Evidence map›Paper›PMID 34917305›Full record

ArticleJournal of healthcare engineering2021

Compressed Sensing Image Reconstruction of Ultrasound Image for Treatment of Early Traumatic Myositis Ossificans of Elbow Joint by Electroacupuncture.

Yi Zhu, Mengyuan Sheng, Yuanming Ouyang, Lichang Zhong, Kun Liu, Tan Ge, Yaochi Wu

Open access · hybridAbstract read
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 85% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

7 authors at 4 institutions in 1 country.

Yi ZhuDepartment of Traumatology Acupuncture and Massage, Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200233, China.ORCID 0000-0002-9721-9716
Mengyuan ShengInnovation Base Hospital of Jiangxi University of Traditional Chinese Medicine, Jiangxi, Nanchang 330004, China.ORCID 0000-0002-9321-8089
Yuanming OuyangDepartment of Orthopaedics, Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200233, China.ORCID 0000-0002-3025-2941
Lichang ZhongDepartment of Medical Ultrasonics, Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200233, China.ORCID 0000-0002-6701-7419
Kun LiuSchool of Information Engineering, Shanghai Maritime University, Shanghai 201204, China.ORCID 0000-0002-9435-8550
Tan GeDepartment of Preventive Medicine, Guangming Traditional Chinese Medicine Hospital, Shanghai 201399, China.ORCID 0000-0003-0065-9465
Yaochi WuDepartment of Traumatology Acupuncture and Massage, Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200233, China.ORCID 0000-0001-8135-0659
Shanghai Sixth People's Hospital · CNJiangxi University of Traditional Chinese Medicine · CNShanghai Maritime University · CNShanghai Traditional Chinese Medicine Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article conducts a retrospective analysis of 500 patients with posttraumatic elbow dysfunction admitted to our department from March 2019 to September 2020. The average time from injury to operation is 11 months (2-20 months). We adopt a personalized treatment method to completely remove the hyperplastic adhesion tissue and heterotopic ossification around the joint, remove part of the joint capsule and ligament, and release it to achieve maximum function. After the operation, an external fixator was used to stabilize the loosened elbow joint, and the patient was guided to perform rehabilitation exercises with the aid of a hinged external fixator, and celecoxib was used to prevent heterotopic ossification. Mayo functional scoring system was used to evaluate the curative effect before and after surgery. The rapid realization of ultrasound imaging under the framework of compressed sensing is studied. Under the premise of ensuring the quality of ultrasound imaging reconstruction, the theory of ultrasound imaging is improved, and a plane wave acoustic scattering ultrasound echo model is established. On this basis, the theory of compressed sensing is introduced, the mathematical model of compressed sensing reconstruction is established, and the fast iterative shrinkage thresholding algorithm (FISTA) of compressed sensing reconstruction is improved to reduce the computational complexity and the number of iterations. This article uses FISTA directly to reconstruct medical ultrasound images, and the reconstruction results are not ideal. Therefore, a simulation model of FISTA training and testing was established using the standard image library. By adding different intensities of noise to all images in the image library, the influence of noise intensity on the quality of FISTA reconstructed images is analyzed, and it is found that the FISTA model has requirements for the quality of the images to be reconstructed and the training set images. In this paper, Rob's blind deconvolution restoration algorithm is used to preprocess the original ultrasound image. The clarity of the texture details of the restored ultrasound image is significantly improved, and the image quality is improved, which meets the above requirements. This paper finally formed a reconstruction model suitable for ultrasound images. The reconstruction strategy verified by the ultrasound images provided by the Institute of Ultrasound Imaging of a medical university has achieved a significant improvement in the quality of ultrasound images.

Indexed as

Elbow JointElectroacupunctureMyositis OssificansHumansImage Processing, Computer-AssistedRetrospective StudiesUltrasonography

Identifiers

PMID34917305
PMCPMC8670903
OpenAlexW4200089222

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.