Evidence map›Paper›PMID 37095888›Full record

ArticlePhotoacoustics2023

Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging

Zhengyuan Zhang, Haoran Jin, Wenwen Zhang, Wenhao Lu, Zesheng Zheng, Arunima Sharma, Manojit Pramanik, Yuanjin Zheng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Photoacoustics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Sound Out the Deep Clarity: Super-Resolution Photoacoustic Imaging at Depths.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024
    Review
  7. Review
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhengyuan ZhangNanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore.
Haoran JinZhejiang University, College of Mechanical Engineering, The State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou 310027, China.
Wenwen ZhangNanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore.
Wenhao LuNanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore.
Zesheng ZhengNanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore.
Arunima SharmaJohns Hopkins University, Electrical and Computer Engineering, Baltimore, MD 21218, USA.
Manojit PramanikIowa State University, Department of Electrical and Computer Engineering, Ames, Iowa, USA.
Yuanjin ZhengNanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and

Indexed as

Acoustic resolutionDeep CNN priorDeep learningImage enhancementModel-based methodPhotoacoustic microscopyPlug and play prior

Identifiers

PMID37095888
PMCPMC10121479

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.