ArticleIEEE transactions on radiation and plasma medical sciences2021
Towards
Article in IEEE transactions on radiation and plasma medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Reconstruction Algorithm-Based Ultrasonic and Spiral CT Images in Evaluating the Effects of Dexmedetomidine Anesthesia for Acute Abdomen.Computational and mathematical methods in medicine · 2021Trial
- Seeing the invisible: In vivo dosimetry in radiotherapy with radiacoustic IMAGING.Medical physics · 2025Review
- Patient-specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy.Medical physics · 2024Article
- 4DPhysics in medicine and biology · 2024Article
- Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction.Physics in medicine and biology · 2024Article
- X-ray-induced acoustic computed tomography and its applications in biomedicine.Journal of biomedical optics · 2024Review
- Discrete Wavelet Transformation for the Sensitive Detection of Ultrashort Radiation Pulse with Radiation-induced Acoustics.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Towards quantitative in vivo dosimetry using x-ray acoustic computed tomography.Medical physics · 2023Article
- Model-Based 3-D X-Ray Induced Acoustic Computerized Tomography.IEEE transactions on radiation and plasma medical sciences · 2023Article
- 3DPhysics in medicine and biology · 2022Article
- Ring artifacts removal in X-ray-induced acoustic computed tomography.Journal of innovative optical health sciences · 2022Article
- Model-Based X-Ray-Induced Acoustic Computed Tomography.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
X-ray-induced acoustic computed tomography (XACT) is a promising imaging modality to monitor the position of the radiation beam and the deposited dose during external beam radiotherapy delivery. The purpose of this study was to investigate the feasibility of using a transperineal ultrasound transducer array for XACT imaging to guide the prostate radiotherapy. A customized two-dimensional (2D) matrix ultrasound transducer array with 10000 (100×100 elements) ultrasonic sensors with a central frequency of 1 MHz was designed on a 5 cm×5 cm plane to optimize three-dimensional (3D) volumetric imaging. The CT scan and dose treatment plan for a prostate patient undergoing intensity modulated radiation therapy (IMRT) were obtained. In-house simulation was developed to model the time varying X-ray induced acoustic (XA) signals detected by the transperineal ultrasound array. A 3D filtered back projection (FBP) algorithm has been used for 3D XACT image reconstruction. Results of this study will greatly enhance the potential of XACT imaging for real time
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Registered trials
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.