Dr. Dante P. I. Capaldi Profile

Dr. Dante P. I. Capaldi

at Univ. of California, San Francisco

SPIE Involvement:

Author

Publications (7)

Proceedings Article | 15 March 2019 Paper

Fourier decomposition free-breathing 1H MRI perfusion maps in asthma

Alexander Matheson, Dante P. Capaldi, Fumin Guo, Rachel Eddy, David McCormack, Grace Parraga

Proceedings Volume 10949, 1094912 (2019) https://doi.org/10.1117/12.2512436

KEYWORDS: Magnetic resonance imaging, Lung, Image segmentation, Tissues, Algorithm development, Visualization, Helium, Biophysics, Medicine, Image registration

Read Abstract +

Proceedings Article | 15 March 2019 Presentation + Paper

Texture analysis of thoracic CT to predict hyperpolarized gas MRI lung function

Andrew Westcott, Dante P. Capaldi, David McCormack, Aaron Fenster, Grace Parraga

Proceedings Volume 10953, 109530H (2019) https://doi.org/10.1117/12.2512851

KEYWORDS: Magnetic resonance imaging, Computed tomography, X-ray computed tomography, Chronic obstructive pulmonary disease, Lung, Image segmentation, Image registration, Machine learning, Feature extraction, Medicine

Read Abstract +

Proceedings Article | 15 March 2019 Presentation + Paper

Development and evaluation of pulmonary imaging multi-parametric response maps for deep phenotyping of chronic obstructive pulmonary disease

Jonathan MacNeil, Dante P. Capaldi, Rachel Eddy, Andrew Westcott, Alexander Matheson, Andrea Barker, Cathy Ong-Ly, David McCormack, Grace Parraga

Proceedings Volume 10953, 109530J (2019) https://doi.org/10.1117/12.2512849

KEYWORDS: Image segmentation, Chronic obstructive pulmonary disease, Magnetic resonance imaging, Image registration, Gold, Lung, Computed tomography, X-ray computed tomography, Image processing algorithms and systems, Emphysema

Read Abstract +

Objective: Our aim was to develop and evaluate multi-parametric response maps derived from pulmonary x-ray computed tomography (CT), ¹H and hyperpolarized ³He static ventilation and diffusion-weighted magnetic resonance imaging (MRI). These maps were generated to phenotype patients with chronic obstructive pulmonary disease (COPD) based on the presence of airways disease, air trapping, emphysema, alveolar distension, and ventilation defects. Methods: To generate thoracic imaging multi-parametric response maps (mPRM), multispectral ¹H, ³He and CT images were segmented and co-registered. ¹H and ³He MR images were segmented using a semi-automated segmentation algorithm, the diffusion weighted MR images were segmented using a threshold-based algorithm and CT images were segmented using Pulmonary Workstation 2.0 (VIDA Diagnostics, Coralville, IA). The volume-matched segmented ¹H/³He maps were registered using landmark rigid registration. The ³He maps/the diffusion weighted images were registered using an intensity-based rigid registration. CT-to-MRI co-registration was achieved using modality-independent neighborhood descriptor (MIND) deformable registration; inspiratory and expiratory CT were co-registered using an affine registration with a deformable step provided by the NiftyReg toolkit. The co-registered thoracic maps were used to generate multiparametric maps. Results: mPRM maps were generated for six different voxel classifications with increasing disease abnormality/severity as follows: 1) ventilated voxels with >-856HU/>-950HU and normal apparent diffusion coefficient (ADC) values, 2) ventilated voxels with <-856HU/<-950HU and abnormal ADC values, 3) ventilated voxels with >-856HU/>-950HU and normal ADC values, 4) ventilated voxels with <-856HU/<-950HU and abnormal ADC values, 5) unventilated voxels with >-856HU/>-950HU, and, 6) unventilated voxels with <-856HU/<-950HU. Conclusion: mPRM measurements were automated in a dedicated pipeline for MRI and CT measurements to phenotype COPD patients.

