matthew Brown

Proceedings Volume 12927, 129270M (2024) https://doi.org/10.1117/12.3008764

KEYWORDS: Computed tomography, Lung, Emphysema, Image segmentation, Education and training, Chest, Voxels, Radio over Fiber, Deep learning, Data modeling

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Proceedings Article | 3 April 2024 Presentation + Paper

Assessing variability in non-contrast CT for the evaluation of stroke: the effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume

Spencer Welland, Grace Hyun Kim, Elham Tavakkol, Kambiz Nael, Anil Yadav, William Hsu, Michael McNitt-Gray, John Hoffman, Matthew Brown

Proceedings Volume 12927, 1292711 (2024) https://doi.org/10.1117/12.3006582

KEYWORDS: Computed tomography, CT reconstruction, Medical image reconstruction, Computer aided detection, Image restoration, Artificial intelligence, Visualization, Therapeutics, Ischemia, Neuroimaging

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Purpose: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume. Methods: The NCCT series image data of 87 patients imaged with a CT stroke protocol at our institution were reconstructed with 3 kernels (H10s-smooth, H40s-medium, H70h-sharp) and 2 slice thicknesses (1.5mm and 5mm) to create a reference condition (H40s/5mm) and 5 non-reference conditions. Each reconstruction for each patient was analyzed with the Brainomix e-Stroke software (Brainomix, Oxford, England) which yields an ASPECTS value and measure of total hypodense volume (mL). Results: An ASPECTS value was returned for 74 of 87 cases in the reference condition (13 failures). ASPECTS in non-reference conditions changed from that measured in the reference condition for 59 cases, 7 of which changed above or below the clinical threshold of 7 for 3 non-reference conditions. ANOVA tests were performed to compare the differences in protocols, Dunnett’s post-hoc tests were performed after ANOVA, and a significance level of p < 0.05 was defined. There was no significant effect of kernel (p = 0.91), a significant effect of slice thickness (p < 0.01) and no significant interaction between these factors (p = 0.91). Post-hoc tests indicated no significant difference between ASPECTS estimated in the reference and any non-reference conditions. There was a significant effect of kernel (p < 0.01) and slice thickness (p < 0.01) on hypodense volume, however there was no significant interaction between these factors (p = 0.79). Post-hoc tests indicated significantly different hypodense volume measurements for H10s/1.5mm (p = 0.03), H40s/1.5mm (p < 0.01), H70h/5mm (p < 0.01). No significant difference was found in hypodense volume measured in the H10s/5mm condition (p = 0.96). Conclusion: Automated ASPECTS and hypodense volume measurements can be significantly impacted by reconstruction kernel and slice thickness.

Proceedings Article | 3 April 2024 Presentation + Paper

Harmonizing quantitative imaging feature values in CT using image quality metrics as a basis

Morgan Daly, John Hoffman, Andrew Hernandez, Ali Uneri, Jeffrey Siewerdsen, Paul Kinahan, Nicholas Bevins, Kalpana Kanal, David Zamora, Benjamin Maloney, J. Anthony Seibert, Mark Supanich, M. Mahesh, John Boone, Michael McNitt-Gray

Proceedings Volume 12927, 129271B (2024) https://doi.org/10.1117/12.3006885

KEYWORDS: Image quality, Scanners, Computed tomography, Modulation transfer functions, CT reconstruction, Quality measurement, Imaging systems, Feature extraction, Medical image reconstruction

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SPIE Journal Paper | 3 April 2024 Open Access

MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis

Karen Drukker, Berkman Sahiner, Tingting Hu, Grace Hyun Kim, Heather Whitney, Natalie Baughan, Kyle Myers, Maryellen Giger, Michael McNitt-Gray

JMI, Vol. 11, Issue 02, 024504, (April 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.024504

KEYWORDS: Medical imaging, Decision trees, Image segmentation, Algorithm development, Statistical analysis, Diseases and disorders, Binary data, Evolutionary algorithms, COVID 19, Biological research

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SPIE Journal Paper | 16 November 2023 Open Access

Sequestration of imaging studies in MIDRC: stratified sampling to balance demographic characteristics of patients in a multi-institutional data commons

Natalie Baughan, Heather Whitney, Karen Drukker, Berkman Sahiner, Tingting Hu, Hyun J. Kim, Michael McNitt-Gray, Kyle Myers, Maryellen Giger

JMI, Vol. 10, Issue 06, 064501, (November 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.064501

KEYWORDS: Algorithm development, Medical imaging, COVID 19, Evolutionary algorithms, Histograms, Databases, Artificial intelligence, Medical research, Education and training, Data modeling

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Proceedings Article | 10 April 2023 Presentation + Paper

Sequestration methodology in practice through evaluation of joint demographic distributions of 54,185 patients in the Medical Imaging and Data Resource Center (MIDRC) data commons

Natalie Baughan, Heather Whitney, Karen Drukker, Michael McNitt-Gray, Kyle Myers, Maryellen Giger, Berkman Sahiner, Tingting Hu, Grace Hyun Kim

Proceedings Volume 12469, 1246909 (2023) https://doi.org/10.1117/12.2654247

KEYWORDS: Medical imaging, COVID 19, Algorithm development, Medical research, Artificial intelligence, Radiology, Large dataset visualization, Databases, Imaging informatics, Medical imaging applications

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Proceedings Article | 7 April 2023 Poster + Paper

Reducing dose in renal perfusion CT scans: the effects on quantitative imaging features for patients of different sizes

Morgan Daly, Gabriel Melendez-Corres, Michael McNitt-Gray, Grace Hyun J. Kim, Steven Raman, M. Wasil Wahi-Anwar

Proceedings Volume 12463, 1246321 (2023) https://doi.org/10.1117/12.2651479

KEYWORDS: Computed tomography, Perfusion imaging, Image quality, Feature extraction, Tumors, Kidney, Image analysis, Visualization, Radiomics, Clinical medicine

