Ultrasound b-mode imaging is a qualitative approach and diagnostic quality strongly depends on operators' training and experience. Quantitative approaches can provide information about tissue properties; therefore, can be used for identifying various tissue types, e.g., speed-of-sound in the tissue can be used as a biomarker for tissue malignancy, especially in breast imaging. Recent studies showed the possibility of speed-of-sound reconstruction using deep neural networks that are fully trained on simulated data. However, because of the ever-present domain shift between simulated and measured data, the stability and performance of these models in real setups are still under debate. In prior works, for training data generation, tissue structures were modeled as simplified geometrical structures which does not reflect the complexity of the real tissues. In this study, we proposed a new simulation setup for training data generation based on Tomosynthesis images. We combined our approach with the simplified geometrical model and investigated the impacts of training data diversity on the stability and robustness of an existing network architecture. We studied the sensitivity of the trained network to different simulation parameters, e.g., echogenicity, number of scatterers, noise, and geometry. We showed that the network trained with the joint set of data is more stable on out-of-domain simulated data as well as measured phantom data.

Quantitative ultrasound, e.g., speed-of-sound (SoS) in tissues, provides information about tissue properties that have diagnostic value. Recent studies showed the possibility of extracting SoS information from pulse-echo ultrasound raw data (a.k.a. RF data) using deep neural networks that are fully trained on simulated data. These methods take sensor domain data, i.e., RF data, as input and train a network in an end-to-end fashion to learn the implicit mapping between the RF data domain and SoS domain. However, such networks are prone to overfitting to simulated data which results in poor performance and instability when tested on measured data. We propose a novel method for SoS mapping employing learned representations from two linked autoencoders. We test our approach on simulated and measured data acquired from human breast mimicking phantoms. We show that SoS mapping is possible using linked autoencoders. The proposed method has a Mean Absolute Percentage Error (MAPE) of 2.39% on the simulated data. On the measured data, the predictions of the proposed method are close to the expected values with MAPE of 1.1%. Compared to an end-to-end trained network, the proposed method shows higher stability and reproducibility.

The gold-standard for breast cancer screening is xray mammography. Alongside, ultrasound scans are being used as additional source of information for patients with dense breast tissue. However, conventional ultrasound imaging is a qualitative approach and is prone to errors. Quantitative approaches can provide valuable information about tissue properties, e.g. the speed-of-sound in the tissue can be used as a biomarker for breast tissue malignancy. Recent studies showed the possibility of speed-of-sound reconstruction from ultrasound raw data using Deep Neural Networks (DNNs). In this study, we investigate the feasibility of DNN-based speed-of-sound reconstruction for automated breast ultrasound with simulated and real data. We set up a DNN for speed-of-sound reconstruction. The network is fully trained on simulated data. Simulations are based on the LightABVS transducer, a linear transducer with 192 active channels. The input of the network is raw channel data from a single plane-wave acquisition. The output of the network is a speed-of-sound map with a resolution of 0.1 mm. We achieved Mean Absolute Percentage Error of 0.39 ± 0.03% and Root-Mean-Square Error of 14.85 ± 0.52 m/s on simulated dataset and promising results on real dataset which demonstrates great potential of this method for integration in conventional ultrasound systems.

We demonstrate the use of organic photodetectors for monitoring fluorescent calcium signals as a measure of functional aspects of neural circuits, in vitro and ex vivo.

Solution‐processed bulk heterojunction based organic photodiodes are ideal candidates for large‐area light sensing. Here, spray‐coated large‐area organic photodiodes based on a novel polymer (Lisicon PV‐D4650) with dark current densities down to 34 pA cm −2 and a maximum responsivity of ≈0.44 A W −1 @660 nm under reverse bias conditions of −5 V are reported. These values are comparable to commercially available state‐of‐the‐art solid‐state photodetectors. Furthermore, cutoff frequencies of 50 kHz and a linear dynamic range exceeding eight orders of magnitude or ≥160 dB are achieved. The usage of 1,8‐diiodooctane as processing additive for spray coating vastly reduces the surface roughness of spray‐coated organic photodiodes which facilitates the preparation of large‐area light sensors and image sensors.

