A Caltech Library Service

Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields

Liu, Renhao and Sun, Yu and Zhu, Jiabei and Tian, Lei and Kamilov, Ulugbek S. (2022) Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields. Nature Machine Intelligence, 4 (9). pp. 781-791. ISSN 2522-5839. doi:10.1038/s42256-022-00530-3.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


Intensity diffraction tomography (IDT) refers to a class of optical microscopy techniques for imaging the three-dimensional refractive index (RI) distribution of a sample from a set of two-dimensional intensity-only measurements. The reconstruction of artefact-free RI maps is a fundamental challenge in IDT due to the loss of phase information and the missing-cone problem. Neural fields has recently emerged as a new deep learning approach for learning continuous representations of physical fields. The technique uses a coordinate-based neural network to represent the field by mapping the spatial coordinates to the corresponding physical quantities, in our case the complex-valued refractive index values. We present Deep Continuous Artefact-free RI Field (DeCAF) as a neural-fields-based IDT method that can learn a high-quality continuous representation of a RI volume from its intensity-only and limited-angle measurements. The representation in DeCAF is learned directly from the measurements of the test sample by using the IDT forward model without any ground-truth RI maps. We qualitatively and quantitatively evaluate DeCAF on the simulated and experimental biological samples. Our results show that DeCAF can generate high-contrast and artefact-free RI maps and lead to an up to 2.1-fold reduction in the mean squared error over existing methods.

Item Type:Article
Related URLs:
URLURL TypeDescription ReadCube access
Liu, Renhao0000-0002-9081-0693
Sun, Yu0000-0001-7225-9677
Tian, Lei0000-0002-1316-4456
Kamilov, Ulugbek S.0000-0001-6770-3278
Additional Information:This work was supported by the NSF award nos. CCF-1813910 and CCF-2043134 (to U.K.), and CCF-1813848 and EPMD-1846784 (to L.T.).
Funding AgencyGrant Number
Issue or Number:9
Record Number:CaltechAUTHORS:20220923-942198900.18
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117132
Deposited By: Melissa Ray
Deposited On:04 Oct 2022 17:21
Last Modified:04 Oct 2022 17:21

Repository Staff Only: item control page