電子斷層掃瞄由於其高解像度而有利於奈米材料的3D表徵，但它受到“缺失楔形效應”的限制，導致重建影象失真。 儘管目前的演算法，特別是機器學習中的神經網路技術，在糾正這些失真方面取得了進展，但由於訓練資料與實際情況之間的差異，它們仍然面臨著準確性挑戰。
fig. 1 schematics of missing wedge artifact in the conventional electron tomography and our usinet workflow.
由美國伊利諾大學厄巴納-香檳分校材料科學與工程系陳倩教授領導的團隊提出了USINET，這是一種無監督投影圖修復方法，旨在解決電子斷層掃瞄中常見的缺失楔形效應。
fig. 2 schematic of usinet training workflow.
USINET的無監督訓練機制消除了對參考真相、手動標註或傾斜影象模擬的依賴，顯著提高了其在真實電子斷層掃瞄資料集處理中的實用性，特別是在無法獲得全角度傾斜序列的情況下。
fig. 3 unsupervised sinogram inpainting implemented on 2d images.
該方法需要非常小的資料集（甚至低至20個奈米顆粒）和較小的傾斜範圍（45°）進行訓練，這使得它在處理光束敏感聚合物和生物材料時特別有價值，這些聚合物和生物材料可能會受到累積光束損傷的限制。
fig. 4 unsupervised sinogram inpainting implemented on 3d images.
Usinet對窄傾斜範圍的耐受性在原位電子斷層掃瞄研究中至關重要，例如在研究電化學迴圈、催化或腐蝕過程中奈米顆粒形態的變化時，由於需要保持時間解像度，只能收集有限數量的傾斜序列。
fig. 5 orientation-dependent missing wedge artifact and comparison between different reconstruction algorithms.
此外，USINET不需要樣品平均，因此適用於廣泛的非均相奈米顆粒系統，例如電極奈米顆粒、催化劑奈米顆粒和可充電離子電池的奈米塑料。 在這些系統中，缺失的楔形效應特別棘手，因為它會導致明顯的各向異性失真。 儘管本次演示的主要重點是膠體奈米顆粒，但Usinet的無監督修復原理同樣適用於其他包含3D奈米級形態細節的樣品，例如合金的微觀域和聚醯胺分離膜的皺紋。 USINET的出現極大地擴充套件了電子斷層掃瞄的潛力，以闡明材料的形貌、合成和效能之間的相關性。
fig. 6 comparison of 3d reconstructions of experimentally synthesized nps with and without inpainting.
Unet的應用前景廣闊，不僅可以揭示電池或催化奈米材料的降解機理，還可以幫助了解天然存在的奈米塑料的形貌和聚集行為，並優化不同成分的奈米顆粒的合成工藝。 本文最近發表在NPJ Computational Materials上
fig. 7 visualizing the heterogeneity of experimentally synthesized nps.
editorial summary
missing wedge effect” in electron tomography: unsupervised ml
electron tomography is f**ored for its high-resolution in 3d characterization of nanomaterials but is limited by the “missing wedge effect”, leading to distortions in the reconstructed images. modern algorithms, especially neural network technologies in machine learning, h**e made advances in correcting these distortions, yet they still face challenges in accuracy due to differences between training data and actual conditions.
