High-throughput screening of encapsulated islets using wide-field lens-free on-chip imaging

Islet microencapsulation is a promising solution to diabetes treatment, but its quality control based on manual microscopic inspection is extremely low-throughput, highly variable and laborious. This study presents a high-throughput islet-encapsulation quality screening system based on lens-free on-chip imaging with a wide field-of-view of 18.15 cm^2, which is more than 100 times larger than that of a lens-based optical microscope, enabling it to image and analyze ~8,000 microcapsules in a single frame. Custom-written image reconstruction and processing software provides the user with clinically important information, such as microcapsule count, size, intactness, and information on whether each capsule contains an islet. This high-throughput and cost-effective platform can be useful for researchers to develop better encapsulation protocols as well as perform quality control prior to transplantation

Terahertz Pulse Shaping Using Diffractive Surfaces

Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive networks merge wave-optics with deep learning to design task-specific elements to all-optically perform various tasks such as object classification and machine vision. Here, we present a diffractive network, which is used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact pulse engineering system. We experimentally demonstrate the synthesis of square pulses with different temporal-widths by manufacturing passive diffractive layers that collectively control both the spectral amplitude and the phase of an input terahertz pulse. Our results constitute the first demonstration of direct pulse shaping in terahertz spectrum, where a complex-valued spectral modulation function directly acts on terahertz frequencies. Furthermore, a Lego-like physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy.Comment: 27 pages, 6 figure

Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks

3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction. Here, we demonstrate the design of optical networks in the form of multiple diffractive layers that are trained using deep learning to transform and encode the spatial information of objects into the power spectrum of the diffracted light, which are used to perform optical classification of objects with a single-pixel spectroscopic detector. Using a time-domain spectroscopy setup with a plasmonic nanoantenna-based detector, we experimentally validated this machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also report the coupling of this spectral encoding achieved through a diffractive optical network with a shallow electronic neural network, separately trained to reconstruct the images of handwritten digits based on solely the spectral information encoded in these ten distinct wavelengths within the diffracted light. These reconstructed images demonstrate task-specific image decompression and can also be cycled back as new inputs to the same diffractive network to improve its optical object classification. This unique machine vision framework merges the power of deep learning with the spatial and spectral processing capabilities of diffractive networks, and can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with spectrally encoded classification tasks performed through diffractive optical networks.Comment: 21 pages, 5 figures, 1 tabl

Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor

Terahertz waves offer numerous advantages for the nondestructive detection of hidden objects/defects in materials, as they can penetrate through most optically-opaque materials. However, existing terahertz inspection systems are restricted in their throughput and accuracy (especially for detecting small features) due to their limited speed and resolution. Furthermore, machine vision-based continuous sensing systems that use large-pixel-count imaging are generally bottlenecked due to their digital storage, data transmission and image processing requirements. Here, we report a diffractive processor that rapidly detects hidden defects/objects within a target sample using a single-pixel spectroscopic terahertz detector, without scanning the sample or forming/processing its image. This terahertz processor consists of passive diffractive layers that are optimized using deep learning to modify the spectrum of the terahertz radiation according to the absence/presence of hidden structures or defects. After its fabrication, the resulting diffractive processor all-optically probes the structural information of the sample volume and outputs a spectrum that directly indicates the presence or absence of hidden structures, not visible from outside. As a proof-of-concept, we trained a diffractive terahertz processor to sense hidden defects (including subwavelength features) inside test samples, and evaluated its performance by analyzing the detection sensitivity as a function of the size and position of the unknown defects. We validated its feasibility using a single-pixel terahertz time-domain spectroscopy setup and 3D-printed diffractive layers, successfully detecting hidden defects using pulsed terahertz illumination. This technique will be valuable for various applications, e.g., security screening, biomedical sensing, quality control, anti-counterfeiting measures and cultural heritage protection.Comment: 23 Pages, 5 Figure

A comparison among PCNL, Miniperc and Ultraminiperc for lower calyceal stones between 1 and 2 cm: A prospective, comparative, multicenter and randomised study

Background: Conventional Percutaneous Lithotripsy (PCNL) has been an effective, successful and easy approach for especially > 1 cm sized calyceal stones however risks of complications and nephron loss are inevitable. Our aim is to compare the efficacy and safety of PCNL, MiniPerc (MP) and UltraMiniPerc (UMP) for lower calyceal stones between 1 and 2 cm with a multicenter prospective randomized study. Methods: Between January 2015 and June 2018, 132 consecutive patients with single lower calyceal stone were enrolled. Patients were randomized in three groups; A: PCNL; B: MP; C: UMP. 44 patients for the Group A, 47 for Group B and 41 for Group C. Exclusion criterias were the presence of coagulation impairments, age of < 18 or > 75, presence of infection or serious comorbidities. Patients were controlled with computerized tomography scan after 3 months. A negative CT or an asymptomatic patient with stone fragments < 3 mm size were the criteria to assess the stone-free status. Patient characteristics, stone free rates (SFR) s, complications and re-treatment rates were analyzed. Results: The mean stone size were 16.38, 16.82 and 15.23 mm respectively in Group A, B and C(p = 0.34). The overall SFR was significantly higher in Group A (86.3%) and B (82.9%) as compared to Group C (78%)(p < 0.05). The re-treatment rate was significantly higher in Group C (12.1%) and complication rates was higher in Group A (13.6%) as compared to others(p < 0.05). The hospitalization was significantly shorter in Group C compared to Group A (p = 0.04). Conclusions: PCNL and MP showed higher efficacy than UMP to obtain a better SFR. Auxiliary and re-treatment rates were higher in UMP. On the other hand for such this kind of stones PCNL had more complications. Overall evaluation favors MP as a better indication in stones 1-2 cm size

A prospective multicenter randomized comparison between Holmium Laser Enucleation of the Prostate (HoLEP) and Thulium Laser Enucleation of the Prostate (ThuLEP)

Purpose: To compare intra and perioperative parameters between HoLEP and ThuLEP in the treatment of benign prostatic hyperplasia and to evaluate clinical and functional outcomes of the two procedures with a 12-month follow-up. Methods: A prospective randomized study was performed on 236 consecutive patients who underwent ThuLEP (n = 115), or HoLEP (n = 121) in three different centers. Intra and perioperative parameters were analyzed: operative time, enucleated tissue weight, irrigation volume, blood loss, catheterization time, hospital stay and complications. Patients were evaluated preoperatively and 3 and 12 months postoperatively with the international prostate symptom score (IPSS), the quality of life (QoL) score, post-void residual volume (PVR), PSA and maximum flow rate (Qmax). Results: Preoperative variables in each study arm did not show any significant difference. Compared to HoLEP, ThuLEP showed similar operative time (63.69 vs 71.66 min, p = 0.245), enucleated tissue weight (48.84 vs 51.13 g, p = 0.321), catheterization time (1.9 vs 2.0 days, p = 0.450) and hospital stay (2.2 vs 2.8 days, p = 0.216), but resulted in less haemoglobin decrease (0.45 vs 2.77 g/dL, p = 0.005). HoLEP presented a significantly higher number of patients with postoperative acute urinary retention and stress incontinence. No significant differences were found in PSA, Qmax, PVR, IPSS and QoL score during follow-up. Conclusion: ThuLEP and HoLEP both relieved lower urinary tract symptoms equally, with high efficacy and safety. ThuLEP detemined reduced blood loss and early postoperative complications. Catheterization time, enucleated tissue, hospital stay, operative time and follow-up parameters did not show any significant difference