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Dynamical machine learning volumetric reconstruction of objects’ interiors from limited angular views

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Light(Science & Applications) 2021年第5期10卷 824-844页

作者： Iksung Kang Alexandre Goy george barbastathis Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology77 Massachusetts AveCambridgeMAUSA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology(SMART)Centre 1 Create WaySingapore 117543Singapore Omnisens SA Morges 1110Switzerland

Limited-angle tomography of an interior volume is a challenging, highly ill-posed problem with practical implications in medical and biological imaging, manufacturing, automation, and environmental and food security. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. Recently, it was shown that one effective way to learn the priors for strongly scattering yet highly structured 3D objects, e.g. layered and Manhattan, is by a static neural network [Goy et al. Proc. Natl. Acad. Sci. 116, 19848–19856 (2019)]. Here, we present a radically different approach where the collection of raw images from multiple angles is viewed analogously to a dynamical system driven by the object-dependent forward scattering operator. The sequence index in the angle of illumination plays the role of discrete time in the dynamical system analogy. Thus, the imaging problem turns into a problem of nonlinear system identification, which also suggests dynamical learning as a better fit to regularize the reconstructions. We devised a Recurrent Neural Network (RNN) architecture with a novel Separable-Convolution Gated Recurrent Unit (SC-GRU) as the fundamental building block. Through a comprehensive comparison of several quantitative metrics, we show that the dynamic method is suitable for a generic interior-volumetric reconstruction under a limited-angle scheme. We show that this approach accurately reconstructs volume interiors under two conditions: weak scattering, when the Radon transform approximation is applicable and the forward operator well defined;and strong scattering, which is nonlinear with respect to the 3D refractive index distribution and includes uncertainty in the forward operator.

关键词： interior dynamical angular

Learning to synthesize:robust phase retrieval at low photon counts

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Light(Science & Applications) 2020年第1期9卷 1657-1672页

作者： Mo Deng Shuai Li Alexandre Goy Iksung Kang george barbastathis Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA Sensebrain Technology Limited LLC 2550 N 1st StreetSuite 300San JoseCA 95131USA Omnisens SA Riond Bosson 31110 MorgesVDSwitzerland Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 117543Singapore

The quality of inverse problem solutions obtained through deep learning is limited by the nature of the priors learned from examples presented during the training phase.Particularly in the case of quantitative phase retrieval,spatial frequencies that are underrepresented in the training database,most often at the high band,tend to be suppressed in the reconstruction.Ad hoc solutions have been proposed,such as pre-amplifying the high spatial frequencies in the examples;however,while that strategy improves the resolution,it also leads to high-frequency artefacts,as well as low-frequency distortions in the reconstructions.Here,we present a new approach that learns separately how to handle the two frequency bands,low and high,and learns how to synthesize these two bands into full-band reconstructions.We show that this“learning to synthesize”(LS)method yields phase reconstructions of high spatial resolution and without artefacts and that it is resilient to high-noise conditions,e.g.,in the case of very low photon flux.In addition to the problem of quantitative phase retrieval,the LS method is applicable,in principle,to any inverse problem where the forward operator treats different frequency bands unevenly,i.e.,is ill-posed.

关键词： synthesize bands phase

Single-shot lensless imaging with fresnel zone aperture and incoherent illumination

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Light(Science & Applications) 2020年第1期9卷 1509-1519页

作者： Jiachen Wu Hua Zhang Wenhui Zhang Guofan Jin Liangcai Cao george barbastathis State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentsTsinghua University100084 BeijingChina Department of Mechanical Engineering Massachusetts Institute of Technology77 Massachusetts AvenueCambridgeMA 02139USA

Lensless imaging eliminates the need for geometric isomorphism between a scene and an image while allowing the construction of compact,lightweight imaging systems.However,a challenging inverse problem remains due to the low reconstructed signal-to-noise ratio.Current implementations require multiple masks or multiple shots to denoise the reconstruction.We propose single-shot lensless imaging with a Fresnel zone aperture and incoherent illumination.By using the Fresnel zone aperture to encode the incoherent rays in wavefront-like form,the captured pattern has the same form as the inline hologram.Since conventional backpropagation reconstruction is troubled by the twin-image problem,we show that the compressive sensing algorithm is effective in removing this twin-image artifact due to the sparsity in natural scenes.The reconstruction with a significantly improved signal-to-noise ratio from a single-shot image promotes a camera architecture that is flat and reliable in its structure and free of the need for strict calibration.

