Gerd Heilemann

Medical University of Vienna

Austria

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Positions (1)

Gerd Heilemann

University Name

Medical University of Vienna

NaN years ago

AI in Medicine, Radiation Oncology

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Articles (20)

Corrigendum to “Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data” [Phys. Imaging Radiat. Oncol. 30 (2024) 100594](S2405631624000642)(10.1016/j.phro.2024.100594)

Article Link

Year:

2025

Dosimetry in MRgPT: Impact of magnetic fields on TLD dose response during proton irradiation

Article Link

Year:

2025

Ultra-fast, one-click radiotherapy treatment planning outside a treatment planning system

Article Link

Year:

2025

Biochemical control in intermediate- and high-risk prostate cancer after EBRT with and without brachytherapy boost

Article Link

Year:

2024

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

Article Link

Year:

2024

The role of artificial intelligence in informed patient consent for radiotherapy treatments—a case report

Article Link

Year:

2024

Automation of ePROMs in radiation oncology and its impact on patient response and bias

Article Link

Year:

2024

Erratum to “Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?” [Z Med Phys 32 (2022) 361–368, (S0939388921001124), (10.1016/j.zemedi.2021.11.006)]

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Year:

2024

Metastases-directed local therapies (MDT) beyond genuine oligometastatic disease (OMD): Indications, endpoints and the role of imaging

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Year:

2024

Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder‐decoder network

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Year:

2023

EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer

Article Link

Year:

2023

Impact of log file source and data frequency on accuracy of log file-based patient specific quality assurance

Article Link

Year:

2023

On the sensitivity of PROMs during breast radiotherapy

Article Link

Year:

2023

Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy

Article Link

Year:

2023

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

Article Link

Year:

2022

Accelerating and improving radiochromic film calibration by utilizing the dose ratio in photon and proton beams.

Article Link

Year:

2022

Neural network-assisted automated image registration for MRI-guided adaptive brachytherapy in cervical cancer.

Article Link

Year:

2022

Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?

Article Link

Year:

2022

An MR-only acquisition and artificial intelligence based image-processing protocol for photon and proton therapy using a low field MR

Article Link

Year:

2021

Technical Note: Dose prediction for radiation therapy using feature-based losses and One Cycle Learning

Article Link

Year:

2021

Collaborators (10)

Joachim Widder

Medical University of Vienna

AUSTRIA

Skylar Gay

The University of Texas MD Anderson Cancer Center

UNITED STATES

Dietmar Georg

Head, Division Medical Radiation Physics, Full Professor

Medical University of Vienna

AUSTRIA

Peter Kuess

Medical University of Vienna

AUSTRIA

Łukasz Magrowski

Narodowy Instytut Onkologii im Marii Skłodowskiej-Curie Państwowy Instytut Badawczy w Warszawie

POLAND

Kunio Nakamura

Assistant Professor

Cleveland Clinic

UNITED STATES

Carlos Cardenas

Assistant Professor

The University of Alabama

UNITED STATES

Thomas Purdie

Associate Professor

University of Toronto

CANADA

Marco Peer

TU Wien

AUSTRIA

Martin Buschmann

Evangelical Hospital Vienna

AUSTRIA

Gerd Heilemann

Research Interests

Explore related searches

Contact this professor

Positions (1)

AI in Medicine, Radiation Oncology

Articles (20)

Corrigendum to “Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data” [Phys. Imaging Radiat. Oncol. 30 (2024) 100594](S2405631624000642)(10.1016/j.phro.2024.100594)

Dosimetry in MRgPT: Impact of magnetic fields on TLD dose response during proton irradiation

Ultra-fast, one-click radiotherapy treatment planning outside a treatment planning system

Biochemical control in intermediate- and high-risk prostate cancer after EBRT with and without brachytherapy boost

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

The role of artificial intelligence in informed patient consent for radiotherapy treatments—a case report

Automation of ePROMs in radiation oncology and its impact on patient response and bias

Erratum to “Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?” [Z Med Phys 32 (2022) 361–368, (S0939388921001124), (10.1016/j.zemedi.2021.11.006)]

Metastases-directed local therapies (MDT) beyond genuine oligometastatic disease (OMD): Indications, endpoints and the role of imaging

Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder‐decoder network

EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer

Impact of log file source and data frequency on accuracy of log file-based patient specific quality assurance

On the sensitivity of PROMs during breast radiotherapy

Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

Accelerating and improving radiochromic film calibration by utilizing the dose ratio in photon and proton beams.

Neural network-assisted automated image registration for MRI-guided adaptive brachytherapy in cervical cancer.

Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?

An MR-only acquisition and artificial intelligence based image-processing protocol for photon and proton therapy using a low field MR

Technical Note: Dose prediction for radiation therapy using feature-based losses and One Cycle Learning

Collaborators (10)

Joachim Widder

Skylar Gay

Dietmar Georg

Peter Kuess

Łukasz Magrowski

Kunio Nakamura

Carlos Cardenas

Thomas Purdie

Marco Peer

Martin Buschmann

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