Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 228

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study

Open access
Hennersperger, Christoph, Fuerst, Bernhard, Virga, Salvatore, Zettinig, Oliver, Frisch, Benjamin, Neff, Thomas, Navab, Nassir·2017·IEEE Transactions on Medical Imaging

Abstract

Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided ultrasound acquisitions. Our approach uses a structured-light 3D scanner for patient-to-robot and image-to-patient calibration, which in turn is used to plan 3D ultrasound trajectories. These MRI-based trajectories are followed autonomously by the robot and are further refined online using automatic MRI/US registration. Despite the low spatial resolution of structured light scanners, the initial planned acquisition path can be followed with an accuracy of 2.46 ± 0.96 mm. This leads to a good initialization of the MRI/US registration: the 3D-scan-based alignment for planning and acquisition shows an accuracy (distance between planned ultrasound and MRI) of 4.47 mm, and 0.97 mm after an online-update of the calibration based on a closed loop registration.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tmi.2016.2620723

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 228

Metadata

Authors
Hennersperger, C., Fuerst, B., Virga, S., Zettinig, O., Frisch, B., Neff, T., & Navab, N.
Venue
IEEE Transactions on Medical Imaging
Published
2017-02
Volume
36
Issue
2
Pages
538-548
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
OpenAlex
W2503967141
Cited by
228
References
1
FWCI
8.21
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study. https://app.cochat.ai/papers/10.1109/tmi.2016.2620723
The paper
Hennersperger, C., Fuerst, B., Virga, S., Zettinig, O., Frisch, B., Neff, T., & Navab, N. (2017). Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2016.2620723

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.