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Proceedings Article | 7 April 2023 Poster + Paper

An analytic, physics-based approach to scoring emphysema in lung CT patients

John Hoffman, Frédéric Noo, Michael McNitt-Gray

Proceedings Volume 12465, 124652Z (2023) https://doi.org/10.1117/12.2654477

KEYWORDS: Emphysema, Lung, Diagnostics, Data modeling, Denoising, CT reconstruction

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Proceedings Article | 7 April 2023 Presentation + Paper

3D patch-based CNN for fissure segmentation on CT images to quantitatively assess fissure integrity and evaluate emphysema patients for endobronchial valve treatment

Dallas Tada, Michael McNitt-Gray, Grace Kim, Jonathan Goldin, Matthew Brown, Pangyu Teng, Kalyani Vyapari, Ashley Banola

Proceedings Volume 12465, 124651F (2023) https://doi.org/10.1117/12.2653698

KEYWORDS: Image segmentation, Computed tomography, Lung, Voxels, Education and training, Emphysema, 3D image processing, 3D modeling, Deep learning

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Proceedings Article | 7 April 2023 Presentation

Prediction of recurrence in non-small cell lung cancer (NSCLC) using Gabor and radiomic-feature based models applied to CT image data

Alexandra Shiffer, Grace Hyun Kim, Michael McNitt-Gray

Proceedings Volume 12465, 1246509 (2023) https://doi.org/10.1117/12.2654457

KEYWORDS: Data modeling, Computed tomography, Tumor growth modeling, Lung cancer, Feature extraction, Performance modeling, Linear filtering, Image classification, Feature selection, Data archive systems

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Proceedings Article | 3 April 2023 Presentation + Paper

Assistance tools for the evaluation of machine learning algorithm performance: the decision tree-based tools developed by the Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c effort

Michael McNitt-Gray, Karen Drukker, Berkman Sahiner, Tingting Hu, Grace Hyun Kim, Heather Whitney, Natalie Baughan, Kyle Myers, Maryellen Giger

Proceedings Volume 12467, 124670E (2023) https://doi.org/10.1117/12.2654250

KEYWORDS: Decision trees, Algorithm development, Image segmentation, Medical imaging, Machine learning, Binary data, Statistical analysis, Analytical research

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Proceedings Article | 18 October 2022 Open Access

Reproducibility in dual energy CT: the impact of a projection domain material decomposition method

V. Haase, M. McNitt-Gray, F. Noo, K. Stierstorfer, A. Maier

Proceedings Volume 12304, 123040H (2022) https://doi.org/10.1117/12.2646523

KEYWORDS: Signal attenuation, Dual energy imaging, Computed tomography, Scanners, Data modeling, Medicine, Image analysis, Data processing, X-rays

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Proceedings Article | 18 October 2022 Open Access

Local linearity analysis of deep learning CT denoising algorithms

Junyuan Li, Wenying Wang, Matthew Tivnan, Jeremias Sulam, Jerry Prince, Michael McNitt-Gray, J. Webster Stayman, Grace Gang

Proceedings Volume 12304, 123040T (2022) https://doi.org/10.1117/12.2646371

KEYWORDS: Neural networks, Denoising, Computed tomography, Complex systems, Algorithm development, Medical imaging, Visualization, Spherical lenses, Space operations, Signal to noise ratio

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The rapid development of deep-learning methods in medical imaging has called for an analysis method suitable for non-linear and data-dependent algorithms. In this work, we investigate a local linearity analysis where a complex neural network can be represented as piecewise linear systems. We recognize that a large number of neural networks consists of alternating linear layers and rectified linear unit (ReLU) activations, and are therefore strictly piecewise linear. We investigated the extent of these locally linear regions by gradually adding perturbations to an operating point. For this work, we explored perturbations based on image features of interest, including lesion contrast, background, and additive noise. We then developed strategies to extend these strictly locally linear regions to include neighboring linear regions with similar gradients. Using these approximately linear regions, we applied singular value decomposition (SVD) analysis to each local linear system to investigate and explain the overall nonlinear and data-dependent behaviors of neural networks. The analysis was applied to an example CT denoising algorithm trained on thorax CT scans. We observed that the strictly local linear regions are highly sensitive to small signal perturbations. Over a range of lesion contrast from 0.007 to 0.04 mm⁻¹, there is a total of 33992 linear regions. The Jacobians are also shift-variant. However, the Jacobians of neighboring linear regions are very similar. By combining linear regions with similar Jacobians, we narrowed down the number of approximately linear regions to four over lesion contrast from 0.001 to 0.08 mm⁻¹. The SVD analysis to different linear regions revealed denoising behavior that is highly dependent on the background intensity. Analysis further identified greater amount of noise reduction in uniform regions compared to lesion edges. In summary, the local linearity analysis framework we proposed has the potential for us to better characterize and interpret the non-linear and data-dependent behaviors of neural networks.

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Proceedings Article | 4 April 2022 Presentation + Paper

Towards enabling better evaluation of emphysema: investigation into a method to reduce the variability of lung CT attenuation values due to patient size and positioning

Viktor Haase, Frédéric Noo, Karl Stierstorfer, Michael McNitt-Gray

Proceedings Volume 12031, 1203115 (2022) https://doi.org/10.1117/12.2612527

KEYWORDS: Lung, Emphysema, Signal attenuation, Chronic obstructive pulmonary disease, Scanners, Computed tomography, X-ray computed tomography, Medicine

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Proceedings Article | 4 April 2022 Presentation + Paper

Sequestration of imaging studies in MIDRC: a multi-institutional data commons

Heather Whitney, Karen Drukker, Berkman Sahiner, Kim Grace Hyun J., Michael McNitt-Gray, Maryellen Giger, Tingting Hu, Kyle Myers, Natalie Baughan

Proceedings Volume 12035, 120350F (2022) https://doi.org/10.1117/12.2610239

KEYWORDS: Medical imaging, Databases, Algorithm development, Data centers, Artificial intelligence, Medical research, Clinical trials, Radiology

