TU‐D‐L100J‐08: Image Quality Enhancement in MVCT Scanning

Overbeck, C; Varadhan,; Song, Hui

doi:10.1118/1.2761372

Cited by 6 publications

(2 citation statements)

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“…Mostly, it is considered effective and efficient for colour transformation contour and visible parts with effective robustness. As for the coloured heart clinical images centred on the texture data structure pixels, the weight setting is done to enhance the level of accuracy in images which can considerably be enhanced to form colour balance [14]. Image similarity in the process of restoration is illustrated as shown below:…”

Section: Step 4: Image Restorationmentioning

confidence: 99%

Big Data Techniques for Clinical Image Analysis

Ying

2021

International Journal of Advanced Information and Communication Technology

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The process of extracting clinical images, for instance, heart images from cameras is full of noises and complexities. As such, the general expenditure for processing these images like resources and time is significantly high, mostly for complex and large amounts of data. In that case, this research contribution utilizes the machine vision-centred method to effectively address these issues. The method significantly incorporates four essential stages with various forms of algorithms to handle the clinical heart images. In the first stage, the smoothing algorithm is utilized to minimize some form of noise. In the second stage, the filtering algorithms are applied for the analysis of images to effectively identify the targeted region. In the third stage, more developed algorithm is utilized to evaluate the image characteristics in the targeted region, identifying the basic image outline. Lastly, the reduction algorithm is meant to transform the original image into significantly precise and smooth pictures. The experimental findings indicate that the machine vision-centred clinical image analysis algorithm might significantly extract fundamental data and attain the most reliable results, contrasting with the most ancient image analysis techniques.

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Section: Step 4: Image Restorationmentioning

confidence: 99%

Big Data Techniques for Clinical Image Analysis

Ying

2021

International Journal of Advanced Information and Communication Technology

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“…The applications in the mentioned fields include point-of-sale accessibility controls, high privacy data accessibility, high privacy physical address controls and user identification. These technologies are appreciated as a result of dual matching capability, since the user vascular patterns might be matching the personalized ID cards over the databases of most scanned vascular frameworks [3].…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition for Veins and Feature Extraction Techniques

Welland

2021

International Journal of Advanced Information and Communication Technology

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Among the biometric system approaches, the Vein Pattern Recognition (VPR) is one of the rapidly expanding aspects of medical image enhancement and scanning. Whereas the ideology behind the technique is modest, there are some problems reported throughout the implementation and designing of vein scanning devices, regarding the hardware lighting framework and the real algorithms utilized for the implementation and processing of a number of images. To maintain the scanning issues to minimize measure, the images from the cameras have to be nearly noiseless and the algorithms have to be capable of detecting the patterns of the veins in different actual-life scenarios. Most implementations of the approach have been commercialized which makes it necessary to launch a system which can extract, analyse and detect the precise human vein patterns whereas maintaining the minimal costs and minimizing the computation requirements of image processing algorithms. Based on that this provides a critical survey of pattern recognition for VPR. Moreover, it provides the hardware processing and implementation remedies based on the approach.

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Multimodality Image Guided Total Marrow Irradiation and Verification of the Dose Delivered to the Lung, PTV, and Thoracic Bone in a Patient: A Case Study

Song¹,

Verneris²,

Dusenbery³

et al. 2009

Technol Cancer Res Treat

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This work reports our initial experience using multimodality image guidance to improve total marrow irradiation (TMI) using helical tomotherapy. We also monitored the details of the treatment delivery to glean information necessary for the implementation of future adaptive processes. A patient with metastatic Ewing's sarcoma underwent MRI, and bone scan imaging prior to TMI. A whole body kilovoltage CT (kVCT) scan was obtained for intensity modulated TMI treatment planning, including a boost treatment to areas of bony involvement. The delivered dose was estimated by using MVCT images from the helical tomotherapy treatment unit, compared to the expected dose distributions mapped onto the kVCT images. Clinical concerns regarding patient treatment and dosimetric uncertainties were also evaluated. A small fraction of thoracic bone volume received lower radiation dose than the prescribed dose. Reconstructed planned treatment volume (PTV) and the dose delivered to the lung were identical to planned dose. Bone scan imaging had a higher sensitivity for detecting skeletal metastasis compared to MR imaging. However the bone scan lacked sufficient specificity in three dimensions to be useful for planning conformal radiation boost treatments. Inclusion of appropriate imaging modalities improves detection of metastases, which allows the possibility of a radiation dose boost to metastases during TMI.Conformal intensity modulated radiation therapy via helical tomotherapy permitted radiation delivery to metastases in the skull with reduced dose to brain in conjunction with TMI. While TMI reduces irradiation to the lungs, onboard megavoltage computed tomography (MVCT) to verify accurate volumetric dose coverage to marrow-containing thoracic bones may be essential for successful conformal TMI treatment.

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TU‐D‐L100J‐08: Image Quality Enhancement in MVCT Scanning

Cited by 6 publications

References 0 publications

Big Data Techniques for Clinical Image Analysis

Big Data Techniques for Clinical Image Analysis

Pattern Recognition for Veins and Feature Extraction Techniques

Multimodality Image Guided Total Marrow Irradiation and Verification of the Dose Delivered to the Lung, PTV, and Thoracic Bone in a Patient: A Case Study

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