Dr. John M. Hoffman Profile

Dr. John M. Hoffman

at Univ of California Los Angeles

SPIE Involvement:

Author

Publications (10)

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, Anil Yadav, John Hoffman, William Hsu, Matthew Brown, Elham Tavakkol, Kambiz Nael, Michael McNitt-Gray

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

Read Abstract +

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

Read Abstract +

Proceedings Article | 2 April 2024 Presentation + Paper

A single-file implementation of the DICOM-CT-PD raw projection data storage format

Joshua Genender, John Hoffman

Proceedings Volume 12931, 129310B (2024) https://doi.org/10.1117/12.3001672

KEYWORDS: Data storage, Computed tomography, Medical imaging, CT reconstruction

Read Abstract +

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

Read Abstract +

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

Read Abstract +

Proceedings Article | 1 March 2019 Presentation + Paper

Accelerating coordinate descent in iterative reconstruction

Scott Hsieh, John Hoffman, Frederic Noo

Proceedings Volume 10948, 1094811 (2019) https://doi.org/10.1117/12.2512615

KEYWORDS: Reconstruction algorithms, Computed tomography, Computer architecture, Diagnostics, Data modeling, Radiology, Graphics processing units

Read Abstract +

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

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

Read Abstract +

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

Read Abstract +

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, F. Noo, K. McMillan, S. Young, 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

Read Abstract +

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

Read Abstract +

Showing 5 of 10 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