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Proceedings Article | 16 February 2021 Presentation + Paper

A novel physics-based data augmentation approach for improved robust deep learning in medical imaging: lung nodule CAD false positive reduction in low-dose CT environments

M. W. Wahi-Anwar, N. Emaminejad, Y. Choi, H. Kim, W. Hsu, M. Brown, M. McNitt-Gray

Proceedings Volume 11595, 115950G (2021) https://doi.org/10.1117/12.2582126

KEYWORDS: Medical imaging, Lung, Computer aided diagnosis and therapy, Imaging systems, Range imaging, Lung cancer, Data acquisition

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Proceedings Article | 15 March 2019 Presentation + Paper

Iterative reconstruction for low dose CT using Plug-and-Play alternating direction method of multipliers (ADMM) framework

Qihui Lyu, Dan Ruan, John Hoffman, Ryan Neph, Michael McNitt-Gray, Ke Sheng

Proceedings Volume 10949, 1094906 (2019) https://doi.org/10.1117/12.2512484

KEYWORDS: Image processing, Reconstruction algorithms, Computed tomography, Image resolution, Denoising, Model-based design, Image restoration, Image filtering, Data acquisition

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Proceedings Article | 2 March 2018 Paper

The effects of variations in parameters and algorithm choices on calculated radiomics feature values: initial investigations and comparisons to feature variability across CT image acquisition conditions

Nastaran Emaminejad, Grace Kim, Matthew Brown, Michael McNitt-Gray, John Hoffman, Muhammad Wahi-Anwar

Proceedings Volume 10575, 105753W (2018) https://doi.org/10.1117/12.2293864

KEYWORDS: Computed tomography, Reconstruction algorithms, Image acquisition, Lung, Feature extraction, Lung cancer, Denoising, Cancer, Tumors, Computer aided diagnosis and therapy

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Proceedings Article | 27 February 2018 Presentation + Paper

Towards quantitative imaging: stability of fully automated nodule segmentation across varied dose levels and reconstruction parameters in a low-dose CT screening patient cohort

M. Wasil Wahi-Anwar, Nastaran Emaminejad, John Hoffman, Grace Kim, Matthew Brown, Michael McNitt-Gray

Proceedings Volume 10575, 105751L (2018) https://doi.org/10.1117/12.2293269

KEYWORDS: Image segmentation, Lung cancer, Computed tomography, Lung, Feature extraction, Computer aided diagnosis and therapy, Cancer, Image quality, Quantitative analysis

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Proceedings Article | 27 March 2017 Paper

The effects of slice thickness and radiation dose level variations on computer-aided diagnosis (CAD) nodule detection performance in pediatric chest CT scans

Nastaran Emaminejad, Pechin Lo, Shahnaz Ghahremani, Grace Kim, Matthew Brown, Michael McNitt-Gray

Proceedings Volume 10134, 101340B (2017) https://doi.org/10.1117/12.2255000

KEYWORDS: Computer aided diagnosis and therapy, Lung, Computed tomography, Radiation effects, Data modeling, Chest, CAD systems, Oncology, Solids, Solid modeling, Computer aided design, Image quality, Diagnostics

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Proceedings Article | 24 March 2016 Paper

Effects of CT dose and nodule characteristics on lung-nodule detectability in a cohort of 90 national lung screening trial patients

Stefano Young, Pechin Lo, John Hoffman, H. J. Grace Kim, Matthew Brown, Michael McNitt-Gray

Proceedings Volume 9785, 97850G (2016) https://doi.org/10.1117/12.2217405

KEYWORDS: Lung, Computer aided diagnosis and therapy, Computer aided design, Computed tomography, Lung cancer, Reconstruction algorithms, Databases, Scanners, Medicine, Image segmentation, Solids

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Proceedings Article | 24 March 2016 Paper

Assessing nodule detection on lung cancer screening CT: the effects of tube current modulation and model observer selection on detectability maps

J. Hoffman, S. Young, F. Noo, K. McMillan, M. McNitt-Gray

Proceedings Volume 9787, 97870Q (2016) https://doi.org/10.1117/12.2217348

KEYWORDS: Lung, Lung cancer, Modulation, Computed tomography, Sensors, Signal attenuation, Data modeling

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Proceedings Article | 19 March 2014 Paper

Estimating lesion volume in low-dose chest CT: How low can we go?

Stefano Young, Michael McNitt-Gray

Proceedings Volume 9033, 903306 (2014) https://doi.org/10.1117/12.2043730

KEYWORDS: Computed tomography, Lung, Chest, Photons, Sensors, Scanners, Data modeling, Heart, Lung cancer, Signal attenuation

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Proceedings Article | 22 March 2012 Paper

Diagnostic accuracy at several reduced radiation dose levels for CT imaging in the diagnosis of appendicitis

Di Zhang, Hyun Kim, Maryam Khatonabadi, Michael McNitt-Gray, Michael Douek, Edward Zaragoza, Maitraya Patel, Margaret Lee, Denise Andrews-Tang, Cheryce Poon, Matilda Jude, Shawn McNitt-Gray, Laura Doepke, John DeMarco, Chris Cagnon

Proceedings Volume 8313, 831347 (2012) https://doi.org/10.1117/12.911428

KEYWORDS: Diagnostics, Image quality, Computed tomography, Liver, Monte Carlo methods, Scanners, Data modeling, Medical imaging, Radiation effects, Inflammation

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Proceedings Article | 3 March 2012 Paper

The relationship between organ dose and patient size in tube current modulated adult thoracic CT scans

Maryam Khatonabadi, Di Zhang, Chris Cagnon, Jeffrey Yang, Michael McNitt-Gray, John DeMarco

Proceedings Volume 8313, 83131Q (2012) https://doi.org/10.1117/12.911382

KEYWORDS: Breast, Lung, Scanners, Computed tomography, Monte Carlo methods, Modulation, Tissues, Data modeling, X-ray computed tomography, Particles

