Evidence map›Paper›PMID 37495922›Full record

ArticleAdvances in experimental medicine and biology2023

Advanced Topics in Quantitative Acoustic Microscopy.

Cameron Hoerig, Jonathan Mamou

Abstract read
PubMed Publisher
In one paragraph

Article in Advances in experimental medicine and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

2 authors at 1 institution in 1 country.

Cameron HoerigDepartment of Radiology, Weill Cornell Medicine, New York, NY, USA.
Jonathan MamouDepartment of Radiology, Weill Cornell Medicine, New York, NY, USA. jom4032@med.cornell.edu.
Weill Cornell Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative acoustic microscopy (QAM) reconstructs two-dimensional (2D) maps of the acoustic properties of thin tissue sections. Using ultrahigh frequency transducers (≥ 100 MHz), unstained, micron-thick tissue sections affixed to glass are raster scanned to collect radiofrequency (RF) echo data and generate parametric maps with resolution approximately equal to the ultrasound wavelength. 2D maps of speed of sound, mass density, acoustic impedance, bulk modulus, and acoustic attenuation provide unique and quantitative information that is complementary to typical optical microscopy modalities. Consequently, many biomedical researchers have great interest in utilizing QAM instruments to investigate the acoustic and biomechanical properties of tissues at the micron scale. Unfortunately, current state-of-the-art QAM technology is costly, requires operation by a trained user, and is accompanied by substantial experimental challenges, many of which become more onerous as the transducer frequency is increased. In this chapter, typical QAM technology and standard image formation methods are reviewed. Then, novel experimental and signal processing approaches are presented with the specific goal of reducing QAM instrument costs and improving ease of use. These methods rely on modern techniques based on compressed sensing and sparsity-based deconvolution methods. Together, these approaches could serve as the basis of the next generation of QAM instruments that are affordable and provide high-resolution QAM images with turnkey solutions requiring nearly no training to operate.

Indexed as

AcousticsMicroscopy, AcousticSoundUltrasonographyCompressed sensingDeconvolutionMachine learningQuantitativeSuper-resolutionUltrahigh frequency

Identifiers

PMID37495922
OpenAlexW4385268796

What OpenQuestion holds

Textmetadata
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.