a team led by prof. qian chen from department of materials science and engineering, university of illinois at urbana-champaign, usa, purposed usinet, an unsupervised sinogram inpainting method to correct the missing wedge effect in electron tomography. the unsupervised training in usinet does not require ground truth, manual annotation, or tilt image simulation, and thus is practically applicable to real electron tomography datasets where full angle tilt series are not obtainable. the authors demonstrate that usinet works with a small number of training dataset (down to 20 nps) and narrow tilt range (±45°),which can be immediately useful for beam sensitive polymeric and biological materials where the tilt range can be limited by accumulated beam damage. the tolerance with a narrow tilt range could be critical for studies involving in-situ electron tomography—for example, on the evolution of the 3d shapes of nps during chemical reactions such as electrochemical cycling, catalysis, and corrosion—where only scarce tilt series can be collected to ensure temporal resolution. moreover, usinet does not require sample **eraging and can thus apply to a broad range of heterogeneous np systems such as electrode nps used in rechargeable ion batteries, catalytical nps, and nanoplastics. the missing wedge effect is otherwise particularly problematic for heterogeneous systems by generating anisotropic distortion. although the demonstration focuses on colloidal nps, the principle of unsupervised inpainting is expected to work for other samples containing 3d nanoscale morphology details, such as microstructural domains in alloys and crumples in polyamide separation membranes. usinet brings the full potential of electron tomography in charting the relationships of morphology with synthesis and performance of materials. a wide scope of applications can be enabled by usinet, such as uncovering degradation mechanisms of battery or catalytical nanomaterials, understanding morphologies and aggregation beh**iors of naturally formed nanoplastics, and optimizing synthetic protocols of nps with varying compositions. this article was recently published in npj computational materials
原文摘要及其譯文
lehan yao, zhiheng lyu, jiahui li & qian chen
abstract complex natural and synthetic materials, such as subcellular organelles, device architectures in integrated circuits, and alloys with microstructural domains, require characterization methods that can investigate the morphology and physical properties of these materials in three dimensions (3d). electron tomography has unparalleled (sub-)nm resolution in imaging 3d morphology of a material, critical for charting a relationship among synthesis, morphology, and performance. however, electron tomography has long suffered from an experimentally un**oidable missing wedge effect, which leads to undesirable and sometimes extensive distortion in the final reconstruction. here we develop and demonstrate unsupervised sinogram inpainting for nanoparticle electron tomography (usinet) to correct missing wedges. usinet is the first sinogram inpainting method that can be realistically used for experimental electron tomography by circumventing the need for ground truth. we quantify its high performance using simulated electron tomography of nanoparticles (nps). we then apply usinet to experimental tomographs, where >100 decahedral nps and vastly different byproduct nps are simultaneously reconstructed without missing wedge distortion. the reconstructed nps are sorted based on their 3d shapes to understand the growth mechanism. our work presents usinet as a potent tool to advance electron tomography, especially for heterogeneous samples and tomography datasets with large missing wedges, e.g. collected for beam sensitive materials or during temporally-resolved in-situ imaging.
總結：複雜的天然和合成材料，如細胞亞結構器官、積體電路中的器件結構以及具有微觀結構域的合金，需要能夠以三維 （3D） 方式研究這些材料的形態和物理特性的表徵方法。 電子斷層掃瞄在對材料的 3D 形貌進行成像方面具有無與倫比的（亞）奈米解像度，這對於描述合成、形貌和效能之間的關係至關重要。 然而，電子斷層掃瞄長期以來一直受到實驗上不可避免的缺失楔形效應的困擾，這導致最終重建中出現不良的、有時甚至是巨大的失真**。 在這裡，我們開發並演示了一種用於奈米顆粒電子斷層掃瞄的無監督投影圖修復 （USINET），以校正缺失的楔形區域。 Uset是第一種可用於真實世界實驗電子斷層掃瞄的投影圖修復方法，它避免了對參考真相的需求。 我們使用模擬奈米顆粒電子斷層掃瞄來量化它們的高效能。 然後，我們將USINET應用於實驗色譜圖，其中同時重建了100多個十面體奈米顆粒和非常不同的副產物奈米顆粒，而不會遺漏楔形畸變。 根據其 3D 形態對重建的奈米顆粒進行分類，以了解生長機制。 我們的工作使用USINET作為推進電子斷層掃瞄的強大工具，特別是對於具有大量缺失楔塊的異質樣品和斷層掃瞄資料集，例如為敏感材料或時間分辨原位成像收集的資料。