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Proceedings Article | 25 March 2011 Paper

DICOM structured report to track patient's radiation dose to organs from abdominal CT exam

Frank Meng, Michael McNitt-Gray, Suzie El-Saden, Adam Turner, Maria Zankl, Craig Morioka

Proceedings Volume 7967, 79670X (2011) https://doi.org/10.1117/12.878277

KEYWORDS: Computed tomography, Radiology, X-ray computed tomography, Scanners, Databases, Stomach, Liver, Colon, Picture Archiving and Communication System, Bladder

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Proceedings Article | 18 March 2011 Paper

The accuracy of estimated organ doses from Monte Carlo CT simulations using cylindrical regions of interest within organs

Maryam Khatonabadi, Erin Angel, Naghmehossadat Eshghi, Jesse Sandberg, Michael McNitt-Gray, John DeMarco, Chris Cagnon, Adam Turner, Di Zhang

Proceedings Volume 7961, 79612G (2011) https://doi.org/10.1117/12.877786

KEYWORDS: Monte Carlo methods, Kidney, Liver, Spleen, Image segmentation, Modulation, X-ray computed tomography, Scanners, 3D modeling, Statistical analysis

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Proceedings Article | 4 March 2011 Paper

Evaluation of 1D, 2D and 3D nodule size estimation by radiologists for spherical and non-spherical nodules through CT thoracic phantom imaging

Binsheng Zhao, Charles Fenimore, Robert Ford, Kristin Borradaile, Z. Q. John Lu, Andrew Buckler, Nicholas Petrick, Hyun J. Grace Kim, Michael McNitt-Gray, David Clunie, Marios Gavrielides, Rongping Zeng

Proceedings Volume 7963, 79630D (2011) https://doi.org/10.1117/12.878265

KEYWORDS: Computed tomography, 3D metrology, X-ray computed tomography, Spherical lenses, Lung, Scanners, Lung cancer, Imaging systems, 3D image processing, Data acquisition

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Proceedings Article | 3 April 2008 Paper

Dependence of CT attenuation values on scanner type using in vivo measurements

Matthew Brown, Mithun Prasad, Jonathan Goldin, Alicia Meza, Hyun Kim, Fereidoun Abtin, Michael McNitt-Gray

Proceedings Volume 6913, 69131K (2008) https://doi.org/10.1117/12.772896

KEYWORDS: Computed tomography, Scanners, Signal attenuation, In vivo imaging, Lung, Manufacturing, Clinical trials, Tomography, Chest, Radiology

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Proceedings Article | 1 April 2008 Paper

Automatic segmentation of lung parenchyma based on curvature of ribs using HRCT images in scleroderma studies

F. Abtin, M. Prasad, I. da Costa, M. McNitt-Gray, M. Brown, S. Ahmad, J. Allen, J. Goldin, H. Kim

Proceedings Volume 6915, 69152K (2008) https://doi.org/10.1117/12.769503

KEYWORDS: Lung, Image segmentation, Computed tomography, Signal attenuation, Image processing, 3D modeling, 3D image processing, Medical imaging, Tissues, Image analysis

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Proceedings Article | 19 March 2008 Paper

Separation of bone from iodine- and gadolinium-based contrast agents using dual energy CT

Jonathan Goldin, Hyun Kim, Erin Angel, Matthew Brown, Graham Cole, Daniel Chong, Michael McNitt-Gray, Ana Gomez, Christoph Panknin, Lousine Boyadzhyan

Proceedings Volume 6913, 69134Q (2008) https://doi.org/10.1117/12.772948

KEYWORDS: Calcium, Iodine, Gadolinium, Bone, Dual energy imaging, Signal attenuation, X-rays, X-ray computed tomography, Computed tomography, Image analysis

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Proceedings Article | 12 March 2008 Paper

Evaluation of airway measurements in phantom parenchyma and soft tissue regions

Matthew Brown, Jonathan Goldin, Hyun Kim, Michael McNitt-Gray, Robert Ochs

Proceedings Volume 6916, 691622 (2008) https://doi.org/10.1117/12.770660

KEYWORDS: Tissues, 3D metrology, 3D image processing, Image segmentation, In vivo imaging, Signal attenuation, Analytical research, Image analysis, Lung, Medical imaging

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Proceedings Article | 12 March 2008 Paper

Feasibility of quantitative lung perfusion by 4D CT imaging by a new dynamic-scanning protocol in an animal model

Jonathan Goldin, Matt Brown, Yang Wang, Mike McNitt-Gray, Fereidoun Abtin

Proceedings Volume 6916, 69161H (2008) https://doi.org/10.1117/12.772720

KEYWORDS: Lung, Computed tomography, Animal model studies, 4D CT imaging, Functional analysis, Data modeling, Blood, Tumors, Radiology, Visualization

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Proceedings Article | 30 March 2007 Paper

The Lung Image Database Consortium (LIDC): a quality assurance model for the collection of expert-defined truth in lung-nodule-based image analysis studies

Eric Hoffman, Laurence Clarke, Charles Meyer, Ella Kazerooni, Geoffrey McLennan, Michael McNitt-Gray, Edwin van Beek, Rachael Roberts, Claudia Henschke, David Yankelevitz, Anthony Reeves, Samuel Armato, Barbara Croft, Heber MacMahon, Denise Aberle

Proceedings Volume 6514, 651429 (2007) https://doi.org/10.1117/12.713227

KEYWORDS: Lung, Computed tomography, Databases, Computer aided diagnosis and therapy, Image quality, Solid modeling, Systems modeling, Image processing, Medical imaging, CAD systems

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Proceedings Article | 30 March 2007 Paper

Forming a reference standard from LIDC data: impact of reader agreement on reported CAD performance

Hyun Kim, Christoph Panknin, Robert Ochs, Michael McNitt-Gray, Matthew Brown, Erin Angel

