Optical diffraction tomography (ODT) is used to reconstruct refractive-index distributions from multiple measurements in the object rotating configuration (ORC) or the illumination scanning configuration (ISC). Because of its fast data acquisition and stability, ISC-based ODT has been widely used for biological imaging. ODT typically fails to reconstruct multiply-scattering samples. The previously developed iterative ODT (iODT) was for the multiply-scattering objects in ORC, and could not be directly applied to ISC. To resolve this mismatch, we developed an ISC update and numerically demonstrated its accuracy. With the same prior knowledge, iODT-ISC outperforms conventional ODT in resolving the missing-angle problem.
Optimization-based optical diffraction tomography using iODT initialization
Optical diffraction tomography (ODT) is a label-free and noninvasive technique for biological imaging. However, ODT is only applicable to weakly scattering objects. To extend ODT to the multiple-scattering regime, more advanced inversion algorithms have been developed, including optimization-based ODT (Opti-ODT) and iterative ODT (iODT). In this paper, we propose a combined strategy, namely, an iODT initialization for Opti-ODT, based on the observed complementarity of their individual advantages. This study numerically demonstrates that under this combined strategy, the reconstruction can accurately converge to a better local minimum, especially in the case of multiply scattering objects with large optical path differences.
more » « less- NSF-PAR ID:
- 10248902
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Journal of the Optical Society of America A
- Volume:
- 38
- Issue:
- 7
- ISSN:
- 1084-7529; JOAOD6
- Format(s):
- Medium: X Size: Article No. 947
- Size(s):
- Article No. 947
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
more » « less
Conventional optical diffraction tomography (ODT) techniques fail in the presence of multiple scattering, and the problem becomes even more challenging when the medium is also lossy. Iterative ODT (iODT), which was shown recently to be more tolerant to multiple scattering than conventional ODT, is here augmented with an error-subtraction (ES) module. Numerical results demonstrate the accuracy and efficiency of iODT with ES for reconstructing multiply-scattering objects with complex refractive index.
-
more » « less
Optical diffraction tomography (ODT) is an indispensable tool for studying objects in three dimensions. Until now, ODT has been limited to coherent light because spatial phase information is required to solve the inverse scattering problem. We introduce a method that enables ODT to be applied to imaging incoherent contrast mechanisms such as fluorescent emission. Our strategy mimics the coherent scattering process with two spatially coherent illumination beams. The interferometric illumination pattern encodes spatial phase in temporal variations of the fluorescent emission, thereby allowing incoherent fluorescent emission to mimic the behavior of coherent illumination. The temporal variations permit recovery of the spatial distribution of fluorescent emission with an inverse scattering model. Simulations and experiments demonstrate isotropic resolution in the 3D reconstruction of a fluorescent object.
-
more » « less
Optical imaging through scattering media has long been a challenge. Many approaches have been developed for focusing light or imaging objects through scattering media, but usually, they are either invasive, limited to stationary or slow-moving media, or require high-resolution cameras and complex algorithms to retrieve the images. By utilizing spatial–temporal encoded patterns (STEPs), we introduce a technique for the computation of imaging that overcomes these restrictions. With a single-pixel photodetector, we demonstrate non-invasive imaging through scattering media. This technique is insensitive to the motion of the media. Furthermore, we demonstrate that our image reconstruction algorithm is much more efficient than correlation-based algorithms for single-pixel imaging, which may allow fast imaging for applications with limited computing resources.
-
more » « less
Abstract Continuous seismograms contain a wealth of information with a large variety of signals with different origin. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of signals in continuous single‐station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an independent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two‐day‐long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics. At a low level in the cluster hierarchy, we obtain three clusters related to anthropogenic and ambient seismic noise and one cluster related to earthquake activity. At a high level in the cluster hierarchy, we identify a seismic burst that includes around 200 events with similar waveforms and high‐frequent signals with correlating envelopes and an anthropogenic origin. The application shows that the cluster hierarchy helps to identify particular families of signals and to extract subclusters for further analysis. This is valuable when certain types of signals, such as earthquakes, are under‐represented in the data. The proposed method may also successfully discover new types of signals since it is entirely data‐driven.
