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Enregistrement W4412909366 · doi:10.1093/mam/ozaf048.1113

A Comparative Analysis of STEM Phase Retrieval Techniques: Evaluating Transfer of Information and Dose Efficiency

2025· article· en· W4412909366 sur OpenAlexaff
Georgios Varnavides, Stephanie M. Ribet, Julie Marie Bekkevold, Berk Küçükoğlu, Henning Stahlberg, Lewys Jones, Mary Scott, Colin Ophus

Notice bibliographique

RevueMicroscopy and Microanalysis · 2025
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueAdvanced X-ray Imaging Techniques
Établissements canadiensTrinity College
Organismes subventionnairesnon disponible
Mots-clésPhase (matter)Information retrievalMaterials scienceComputer scienceChemistry

Résumé

récupéré en direct d'OpenAlex

Reconstructing the phase information of weakly-scattering samples using intensity measurements is a longstanding problem in many imaging and diffraction fields, including electron microscopy [1, 2]. Recent hardware and algorithmic developments have renewed interest in using a set of converged probe diffraction intensities, that is 4D-STEM measurements [3], for dose-efficient phase retrieval in STEM. These include integrated center-of-mass (iCOM) imaging [4], tilt-corrected bright-field (tcBF) STEM [5], as well as direct ptychography, such as single-side band (SSB) and Wigner distribution deconvolution (WDD) [6-7], and iterative ptychography [8]. These STEM phase retrieval techniques all have their strengths and weaknesses, as well as distinct experimental acquisition and computational reconstruction requirements. iCOM imaging is the most computationally inexpensive technique yet suffers from low-spatial frequency artifacts and scan-step limited resolution. tcBF relaxes scan sampling requirements and provides robust estimates of the aberration surface yet exhibits “Thon-like” oscillations and contrast reversals characteristic of HRTEM. Direct ptychography techniques improve the fidelity of iCOM imaging and are robust at low electron doses yet require precise knowledge of the often-unknown incoming illumination. Iterative ptychography offers “super” resolution beyond the numerical aperture and can solve for the unknown illumination yet is very computationally expensive and slow at converging low spatial frequencies. Figure 1 illustrates the above observations graphically by formulating the contrast transfer function (CTF) as the outcome of convolution with the converged probe. The aperture autocorrelation function, which can be understood geometrically as the area of the double-overlap region of shifted apertures, acts as an envelope function for the CTF and is modulated by the probe aberration function [9]. While iCOM and tcBF should be performed in-focus and out-of-focus respectively, SSB illustrates strong signal under both conditions – provided the aberration surface is known. While the CTF is an important tool in assessing the performance of imaging techniques, representing the maximum usable signal in an ideal “infinite-dose” measurement, it fails to capture the inevitable effect of finite-dose at realistic detectors and is thus of limited utility to STEM practitioners. As two simple failure modes, notice how iCOM is predicted to obtain the zero-frequency component with high fidelity, which is inaccurate since, without a reference wave, we can only measure relative phase changes. Similarly, the iterative ptychography CTF is predicted to be unity, which we know from experimental observations of “Thon-like” oscillations in biological reconstructions over vitreous ice to be erroneous (Fig. 2) [10]. To investigate this, we perform numerical simulations on white-noise objects, that is samples with random phase and constant Fourier amplitude, for various electron doses repeatedly and compute the spectral signal-to-noise ratio (SSNR) [11]. The choice of a white-noise object implies this is trivially related to the square-root of the recently proposed detective quantum efficiency metric without the need for a reference reconstruction [12]. Figure 2 summarizes the results and highlights how for iCOM, tcBF, and SSB, the SSNR is, as expected, independent of electron dose, and can in-fact be modeled analytically. The SSNR for iterative ptychography however, illustrates curious behavior: At low electron doses, it asymptotically approaches the SSB SSNR while at higher electron doses, it starts to “fill-in” high-frequency information. The analytical and numerical results are compared against experimental tcBF and iterative ptychography reconstructions of biological crystals at large defocus [10]. The SSNR results are placed in the context of how practitioners can choose between different STEM phase retrieval techniques and design acquisition parameters. Moreover, we investigate the robustness of information transfer for each of these techniques using segmented detectors and discuss their promise for sub-second phase retrieval. Finally, we discuss how we can boost low spatial frequency convergence by using STEM ptychographic holography, where one uses a diffraction grating to scan multiple beams across the sample while keeping one of them over vacuum to act as a reference [13]. Analytical models for STEM phase retrieval contrast transfer functions (CTFs), highlighting the convolutional role of the converged probe and demonstrating different techniques are best suited for in-focus / defocused acquisitions respectively. Numerical and analytical results of the spectral signal to noise ratio (SSNR) for STEM phase retrieval techniques. The derived functional forms for tcBF and ptychography are compared against experimental power spectra of biological crystals.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,002
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,362
Écart entre enseignants0,347 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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