Proceedings Volume 6514, 65142A (2007) https://doi.org/10.1117/12.707916

KEYWORDS: Computer aided design, Computer aided diagnosis and therapy, CAD systems, Standards development, Databases, Analytical research, Lung, Medical imaging, Scanners, Imaging systems

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Proceedings Article | 30 March 2007 Paper

Differentiating solitary pulmonary nodules (SPNs) with 3D shape features

Jonathan Goldin, Michael Mcnitt-Gray, Yang Wang, Denise Aberle, Haiming Zhao, Matthew Brown

Proceedings Volume 6514, 65143D (2007) https://doi.org/10.1117/12.713686

KEYWORDS: Manganese, Statistical analysis, Diagnostics, Computed tomography, Lung, 3D displays, Feature extraction, 3D modeling, 3D metrology, Collimation

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Proceedings Article | 29 March 2007 Paper

The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation

J. Guo, D. Qing, C. Meyer, S. Armato, E. Kazerooni, G. Laderach, L. Clarke, G. McLennan, C. Piker, D. Yankelevitz, A. Reeves, P. Bland, B. Croft, E. van Beek, R. Engelmann, M. McNitt-Gray, R. Pais, J. Freymann, H. MacMahon, M. Brown, D. Aberle

Proceedings Volume 6514, 65140K (2007) https://doi.org/10.1117/12.713754

KEYWORDS: Data processing, Image processing, Databases, Lung, Medical imaging, Computed tomography, Telecommunications, Data communications, Computer aided diagnosis and therapy, Medical research

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Proceedings Article | 29 March 2007 Paper

The Lung Image Database Consortium (LIDC): pulmonary nodule measurements, the variation, and the difference between different size metrics

Eric Hoffman, Samuel Armato, Heber MacMahon, Charles Meyer, Claudia Henschke, Barbara Croft, Ella Kazerooni, Tatiyana Apanasovich, Anthony Reeves, Michael McNitt-Gray, David Yankelevitz, Edwin van Beek, Laurence Clarke, Alberto Biancardi, Geoffrey McLennan, Denise Aberle

Proceedings Volume 6514, 65140J (2007) https://doi.org/10.1117/12.713672

KEYWORDS: Databases, Lung, Computed tomography, Computer aided diagnosis and therapy, 3D image processing, Solids, Cancer, Lung cancer, Computing systems, Image processing

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Proceedings Article | 29 March 2007 Paper

Computer-aided characterization of solitary pulmonary nodules (SPNs) using structural 3D, texture, and functional dynamic contrast features

Yang Wang, Denise Aberle, Sumit Shah, Michael McNitt-Gray, Jonathan Goldin, Matthew Brown

Proceedings Volume 6514, 651413 (2007) https://doi.org/10.1117/12.713232

KEYWORDS: Computed tomography, 3D image processing, Diagnostics, Tumors, Statistical analysis, Lung, Natural surfaces, Manganese, Signal attenuation, Feature extraction

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Proceedings Article | 15 March 2007 Paper

Radiation dose from MDCT using Monte Carlo simulations: estimating fetal dose due to pulmonary embolism scans accounting for overscan

M. Goodsitt, C. Cagnon, D. Cody, M. Ghatali, C. Wellnitz, E. Angel, D. Stevens, M. McNitt-Gray, J. DeMarco, A. Primak, C. McCollough

Proceedings Volume 6510, 65102C (2007) https://doi.org/10.1117/12.713820

KEYWORDS: Lung, Monte Carlo methods, Scanners, X-rays, Mathematical modeling, X-ray computed tomography, Data modeling, Collimation, Optical simulations, Fetus

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Proceedings Article | 3 March 2007 Paper

Multiscale shape features for classification of bronchovascular anatomy in CT using AdaBoost

Fereidoun Abtin, Hyun Kim, Matthew Brown, Michael McNitt-Gray, Poonam Batra, Jonathan Goldin, Robert Ochs, Kathleen Brown, Donald Roback

Proceedings Volume 6512, 65120T (2007) https://doi.org/10.1117/12.707655

KEYWORDS: Lung, Signal attenuation, Image segmentation, Image classification, Computed tomography, Machine learning, Databases, Chest, Algorithm development, Detection and tracking algorithms

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Proceedings Article | 17 March 2006 Paper

The influence of CT dose and reconstruction parameters on automated detection of small pulmonary nodules

Christoph Panknin, Michael McNitt-Gray, Jonathan Goldin, Iva Petkovska, Matthew Brown, Kirsten Boedeker, Denise Aberle, Robert Ochs, Erin Angel

Proceedings Volume 6144, 61445W (2006) https://doi.org/10.1117/12.658663

KEYWORDS: CAD systems, Computer aided diagnosis and therapy, Computer aided design, Prototyping, Data acquisition, Signal to noise ratio, Glasses, Computed tomography, Lung, Databases

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Proceedings Article | 29 April 2005 Paper

Solitary pulmonary nodule characterization on CT by use of contrast enhancement maps

Robert Suh, Sumit Shah, Iva Petkovska, Denise Aberle, Kheshini DeZoysa, Michael McNitt-Gray, Jonathan Goldin, Hyun Jun Kim

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595874

KEYWORDS: Image enhancement, Medical imaging, Image processing, Matrices, Dimension reduction, Factor analysis, Volume rendering

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Proceedings Article | 20 April 2005 Paper

Tradeoffs in noise, resolution, and dose with reconstruction filter selection in lung nodule detection in CT

Kirsten Boedeker, Michael McNitt-Gray

Proceedings Volume 5745, (2005) https://doi.org/10.1117/12.596998

KEYWORDS: Signal to noise ratio, Optical spheres, Image filtering, Modulation transfer functions, Optical filters, Electronic filtering, Interference (communication), Lung, Computer aided design, Spatial frequencies

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Proceedings Article | 20 April 2005 Paper

