Prof. Tim Leiner Profile

Prof. Tim Leiner

at Mayo Clinic

SPIE Involvement:

Conference Program Committee | Author

Proceedings Article | 15 February 2021 Presentation + Paper

Dual-contrast decomposition of dual-energy CT using convolutional neural networks

Nadav Shapira, Wenchao Cao, Leening Liu, Tim Leiner, Maarten L. Smits, Peter Noël

Proceedings Volume 11595, 115951D (2021) https://doi.org/10.1117/12.2581333

KEYWORDS: Convolutional neural networks, Liver, Computed tomography, Network architectures, Magnetic resonance imaging, Iodine, Gadolinium, Feature extraction, Diagnostics, Computer simulations

Read Abstract +

Proceedings Article | 15 February 2021 Presentation + Paper

Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis

Majd Zreik, Nils Hampe, Tim Leiner, Nadih Khalili, Jelmer Wolterink, Michiel Voskuil, Max Viergever, Ivana Išgum

Proceedings Volume 11596, 115961F (2021) https://doi.org/10.1117/12.2580847

KEYWORDS: Arteries, Angiography, Computed tomography, Independent component analysis, Receivers, Neural networks, Computer aided design, Calcium

Read Abstract +

Proceedings Article | 16 March 2020 Presentation + Paper

Contrast flow velocity quantification from time-resolved CT angiography: a phantom study

Pieter Thomas Boonen, Nico Buls, Gert Van Gompel, Yannick De Brucker, Dimitri Aerden, Tim Leiner, Johan de Mey, Jef Vandemeulebroucke

Proceedings Volume 11312, 1131215 (2020) https://doi.org/10.1117/12.2549558

KEYWORDS: Blood circulation, Computed tomography, Targeting Task Performance metric, Arteries, Angiography, Velocity measurements, Statistical analysis, Scanners, Image segmentation, Radiology

Read Abstract +

Proceedings Article | 10 March 2020 Presentation + Paper

Coronary artery calcium scoring: Can we do better?

Sanne G. Van velzen, Bob de Vos, Helena Verkooijen, Tim Leiner, Max Viergever, Ivana Išgum

Proceedings Volume 11313, 113130G (2020) https://doi.org/10.1117/12.2549557

KEYWORDS: Calcium, Computed tomography, Arteries, Chest, Medical imaging, Radiology

Read Abstract +

Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) that can be quantified in CT scans showing the heart. CAC lesions are defined as lesions in the coronary arteries with image intensity above 130 HU. The use of a threshold may lead to under- or over-estimation of the amount of CAC and, hence, to incorrect cardiovascular categorization of patients. This is especially pronounced in CT scans without ECG-synchronization where lesions are more subject to cardiac motion and partial volume effects. To address this, we propose a method for quantification of CAC without a threshold. A set of 373 cardiac and 1181 chest CT scans was included to develop the method and a set of 21 scan-rescan pairs (42 scans) was included for final evaluation. Assuming that the attenuation of CAC is superimposed on the attenuation of the artery, we aimed to separate the CAC from the coronary arteries by employing a CycleGAN to generate a synthetic image without CAC from an image containing CAC and vice versa. By subtracting the synthetic image without CAC from the image with CAC, a CAC map is created. The CAC-map can subsequently be used to identify and quantify CAC. The ground truth, i.e. the true amount of CAC, can not be established, therefore, in this work the results generated by the method are compared with clinical calcium scoring in terms of reproducibility. The average relative difference between the calcium scores in scan-rescan pairs of scans was 50% with the proposed method and 86% for the conventional method. Moreover, the correlation between CAC pseudo masses in scan-rescan pairs was 0.92 with the proposed method and 0.89 with conventional calcium scoring. Our proposed method is able to identify and quantify CAC lesions in CT scans without using an intensity level thresholding. This might allow for more reproducible quantification of CAC in CT scans made without ECG synchronization, and, therefore, it might allow more accurate CVD risk prediction.

Proceedings Article | 15 March 2019 Paper

Improving myocardium segmentation in cardiac CT angiography using spectral information

Steffen Bruns, Jelmer Wolterink, Robbert van Hamersvelt, Majd Zreik, Tim Leiner, Ivana Išgum

Proceedings Volume 10949, 109492M (2019) https://doi.org/10.1117/12.2512229

KEYWORDS: Image segmentation, CT reconstruction, Scanners, X-ray computed tomography, Computed tomography, Angiography, Blood, 3D image reconstruction, 3D acquisition, Signal attenuation

Read Abstract +

Proceedings Article | 21 March 2016 Paper

Automatic detection of cardiovascular risk in CT attenuation correction maps in Rb-82 PET/CTs

Ivana Išgum, Bob de Vos, Jelmer Wolterink, Damini Dey, Daniel Berman, Mathieu Rubeaux, Tim Leiner, Piotr Slomka

Proceedings Volume 9784, 978405 (2016) https://doi.org/10.1117/12.2216992

KEYWORDS: Calcium, Algorithm development, Computed tomography, Arteries, Chest, Positron emission tomography

Read Abstract +

Proceedings Article | 18 March 2014 Paper

An automatic machine learning system for coronary calcium scoring in clinical non-contrast enhanced, ECG-triggered cardiac CT

Jelmer Wolterink, Tim Leiner, Richard A. Takx, Max Viergever, Ivana Išgum

Proceedings Volume 9035, 90350E (2014) https://doi.org/10.1117/12.2042226

KEYWORDS: Arteries, Calcium, Computed tomography, Chemical vapor deposition, Image segmentation, Machine learning, Heart, Metals, Image enhancement, Image registration

Read Abstract +

Showing 5 of 7 publications

Conference Committee Involvement (5)

Image Processing

17 February 2025 | San Diego, California, United States

Image Processing

19 February 2024 | San Diego, California, United States

Image Processing

20 February 2023 | San Diego, California, United States

Image Processing

20 February 2022 | San Diego, California, United States

Image Processing

15 February 2021 | Online Only, California, United States

Keywords/Phrases

Search In:

Publication Years