关键词： illumination Fresnel removing

Attentional Ptycho-Tomography(APT)for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

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Light(Science & Applications) 2023年第6期12卷 1127-1140页

作者： Iksung Kang Ziling Wu Yi Jiang Yudong Yao Junjing Deng Jeffrey Klug Stefan Vogt george barbastathis Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Argonne National Laboratory LemontIL 60439USA Singapore-MIT Alliance for Research and Technology(SMART)Centre 1 CREATE WaySingapore 138602Singapore Present address:Department of Molecular and Cell Biology University of CaliforniaBerkeleyCA 94720USA Present address:Singapore-MIT Alliance for Research and Technology(SMART)Centre 1 CREATE WaySingapore 138602Singapore Present address:Center for Transformative Science ShanghaiTech University201210 ShanghaiChina

Noninvasive X-ray imaging of nanoscale three-dimensional objects,such as integrated circuits(ICs),generally requires two types of scanning:ptychographic,which is translational and returns estimates of the complex electromagnetic field through the IC;combined with a tomographic scan,which collects these complex field projections from multiple angles.Here,we present Attentional Ptycho-Tomography(APT),an approach to drastically reduce the amount of angular scanning,and thus the total acquisition time.APT is machine learning-based,utilizing axial self-Attention for Ptycho-Tomographic reconstruction.APT is trained to obtain accurate reconstructions of the ICs,despite the incompleteness of the measurements.The training process includes regularizing priors in the form of typical patterns found in IC interiors,and the physics of X-ray propagation through the IC.We show that APT with×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique(SART)with the original set of angles.When using the same set of reduced angles,then APT also outperforms Filtered Back Projection(FBP),Simultaneous Iterative Reconstruction Technique(SIRT)and SART.The time needed to compute the reconstruction is also reduced,because the trained neural network is a forward operation,unlike the iterative nature of these alternatives.Our experiments show that,without loss in quality,for a 4.48×93.2×3.92µm^(3) IC(≃6×10^(8) voxels),APT reduces the total data acquisition and computation time from 67.96 h to 38 min.We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging,including materials science and biological imaging.

关键词： utilizing computation interior

Phase imaging with an untrained neural network

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Light(Science & Applications) 2020年第1期9卷 1258-1264页

作者： Fei Wang Yaoming Bian Haichao Wang Meng Lyu Giancarlo Pedrini Wolfgang Osten george barbastathis Guohai Situ Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences201800 ShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences100049 BeijingChina Institut für Technische Optik Universität StuttgartPfaffenwaldring 970569 StuttgartGermany Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139-4301USA Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences310024 HangzhouChina

Most of the neural networks proposed so far for computational imaging(CI)in optics employ a supervised training strategy,and thus need a large training set to optimize their weights and biases.Setting aside the requirements of environmental and system stability during many hours of data acquisition,in many practical applications,it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training.Here,we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation.The most significant advantage of the resulting physics-enhanced deep neural network(PhysenNet)is that it can be used without training beforehand,thus eliminating the need for tens of thousands of labeled data.We take single-beam phase imaging as an example for demonstration.We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object,and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model.This opens up a new paradigm of neural network design,in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

关键词： neural network eventually

Diagnosis and management of malaria in the intensive care unit

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Journal of Intensive Medicine 2024年第1期4卷 3-15页

作者： george Akafity Nicholas Kumi Joyce Ashong Department of Research Monitoringand EvaluationCape Coast Teaching HospitalCape CoastGhana Intensive Care Unit Department of Critical Care and AnesthesiaCape Coast Teaching HospitalCape Coast Department of Paediatrics and Child Health Cape Coast Teaching HospitalCape CoastGhana

Malaria is responsible for approximately three-quarters of a million deaths in humans globally each year.Most of the morbidity and mortality reported are from Sub-Saharan Africa and Asia,where the disease is endemic.In non-endemic areas,malaria is the most common cause of imported infection and is associated with significant mortality despite recent advancements and investments in elimination programs.Severe malaria often requires intensive care unit admission and can be complicated by cerebral malaria,respiratory distress,acute kidney injury,bleeding complications,and co-infection.Intensive care management includes prompt diagnosis and early initiation of effective antimalarial therapy,recognition of complications,and appropriate supportive care.However,the lack of diagnostic capacities due to limited advances in equipment,personnel,and infrastructure presents a challenge to the effective diagnosis and management of malaria.This article reviews the clinical classification,diagnosis,and management of malaria as relevant to critical care clinicians,highlighting the role of diagnostic capacity,treatment options,and supportive care.