Estimating surface radiation dose from multidetector CT: cylindrical phantoms, anthropomorphic phantoms, and Monte Carlo simulations

D. Stevens, J. DeMarco, C. McCollough, D. Cody, J. O'Daniel, M. McNitt-Gray, C. Cagnon

Proceedings Volume 5745, (2005) https://doi.org/10.1117/12.595933

KEYWORDS: Monte Carlo methods, Field effect transistors, Scanners, X-ray computed tomography, Sensors, Head, Natural surfaces, Mathematical modeling, Computed tomography, Collimation

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Proceedings Article | 5 June 2003 Paper

Effects of collimation and reconstruction algorithm on noise using low-dose lung cancer screening CT techniques

Kirsten Boedeker, Michael McNitt-Gray, Chris Cagnon

Proceedings Volume 5030, (2003) https://doi.org/10.1117/12.480382

KEYWORDS: Reconstruction algorithms, Collimation, Lung cancer, Bone, Computed tomography, Lung, Spatial resolution, Sensors, Image filtering, Scanners

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Proceedings Article | 2 May 2003 Paper

Reconstruction algorithm effects on density mask score and volume in patients with low amounts of emphysema

Kirsten Boedeker, Dao Truong, Michael McNitt-Gray, Matthew Brown, Jonathan Goldin, David Gjertson, Sarah Rogers

Proceedings Volume 5031, (2003) https://doi.org/10.1117/12.480685

KEYWORDS: Reconstruction algorithms, Lung, Emphysema, Modulation transfer functions, Computed tomography, Spatial frequencies, Scanners, Imaging systems, Image filtering, Bone

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Proceedings Article | 3 July 2001 Paper

Patient-specific models for lung nodule detection and surveillance in CT images

Matthew Brown, Michael McNitt-Gray, Robert Suh, Denise Aberle, Jonathan Goldin

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431146

KEYWORDS: Image segmentation, Lung, Computed tomography, Fuzzy logic, Data modeling, Surveillance, Lung cancer, 3D modeling, Mathematical modeling, Image processing

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Proceedings Article | 3 July 2001 Paper

Computer-aided diagnosis of the solitary pulmonary nodule imaged on CT: 2D, 3D, and contrast enhancement features

Robert Suh, Denise Aberle, Michael McNitt-Gray, Jonathan Goldin, James Sayre, Nathaniel Wyckoff

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431075

KEYWORDS: Computed tomography, 3D metrology, Electronic filtering, Diagnostics, 3D image processing, 3D image enhancement, Biopsy, Feature selection, Lung cancer, X-ray computed tomography

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The goal of this work is to determine whether malignant solitary pulmonary nodules (SPNs) can be discriminated form benign lesions based on quantitative features derived form CT images. The goal is to reach an accurate diagnosis quickly and without the need for additional imaging or more invasive tests. CT images were obtained from 54 patients identified as having an SPN. Of these, 24 SPN patients scanned by a spiral volumetric technique before and after the injection of an intravenous contrast agent. Diagnostic truth was determined using either pathology results from biopsy or surgical resection or from radiographic follow-up. All images were acquired using a volumetric CT scan protocol of <EQ3 mm beam collimation, pitch 1, and were reconstructed <EW3 mm apart (typically 1.5mm). For patients receiving the contrast enhanced spiral CT protocol, the nodule was scanned prior to the contrast injection and at 45, 90, 180, and 300 seconds after injection. Nodule boundaries were isolated using a semi-automated contouring procedure on each image in which the nodule appeared. The contour boundaries, as well as their internal pixels, were combined to form 3D regions of interest (ROIs). These ROIs were then used to extract two dimensional (from a representative slice) and 3D (from the complete volume of interest) measures of interest. Two dimensional measure categories include: attenuation, size, texture and boundary shape. 3D categories include attenuation properties, size and surface boundary shape. Each nodule's contrast enhancement was measured using individual pre-contrast and post-contrast images acquired at the same location. Stepwise analyses were performed for each category of features and then again using the combined results form each category of features. Once features were selected, they were used as input variables to a linear discriminant and a logistic regression classifier. The performance of each was evaluated using ROC analysis. Because false negatives are much more serious than false positives, we also evaluated performance using the false positive fraction (FPF) at which the true positive fraction (TPF) goes to 1.0. When the Logistic Regression classifier was used with 2D non texture features, it achieved >90% accuracy, area under ROC of >0.96 and FPF of .28 for TPF = 1.0. When the Logistic Regression classifier was used with 3D and contrast enhancement features, it achieved 92% accuracy, an area under ROC of .969 and a FPF of .063 for TPF = 1.0. When combinations of group features were used, the performance was not as good as individual groups listed above. The best was when enhancement and sphericity were used in a linear discriminant model, which achieved 87.5% accuracy and area under ROC of .820. The combination of contrast enhancement measures with other, morphological descriptors, hold promise for accurate classification of solitary pulmonary nodules imaged on CT. These results are preliminary as they are based on small numbers of cases and may be sensitive to the results of individual cases.

Proceedings Article | 28 June 2001 Paper

Monte-Carlo-based simulation tool to model the physics and geometry of electron beam computed tomography

Chris Cagnon, Michael McNitt-Gray, John DeMarco

Proceedings Volume 4320, (2001) https://doi.org/10.1117/12.430891

KEYWORDS: Monte Carlo methods, Sensors, Photons, Scanners, X-ray computed tomography, Optical simulations, Computed tomography, Tissues, Bone, CT reconstruction

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Proceedings Article | 6 June 2000 Paper

Knowledge-based segmentation of pediatric kidneys in CT for measuring parenchymal volume

Michael McNitt-Gray, Matthew Brown, Waldo Feng, Bernard Churchill, Theodore Hall

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387679

KEYWORDS: Kidney, Image segmentation, X-ray computed tomography, Signal attenuation, 3D modeling, Liver, Spleen, X-rays, Computed tomography, Image processing

