Marco Riboldi

About Marco Riboldi

Marco Riboldi, With an exceptional h-index of 33 and a recent h-index of 24 (since 2020), a distinguished researcher at Ludwig-Maximilians-Universität München, specializes in the field of medical physics, image guidance, radiotherapy, particle therapy.

His recent articles reflect a diverse array of research interests and contributions to the field:

Simultaneous object detection and segmentation for patient‐specific markerless lung tumor tracking in simulated radiographs with deep learning

Intra‐frame motion deterioration effects and deep‐learning‐based compensation in MR‐guided radiotherapy

Reduction of cone‐beam CT artifacts in a robotic CBCT device using saddle trajectories with integrated infrared tracking

Comparison of deep learning networks for fully automated head and neck tumor delineation on multi-centric PET/CT images

Continuous time-resolved estimated synthetic 4D-CTs for dose reconstruction of lung tumor treatments at a 0.35 T MR-linac

Investigating the benefit of scattering in 2D-3D rigid registration using a single proton radiography

Experimental comparison of linear regression and LSTM motion prediction models for MLC‐tracking on an MRI‐linac

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects

Marco Riboldi Information

University

Position

___

Citations(all)

3825

Citations(since 2020)

2061

Cited By

2691

hIndex(all)

33

hIndex(since 2020)

24

i10Index(all)

97

i10Index(since 2020)

62

Email

University Profile Page

Google Scholar

Marco Riboldi Skills & Research Interests

medical physics

image guidance

radiotherapy

particle therapy

Top articles of Marco Riboldi

Simultaneous object detection and segmentation for patient‐specific markerless lung tumor tracking in simulated radiographs with deep learning

Medical Physics

2024/3

Intra‐frame motion deterioration effects and deep‐learning‐based compensation in MR‐guided radiotherapy

Medical Physics

2024/3

Reduction of cone‐beam CT artifacts in a robotic CBCT device using saddle trajectories with integrated infrared tracking

Medical Physics

2024/1/15

Comparison of deep learning networks for fully automated head and neck tumor delineation on multi-centric PET/CT images

Radiation Oncology

2024/1/8

Continuous time-resolved estimated synthetic 4D-CTs for dose reconstruction of lung tumor treatments at a 0.35 T MR-linac

Physics in Medicine & Biology

2023/11/28

Investigating the benefit of scattering in 2D-3D rigid registration using a single proton radiography

2023/11/4

Experimental comparison of linear regression and LSTM motion prediction models for MLC‐tracking on an MRI‐linac

Medical Physics

2023/11

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects

2023/10/26

Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors

Radiation Oncology

2023/8/14

Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients

Physics in Medicine & Biology

2023/7/5

Multi-stage image registration based on list-mode proton radiographies for small animal proton irradiation: A simulation study

Zeitschrift für Medizinische Physik

2023/6/21

Assessment of intrafractional prostate motion and its dosimetric impact in MRI-guided online adaptive radiotherapy with gating

Strahlentherapie und Onkologie

2023/6

On the robustness of multilateration of ionoacoustic signals for localization of the Bragg peak at pre-clinical proton beam energies in water

Physics in Medicine & Biology

2023/5/8

PO-1901 Comparing LSTM networks for real-time target segmentation prediction on low-field MR-linacs

Radiotherapy and Oncology

2023/5/1

MO-0227 Validation of 4DMRI-based virtual 4DCT and application in gated particle therapy of lung cancer

Radiotherapy and Oncology

2023/5/1

PO-1889 Feasibility of markerless lung tumor tracking on radiographs with Retina U-Net

Radiotherapy and Oncology

2023/5/1

Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy

Radiotherapy and Oncology

2023/5/1

Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT Images

Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

2023/3/17

Marco Riboldi
Marco Riboldi

H-Index: 23

Impact of secondary particles on the magnetic field generated by a proton pencil beam: a finite‐element analysis based on Geant4‐DNA simulations

Medical Physics

2023/2

OC-0460 Deep learning based time to event analysis with PET, CT and joint PET/CT for H&N cancer prognosis

Radiotherapy and Oncology

2022/5/1

See List of Professors in Marco Riboldi University(Ludwig-Maximilians-Universität München)