The solution-processed organic photodetectors underpin an emerging technology with inherent implications in the biological sensors and imaging displays. Conventional organic photodiodes, the core element of an organic photodetector, rely mainly on fullerene-based acceptors, which in combination with high and middle bandgap donors, limit the spectral photosensitivity to the visible range. Even in the case of low bandgap polymers the oscillator strength and thus the extinction coefficient are usually limited in the NIR due to the nature of molecular orbital hybridization. Instead, it is showed that pairing prototypical poly(3-hexylthiophene) (P3 HT) with rhodanine-benzothiadiazolecoupled indacenodithiophene (IDTBR), a nonfullerene electron acceptor absorbing beyond 750 nm, as the photoactive material of a simple photodiode results in a highly efficient organic photodetector with a record responsivity of 0.42 A W-1 and external quantum efficiency (EQE) of 69% in the NIR (755 nm) Nonfullerene-based photodiodes are processed on amorphous silicon active matrix backplanes to realize large area flat panel photodetector imagers able to detect objects under visible and NIR light conditions with an exceptional combination of responsivity, dynamic response and image crosstalk.

In this work, we present and support the positive influence of 1,8-diiodooctane (DIO) as processing additive for the fabrication of solution processed near infrared (NIR) organic photodiodes (OPDs) based on the spray-coated blend of [6,6] phenyl-C70-butyric acid methyl ester (PC70BM) and poly[(2,5-bis(2-hexyldecyl)-2,3,5,6-tetrahydro-3,6-diox-opyrrolo[3,4-c] pyrrole-1,4-diyl)-alt-([2,2':5', 2''- terthioph-ene]-5,5''- diyl)] (PDPP3T). Transmission electron microscopy (TEM) analysis show that the use of DIO improves donor- acceptor interfaces in the bulk heterojunction (BHJ) layer. Besides the enhancement of electron/hole transport pathways that leads to higher efficiencies, it reduces significantly the roughness of spray coated layers. The External Quantum Efficiency (EQE) is increased up to 35% at 900 nm and the dark current is decreased one order of magnitude in comparison to the reference down to 20 nA/cm(2) at -5 V. The OPD's detectivity is increased from 1.66x10(11) Jones to 3.34x10(12) Jones and the linearity response is improved. A comprehensive morphological and electro-optical characterization of the generated layers and devices is the main focus of this work.

Light induced fullerene dimerization is controlled by both the fullerene and polymer morphology of organic solar cells.

Medical X-ray imaging requires cost-effective and high-resolution flat-panel detectors for the energy range between 20 and 120 keV. Solution-processed photodetectors provide the opportunity to fabricate detectors with a large active area at low cost. Here, we present a disruptive approach that improves the resolution of such detectors by incorporating terbium-doped gadolinium oxysulfide scintillator particles into an organic photodetector matrix. The X-ray induced light emission from the scintillators is absorbed within hundreds of nanometres, which is negligible compared with the pixel size. Hence, optical crosstalk, a limiting factor in the resolution of scintillator-based X-ray detectors, is minimized. The concept is validated with a 256 × 256 pixel detector with a resolution of 4.75 lp mm −1 at a MTF = 0.2, significantly better than previous stacked scintillator-based flat-panel detectors. We achieved a resolution that proves the feasibility of solution-based detectors in medical applications. Time-resolved electrical characterization showed enhanced charge carrier mobility with increased scintillator filling, which is explained by morphological changes.

Flexible organic solar cells and modules based on P3ht:PCBM bulk-heterojunctions were fabricated and their lifetime was investigated under laboratory and outdoor conditions. In the laboratory cells were exposed to 1sun illumination at 65°C in order to accelerate the degradation. The outdoor behavior of modules was investigated at the Konarka rooftop testing setup in Lowell, MA (USA). We show that these flexible polymer solar cells have a good light stability, passing 1000h under accelerated light soaking conditions in the laboratory, and that flexible modules survived over 1 year of outdoor exposure without performance losses.