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Proceedings Article | 6 June 2000 Paper

Classification of solitary pulmonary nodules (SPNs) imaged on high-resolution CT using contrast enhancement and three-dimensional quantitative image features

Jonathan Goldin, Denise Aberle, Michael McNitt-Gray, Nathaniel Wyckoff, James Sayre, Robert Suh

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387615

KEYWORDS: Computed tomography, 3D image processing, Diagnostics, 3D image enhancement, Image enhancement, Image classification, Biopsy, Signal attenuation, Feature selection, Lung cancer

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Proceedings Article | 20 April 2000 Paper

Contrast enhancement maps for lung lesions imaged on CT

Michael McNitt-Gray, Robert Suh, Denise Aberle, Jonathan Goldin, Nathaniel Wyckoff, Matthew Brown

Proceedings Volume 3978, (2000) https://doi.org/10.1117/12.383442

KEYWORDS: Computed tomography, Diagnostics, Tumors, Lung, Lung cancer, X-ray computed tomography, Biopsy, Image contrast enhancement, Image registration, Image enhancement

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Proceedings Article | 28 May 1999 Paper

Effect of pitch, collimation, and reconstruction interval on low-contrast detectability in spiral computed tomography

Keyvan Farahani, Michael McNitt-Gray, Carolyn Kimme-Smith, Deborah Shumaker

Proceedings Volume 3659, (1999) https://doi.org/10.1117/12.349539

KEYWORDS: Collimation, Optical spheres, Computed tomography, Reconstruction algorithms, Lithium, Signal to noise ratio, Image analysis, Contrast sensitivity, Interference (communication), Image quality

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Proceedings Article | 3 July 1998 Paper

Automated assessment of split lung functon in post-lung-transplant evaluation

Matthew Brown, Denise Aberle, Michael McNitt-Gray, Katherine Martin, Jonathan Goldin, Lloyd Greaser, James Sayre

Proceedings Volume 3337, (1998) https://doi.org/10.1117/12.312573

KEYWORDS: Lung, Image segmentation, Signal attenuation, Image processing, X-rays, Chest, X-ray computed tomography, Transplantation, Electron beams, Tissues

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Proceedings Article | 26 June 1998 Paper

Object-oriented region-of-interest toolkit for workstations

Michael McNitt-Gray, Nathaniel Wyckoff, Alex Bui, Matthew Brown

Proceedings Volume 3335, (1998) https://doi.org/10.1117/12.312542

KEYWORDS: Binary data, Medical imaging, Image processing, 3D image processing, Tomography, Raster graphics, Image visualization, Visualization, Image processing algorithms and systems, Image segmentation

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Proceedings Article | 24 June 1998 Paper

Application of image analysis techniques to distinguish benign from malignant solitary pulmonary nodules imaged on CT

Denise Aberle, Nathaniel Wyckoff, James Sayre, Michael McNitt-Gray, Jonathan Goldin, Eric Hart

Proceedings Volume 3338, (1998) https://doi.org/10.1117/12.310850

KEYWORDS: Computed tomography, Calcium, Diagnostics, Signal attenuation, Feature extraction, X-ray computed tomography, Tissues, Image analysis, Fractal analysis, Feature selection

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Proceedings Article | 24 June 1998 Paper

Extensible knowledge-based architecture for segmenting CT data

Denise Aberle, Matthew Brown, Michael McNitt-Gray, Jonathan Goldin

Proceedings Volume 3338, (1998) https://doi.org/10.1117/12.310934

KEYWORDS: Image segmentation, Lung, Signal attenuation, Image processing, Tumors, X-ray computed tomography, X-rays, Brain, Chest, Skull

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Proceedings Article | 16 April 1996 Paper

Pattern classification approach to characterizing solitary pulmonary nodules imaged on high-resolution computed tomography

Denise Aberle, Eric Hart, Michael McNitt-Gray, Jonathan Goldin, Chih-Wei Yao

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237911

KEYWORDS: Image classification, Feature extraction, Calcium, Computed tomography, Signal attenuation, Fractal analysis, Neural networks, X-ray computed tomography, Diagnostics, Analytical research

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The purpose of our study was to characterize solitary pulmonary nodules (SPN) as benign or malignant based on pattern classification techniques using size, shape, density and texture features extracted from HRCT images. HRCT images of patients with a SPN are acquired, routed through a PACS and displayed on a thoracic radiology workstation. Using the original data, the SPN is semiautomatically contoured using a nodule/background threshold. The contour is used to calculate size and several shape parameters, including compactness and bending energy. Pixels within the interior of the contour are used to calculate several features including: (1) nodule density-related features, such as representative Hounsfield number and moment of inertia, and (2) texture measures based on the spatial gray level dependence matrix and fractal dimension. The true diagnosis of the SPN is established by histology from biopsy or, in the case of some benign nodules, extended follow-up. Multi-dimensional analyses of the features are then performed to determine which features can discriminate between benign and malignant nodules. When a sufficient number of cases are obtained two pattern classifiers, a linear discriminator and a neural network, are trained and tested using a select subset of features. Preliminary data from nine (9) nodule cases have been obtained and several features extracted. While the representative CT number is a reasonably good indicator, it is an inconclusive predictor of SPN diagnosis when considered by itself. Separation between benign and malignant nodules improves when other features, such as the distribution of density as measured by moment of inertia, are included in the analysis. Software has been developed and preliminary results have been obtained which show that individual features may not be sufficient to discriminate between benign and malignant nodules. However, combinations of these features may be able to discriminate between these two classes. With additional cases and more features, we will be able to perform a feature selection procedure and ultimately to train and test pattern classifiers in this discrimination task.