关键词： Malaria Intensive care unit Severe malaria Antimicrobial resistance Antimalarials Artemisinin-based combination therapy

Engineering Nano/Microscale Chiral Self‑Assembly in 3D Printed Constructs

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Nano-Micro Letters 2024年第3期16卷 313-332页

作者： Mohsen Esmaeili Ehsan Akbari Kyle george Gelareh Rezvan Nader Taheri‑Qazvini Monirosadat Sadati Department of Chemical Engineering University of South CarolinaColumbiaSC 29208USA TA Instruments Waters LLCNew CastleDE 19720USA Biomedical Engineering Program University of South CarolinaColumbiaSC 29208USA

Helical hierarchy found in biomolecules like cellulose,chitin,and collagen underpins the remarkable mechanical strength and vibrant colors observed in living organisms.This study advances the integration of helical/chiral assembly and 3D printing technology,providing precise spatial control over chiral nano/microstructures of rod-shaped colloidal nanoparticles in intricate geometries.We designed reactive chiral inks based on cellulose nanocrystal(CNC)suspensions and acrylamide monomers,enabling the chiral assembly at nano/microscale,beyond the resolution seen in printed materials.We employed a range of complementary techniques including Orthogonal Superposition rheometry and in situ rheo-optic measurements under steady shear rate conditions.These techniques help us to understand the nature of the nonlinear flow behavior of the chiral inks,and directly probe the flow-induced microstructural dynamics and phase transitions at constant shear rates,as well as their post-flow relaxation.Furthermore,we analyzed the photo-curing process to identify key parameters affecting gelation kinetics and structural integrity of the printed object within the supporting bath.These insights into the interplay between the chiral inks self-assembly dynamics,3D printing flow kinematics and photopolymerization kinetics provide a roadmap to direct the out-of-equilibrium arrangement of CNC particles in the 3D printed filaments,ranging from uniform nematic to 3D concentric chiral structures with controlled pitch length,as well as random orientation of chiral domains.Our biomimetic approach can pave the way for the creation of materials with superior mechanical properties or programable photonic responses that arise from 3D nano/microstructure and can be translated into larger scale 3D printed designs.

关键词： Directed chiral self-assembly Cellulose nanocrystals Bioinspired nanocomposite 3D printing Rheology

Flexible ultra-thin super-resolution endoscopy

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Science China(Physics,Mechanics & Astronomy) 2024年第3期67卷 171-172页

作者： george S.D.Gordon Department of Electrical and Electronic Engineering University of NottinghamNottingham NG7 2RDUK

In recent years, multimode fiber(MMF) has become an important tool for high-resolution endoscopic imaging in narrow channels(e.g., in the brain via needles or inside blood vessels) due to its high mode density and fine diameter, typically <0.125 mm [1-6]. However, a major obstacle to widespread deployment in applications is that realistic perturbations, including bending, twisting and changes in temperature, cause unacceptable image degradation.

关键词： fiber resolution typically

Nanozyme‑Engineered Hydrogels for Anti‑Inflammation and Skin Regeneration

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Nano-Micro Letters 2024年第6期16卷 127-179页

作者： Amal george Kurian Rajendra K.Singh Varsha Sagar Jung‑Hwan Lee Hae‑Won Kim Institute of Tissue Regeneration Engineering(ITREN) Dankook UniversityCheonan 31116Republic of Korea Department of Nanobiomedical Science&BK21 NBM Global Research Center for Regenerative Medicine Dankook UniversityCheonan 31116Republic of Korea Department of Biomaterials Science School of DentistryDankook UniversityCheonan 31116Republic of Korea UCL Eastman‑Korea Dental Medicine Innovation Centre Dankook UniversityCheonan 31116Republic of Korea Cell and Matter Institute Dankook UniversityCheonan 31116Republic of Korea Mechanobiology Dental Medicine Research Center Dankook UniversityCheonan 31116Republic of Korea

Inflammatory skin disorders can cause chronic scarring and functional impairments,posing a significant burden on patients and the healthcare system.Conventional therapies,such as corticosteroids and nonsteroidal anti-inflammatory drugs,are limited in efficacy and associated with adverse effects.Recently,nanozyme(NZ)-based hydrogels have shown great promise in addressing these challenges.NZ-based hydrogels possess unique therapeutic abilities by combining the therapeutic benefits of redox nanomaterials with enzymatic activity and the water-retaining capacity of hydrogels.The multifaceted therapeutic effects of these hydrogels include scavenging reactive oxygen species and other inflammatory mediators modulating immune responses toward a pro-regenerative environment and enhancing regenerative potential by triggering cell migration and differentiation.This review highlights the current state of the art in NZ-engineered hydrogels(NZ@hydrogels)for anti-inflammatory and skin regeneration applications.It also discusses the underlying chemo-mechano-biological mechanisms behind their effectiveness.Additionally,the challenges and future directions in this ground,particularly their clinical translation,are addressed.The insights provided in this review can aid in the design and engineering of novel NZ-based hydrogels,offering new possibilities for targeted and personalized skin-care therapies.

关键词： Nanozymes Hydrogels ROS scavenging Anti-inflammation Skin regeneration