Monitoring the 3-D cell culture process or drug responses non-destructively using Electrical Impedance Tomography (EIT) is an emerging topic in biomedical imaging. Significant efforts have been spent on developing EIT image reconstruction algorithms in order to achieve robust and high-quality cell imaging. The considerable computation time and imperfect image quality are the main issues of these conventional methods whereas the emergence of deep learning techniques point out a new direction due to its fast inferences on object detection, image segmentation and classification. In this paper, a novel deep learning architecture is proposed by adding a fully connected layer before a U-Net structure. This new architecture will first generate an initial guess of the conductivity distribution and then feed it to the following denoising model. A novel initialization strategy is also proposed to further help obtain this initial guess. The performance of the method is verified by simulation and experimental data. The results show that the proposed model outperforms the state-of-the-art EIT algorithms and can generalize well to reconstruct unseen cases consisting of human breast cancer cell pellet.

Cells need to rapidly and precisely react to multiple mechanical and chemical stimuli in order to ensure precise context-dependent responses. This requires dynamic cellular signalling events that ensure homeostasis and plasticity when needed. A less wellunderstood process is cellular response to elevated interstitial fluid pressure, where the cell senses and responds to changes in extracellular hydrostatic pressure. Here, using quantitative label-free digital holographic imaging, combined with genome editing, biochemical assays and confocal imaging, we analyse the temporal cellular response to hydrostatic pressure. Upon elevated cyclic hydrostatic pressure, the cell responds by rapid, dramatic and reversible changes in cellular volume. We show that YAP and TAZ, the co-transcriptional regulators of the Hippo signalling pathway, control cell volume and that cells without YAP and TAZ have lower plasma membrane tension. We present direct evidence that YAP/ TAZ drive the cellular response to hydrostatic pressure, a process that is at least partly mediated via clathrin-dependent endocytosis. Additionally, upon elevated oscillating hydrostatic pressure, YAP/TAZ are activated and induce TEAD-mediated transcription and expression of cellular components involved in dynamic regulation of cell volume and extracellular matrix. This cellular response confers a feedback loop that allows the cell to robustly respond to changes in interstitial fluid pressure.

We present a first spectral-domain optical coherence tomography (SD-OCT) system deploying a complementary metal-oxide-semiconductor (CMOS) single-photon avalanche diode (SPAD) based, time-resolved line sensor. The sensor with 1024 pixels achieves a sensitivity of 87 dB at an A-scan rate of 1 kHz using a supercontinuum laser source with a repetition rate of 20 MHz, 38 nm bandwidth, and 2 mW power at 850 nm centre wavelength. In the time-resolved mode of the sensor, the system combines low-coherence interferometry (LCI) and massively parallel time-resolved single-photon counting to control the detection of interference spectra on the single-photon level based on the time-of-arrival of photons. For proof of concept demonstration of the combined detection scheme we show the acquisition of time-resolved interference spectra and the reconstruction of OCT images from selected time bins. Then, we exemplify the temporal discrimination feature with 50 ps time resolution and 249 ps timing uncertainty by removing unwanted reflections from along the optical path at a 30 mm distance from the sample. The current limitations of the proposed technique in terms of sensor parameters are analysed and potential improvements are identified for advanced photonic applications.