74 citations · 263 across the 13 of their papers we have counts for
18 papers
Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts
Hanna Pulkkinen, Jenni Poimala, Leonid Kunyansky +2
We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited-view artifacts and noise comm…
Towards robust quantitative photoacoustic tomography via learned iterative methods
Anssi Manninen, Janek Gröhl, Felix Lucka +1
Photoacoustic tomography (PAT) is a medical imaging modality that can provide high-resolution tissue images based on the optical absorption. Classical reconstruction methods for qu…
PhotIQA: A photoacoustic image data set with image quality ratings
Anna Breger, Janek Gröhl, Clemens Karner +7
Image quality assessment (IQA) is crucial in the evaluation stage of novel algorithms operating on images, including traditional and machine learning based methods. Due to the lack…
A study on the adequacy of common IQA measures for medical images
Anna Breger, Clemens Karner, Ian Selby +11
Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images. The most commonly used IQA measures have b…
A study of why we need to reassess full reference image quality assessment with medical images
Anna Breger, Ander Biguri, Malena Sabaté Landman +11
Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the development stage of machine learning algorithms that operate on me…
Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry
Janek Gröhl, Kylie Yeung, Kevin Gu +5
Significance: Photoacoustic imaging (PAI) promises to measure spatially-resolved blood oxygen saturation, but suffers from a lack of accurate and robust spectral unmixing methods t…
Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms
Janek Gröhl, Thomas R. Else, Lina Hacker +3
Accurate measurement of optical absorption coefficients from photoacoustic imaging (PAI) data would enable direct mapping of molecular concentrations, providing vital clinical insi…
Unsupervised Domain Transfer with Conditional Invertible Neural Networks
Kris K. Dreher, Leonardo Ayala, Melanie Schellenberg +11
Synthetic medical image generation has evolved as a key technique for neural network training and validation. A core challenge, however, remains in the domain gap between simulatio…
Semantic segmentation of multispectral photoacoustic images using deep learning
Melanie Schellenberg, Kris Dreher, Niklas Holzwarth +7
Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectr…
Photoacoustic image synthesis with generative adversarial networks
Melanie Schellenberg, Janek Gröhl, Kris K. Dreher +5
Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the opt…
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Jan-Hinrich Nölke, Tim Adler, Janek Gröhl +5
Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation. However, the underly…
Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern
Niklas Holzwarth, Melanie Schellenberg, Janek Gröhl +6
Purpose: Photoacoustic tomography (PAT) is a novel imaging technique that can spatially resolve both morphological and functional tissue properties, such as the vessel topology and…
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
Lena Maier-Hein, Martin Wagner, Tobias Ross +30
Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracki…
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
Tim J. Adler, Lynton Ardizzone, Anant Vemuri +8
Purpose: Optical imaging is evolving as a key technique for advanced sensing in the operating room. Recent research has shown that machine learning algorithms can be used to addres…
Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)
Janek Gröhl, Thomas Kirchner, Tim Adler +1
One of the main applications of photoacoustic (PA) imaging is the recovery of functional tissue properties, such as blood oxygenation (sO2). This is typically achieved by linear sp…
An open-source software platform for translational photoacoustic research and its application to motion-corrected blood oxygenation estimation
Thomas Kirchner, Janek Gröhl, Franz Sattler +4
Photoacoustic (PA) imaging systems based on clinical linear ultrasound arrays have become increasingly popular in translational PA research. Such systems can be more easily integra…
Photoacoustics can image spreading depolarization deep in gyrencephalic brain
Thomas Kirchner, Janek Gröhl, Mildred Herrera +4
Spreading depolarization (SD) is a self-propagating wave of near-complete neuronal depolarization that is abundant in a wide range of neurological conditions, including stroke. SD…
Context encoding enables machine learning-based quantitative photoacoustics
Thomas Kirchner, Janek Gröhl, Lena Maier-Hein
Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and intervent…