SPIE Journal Paper | 28 June 2018

Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease

Fumin Guo, Dante Capaldi, Miranda Kirby, Khadija Sheikh, Sarah Svenningsen, David McCormack, Aaron Fenster, Grace Parraga

JMI, Vol. 5, Issue 02, 026002, (June 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.026002

KEYWORDS: Magnetic resonance imaging, Lung, Image segmentation, Computed tomography, Software development, Point-of-care devices, Visualization, Algorithm development, Xenon, Image analysis

Read Abstract +

Proceedings Article | 13 March 2017 Paper

Registration pipeline for pulmonary free-breathing 1H MRI ventilation measurements

Fumin Guo, Dante P. Capaldi, Robert Di Cesare, Aaron Fenster, Grace Parraga

Proceedings Volume 10137, 101370A (2017) https://doi.org/10.1117/12.2253879

KEYWORDS: Magnetic resonance imaging, Lung, Image segmentation, Noble gases, Image registration, Chronic obstructive pulmonary disease, Convex optimization, Pulmonary function tests, Image processing, Image acquisition

Read Abstract +

Objectives: Our aim was to develop a clinically-practical and physiologically-relevant approach for regional structure-function measurements of the lung using Fourier decomposition of free-breathing pulmonary magnetic resonance imaging (FDMRI). Methods: Ten patients with chronic obstructive pulmonary disease provided written informed consent to a study protocol approved by Health Canada and completed pulmonary function tests, 1H/hyperpolarized noble gas and free-breathing pulmonary magnetic resonance imaging (MRI) during a single 2-hour visit. Free-breathing ¹H MRI was simultaneously segmented using a multi-region coupled continuous max-flow approach by exploring primal/dual analysis and convex optimization techniques. The segmented free-breathing 1H MRI lung was registered using deformable registration approach that was developed using dual and convex optimization methods to compensate for respiratory/cardiac motion. Fourier decomposition of the co-registered lung was used to generate pulmonary functional information that was quantified as ventilation-defect-percent (VDP). The pipeline was implemented on a GPU for speed-up. Lung segmentation accuracy was measured by comparing algorithm and manual lung masks using Dice-similarity-coefficient (DSC). FD-VDP was compared to ³He-VDP using Pearson correlation coefficient and Bland-Altman analysis. The reproducibility of our algorithm was measured using coefficient of variation (CoV) and intraclass correlation coefficient (ICC) for DSC and FD-VDP. Results: The pipeline yielded a whole lung DSC of 95.7±1.7% and FD-VDP that were correlated with ³He-VDP (r = 0.81, p = 0.004). CoV (ICC) were 0.4% (0.98) and 4.1% (0.98) for whole lung DSC and FD-VDP, respectively. The proposed approach requires ~45 min for parallel implementation with minimal user interaction. Conclusion: The proposed approach provides a clinically-practical pipeline to generate regional pulmonary structure-function measurements using free-breathing pulmonary ¹HMRI with promising potential for widespread clinical translation.

Proceedings Article | 17 March 2015 Paper

Fourier-based linear systems description of free-breathing pulmonary magnetic resonance imaging

D. Capaldi, S. Svenningsen, I. Cunningham, G. Parraga

Proceedings Volume 9417, 94171A (2015) https://doi.org/10.1117/12.2081503

KEYWORDS: Signal to noise ratio, Magnetic resonance imaging, Imaging systems, Systems modeling, Chronic obstructive pulmonary disease, Image registration, Image quality, Fourier transforms, Lung, Pulmonary function tests

Read Abstract +

Proceedings Article | 17 March 2015 Paper

Principal component analysis of the CT density histogram to generate parametric response maps of COPD

Nanxi Zha, Dante P. Capaldi, Damien Pike, David McCormack, Ian Cunningham, Grace Parraga

Proceedings Volume 9417, 941716 (2015) https://doi.org/10.1117/12.2076396

KEYWORDS: Chronic obstructive pulmonary disease, Principal component analysis, Magnetic resonance imaging, Emphysema, X-ray computed tomography, Lung, Gold, Signal attenuation, Diffusion, Medicine

Read Abstract +

Showing 5 of 7 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years