Proceedings Article | 11 April 1996 Paper

Tradeoffs in helical CT: the effects of collimation and pitch on dose, noise, and slice sensitivity profiles

Chris Cagnon, Tim Solberg, Michael McNitt-Gray

Proceedings Volume 2708, (1996) https://doi.org/10.1117/12.237804

KEYWORDS: Scanners, Collimation, Reconstruction algorithms, Computed tomography, X-ray computed tomography, Sensors, Image quality, Medicine, Image quality standards, Image resolution

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Proceedings Article | 8 April 1996 Paper

Respiratory-triggered electron beam CT with integrated spirometry for evaluation of dynamic airflow

Mike Welch, Michael McNitt-Gray, Denise Aberle, Jonathan Goldin, Michael Levine, Oded Szold

Proceedings Volume 2709, (1996) https://doi.org/10.1117/12.237884

KEYWORDS: Lung, Signal attenuation, Scanners, Emphysema, Spirometry, Electron beams, Computed tomography, Switches, Pulmonary function tests, Data acquisition

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Proceedings Article | 8 April 1996 Paper

Knowledge-based automated technique for measuring total lung volume from CT

Michael McNitt-Gray, Denise Aberle, Jonathan Goldin, Nicholas Mankovich, Matthew Brown

Proceedings Volume 2709, (1996) https://doi.org/10.1117/12.237898

KEYWORDS: Lung, Image segmentation, Signal attenuation, X-ray computed tomography, Chest, Computed tomography, Pulmonary function tests, X-rays, Emphysema, 3D modeling

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Proceedings Article | 11 May 1994 Paper

Pattern classification approach to segmentation of digital chest radiographs and chest CT image slices

Mahmood Razavi, Denise Aberle, James Sayre, Michael McNitt-Gray, H. Huang

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175080

KEYWORDS: Image segmentation, Chest imaging, Computed tomography, Image classification, Lung, Neural networks, Chest, Heart, Image processing, Tissues

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Proceedings Article | 14 September 1993 Paper

Pattern classification approach to segmentation of chest radiographs

James Sayre, Michael McNitt-Gray, Mahmood Razavi, H. Huang

Proceedings Volume 1898, (1993) https://doi.org/10.1117/12.154500

KEYWORDS: Image segmentation, Image classification, Neural networks, Heart, Lung, Image processing, Chest imaging, Chest, Fractal analysis, Feature selection

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Proceedings Article | 14 September 1993 Paper

Techniques for multiple-signal multiple-reader evaluations

H. Huang, James Lee, Minzhi Liu, Larry Cook, James Sayre, Brent Stewart, Michael McNitt-Gray, Glendon Cox, Samuel Dwyer

Proceedings Volume 1898, (1993) https://doi.org/10.1117/12.154552

KEYWORDS: Diagnostics, Imaging systems, Medical imaging, Image processing, Binary data, Error analysis, Systems modeling, Electronic filtering, Statistical analysis, Receivers

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Proceedings Article | 1 July 1992 Paper

Adding intelligence to PACS

Shantanu Sinha, H. Huang, Ricky Taira, Michael McNitt-Gray, Claudine Breant

Proceedings Volume 1654, (1992) https://doi.org/10.1117/12.60302

KEYWORDS: Picture Archiving and Communication System, Image retrieval, Tolerancing, Human-machine interfaces, Visualization, Tissues, Bone, Optimization (mathematics), Neuroimaging, Brain

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Proceedings Article | 1 June 1992 Paper

Computerized bone analysis of hand radiographs

Michael McNitt-Gray, Theodore Hall, H. Huang, Ewa Pietka

Proceedings Volume 1652, (1992) https://doi.org/10.1117/12.59462

KEYWORDS: Bone, Radiography, Feature extraction, Medical imaging, Image processing, Chromium, Tissues, Computing systems, Picture Archiving and Communication System, Standards development

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Proceedings Article | 1 May 1992 Paper

Automatic removal of unexposed background in digital radiographs

Sandra Eldredge, James Tagawa, Michael McNitt-Gray, H. Huang

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59523

KEYWORDS: Radiography, Collimation, Chromium, Picture Archiving and Communication System, Digital imaging, Video, Chest, Error analysis, Photons, Image processing

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Proceedings Article | 1 May 1992 Paper

Preprocessing functions for computed radiography images in a PACS environment

H. Huang, Ewa Pietka, Michael McNitt-Gray

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59488

KEYWORDS: Chromium, Picture Archiving and Communication System, Tissues, Chest, Spine, Radiography, Abdomen, Collimation, Neck, Lung

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Proceedings Article | 1 June 1991 Paper

Brightness and contrast adjustments for different tissue densities in digital chest radiographs

Michael McNitt-Gray, Mahmood Razavi, Sandra Eldredge, Ricky Taira

Proceedings Volume 1445, (1991) https://doi.org/10.1117/12.45243

KEYWORDS: Tissues, Lung, Chest, Image enhancement, Digital imaging, Image processing, Chest imaging, Video, Laser scanners, Radiography

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Showing 5 of 70 publications

Conference Committee Involvement (8)

Computer-Aided Diagnosis

18 February 2014 | San Diego, California, United States

Computer-Aided Diagnosis

12 February 2013 | Lake Buena Vista (Orlando Area), Florida, United States

Computer-Aided Diagnosis

7 February 2012 | San Diego, California, United States

Computer-Aided Diagnosis

15 February 2011 | Lake Buena Vista (Orlando), Florida, United States

Computer-Aided Diagnosis

16 February 2010 | San Diego, California, United States

Showing 5 of 8 Conference Committees

Course Instructor

SC1064: Radiation Dose in CT

Radiation dose from CT has continued to be a concern, especially to the medical imaging community. Recent high profile events (CT being identified as the largest single source of medical radiation to the US population, widely reported overexposure incidents, etc.) have sparked action by national and international groups (NIH, FDA, IAEA, and ACR) as well as legislation in California. The purpose of this course is to cover several areas of interest with respect to radiation dose from CT. This course will describe methods being used to measure and report radiation dose in CT, methods to estimate radiation dose to patients as well as scanner technologies that have been used to reduce radiation dose in CT exams.

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