Multiparametric prostate MRI quality assessment using a semi-automated PI-QUAL software program

AbstractThe technical requirements for the acquisition of multiparametric magnetic resonance imaging (mpMRI) of the prostate have been clearly outlined in the Prostate Imaging Reporting and Data System (PI-RADS) guidelines, but there is still huge variability in image quality among centres across the world. It has been difficult to quantify what constitutes a good-quality image, and a first attempt to address this matter has been the publication of the Prostate Imaging Quality (PI-QUAL) score and its dedicated scoring sheet. This score includes the assessment of technical parameters that can be obtained from the DICOM files along with a visual evaluation of certain features on prostate MRI (e.g., anatomical structures). We retrospectively analysed the image quality of 10 scans from different vendors and magnets using a semiautomated dedicated PI-QUAL software program and compared the time needed for assessing image quality using two methods (semiautomated assessment versus manual filling of the scoring sheet). This semiautomated software is able to assess the technical parameters automatically, but the visual assessment is still performed by the radiologist. There was a significant reduction in the reporting time of prostate mpMRI quality according to PI-QUAL using the dedicated software program compared to manual filling (5′54″ versus 7′59″; p = 0.005). A semiautomated PI-QUAL software program allows the radiologist to assess the technical details related to the image quality of prostate mpMRI in a quick and reliable manner, allowing clinicians to have more confidence that the quality of mpMRI of the prostate is sufficient to determine patient care.

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Understanding PI-QUAL for prostate MRI quality: a practical primer for radiologists

Insights into Imaging ◽

10.1186/s13244-021-00996-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Francesco Giganti ◽

Alex Kirkham ◽

Veeru Kasivisvanathan ◽

Marianthi-Vasiliki Papoutsaki ◽

Shonit Punwani ◽

...

Keyword(s):

Diffusion Weighted Imaging ◽

Signal To Noise Ratio ◽

High Signal ◽

Diagnostic Quality ◽

Prostate Mri ◽

Prostate Imaging ◽

Dynamic Contrast Enhanced ◽

Diffusion Weighted ◽

Contrast Enhanced

AbstractProstate magnetic resonance imaging (MRI) of high diagnostic quality is a key determinant for either detection or exclusion of prostate cancer. Adequate high spatial resolution on T2-weighted imaging, good diffusion-weighted imaging and dynamic contrast-enhanced sequences of high signal-to-noise ratio are the prerequisite for a high-quality MRI study of the prostate. The Prostate Imaging Quality (PI-QUAL) score was created to assess the diagnostic quality of a scan against a set of objective criteria as per Prostate Imaging-Reporting and Data System recommendations, together with criteria obtained from the image. The PI-QUAL score is a 1-to-5 scale where a score of 1 indicates that all MR sequences (T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced sequences) are below the minimum standard of diagnostic quality, a score of 3 means that the scan is of sufficient diagnostic quality, and a score of 5 implies that all three sequences are of optimal diagnostic quality. The purpose of this educational review is to provide a practical guide to assess the quality of prostate MRI using PI-QUAL and to familiarise the radiologist and all those involved in prostate MRI with this scoring system. A variety of images are also presented to demonstrate the difference between suboptimal and good prostate MR scans.

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A SPATIAL DOMAIN OBJECT SEPARABILITY BASED NO-REFERENCE IMAGE QUALITY MEASURE USING MEAN AND VARIANCE

International Journal of Image and Graphics ◽

10.1142/s0219467813400056 ◽

2013 ◽

Vol 13 (02) ◽

pp. 1340005 ◽

Cited By ~ 2

Author(s):

KANJAR DE ◽

V. MASILAMANI

Keyword(s):

Image Quality ◽

Image Quality Assessment ◽

Quality Measure ◽

Reference Image ◽

Individual Object ◽

The Difference ◽

Image Quality Measure ◽

Good Quality Image ◽

Degraded Images

In many modern image processing applications determining quality of the image is one of the most challenging tasks. Researchers working in the field of image quality assessment design algorithms for measuring and quantifying image quality. The human eye can identify the difference between a good quality image and a noisy image by simply looking at the image, but designing a computer algorithm to automatically determine the quality of an image is a very challenging task. In this paper, we propose an image quality measure using the concept of object separability. We define object separability using variance. Two objects are very well separated if variance of individual object is less and mean pixel values of neighboring objects are very different. Degradation in images can be due to a number of reasons like additive noises, quantization defects, sampling defects, etc. The proposed no-reference image quality measure will determine quality of degraded images and differentiate between good and degraded images.

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Usefulness of a No-Reference Metric for Evaluation of Images in Nuclear Medicine -A Comparative Study with Visual Assessment

10.21203/rs.3.rs-372416/v1 ◽

2021 ◽

Author(s):

Shigeaki Higashiyama ◽

Yutaka Katayama ◽

Atsushi Yoshida ◽

Nahoko Inoue ◽

Takashi Yamanaga ◽

...

Keyword(s):

Nuclear Medicine ◽

Image Quality ◽

Paired Comparison ◽

Evaluation Criteria ◽

Visual Assessment ◽

Comparison Method ◽

Natural Image ◽

Nuclear Medicine Imaging ◽

Visual Evaluation ◽

Long Time

Abstract Purpose The normalized mean square error (NMSE) method is widely used to evaluate images in nuclear medicine. However, its use in clinical practice requires creating target images over a long time, which is difficult. It also requires standard images and reference teacher data that can be used at facilities worldwide because methods and doses in nuclear medicine imaging are yet to be standardized. This study examined the validity of perception-based image quality evaluator (PIQE), a no-reference metric that does not require a target image for image evaluation in nuclear medicine.Methods The Hoffman brain phantom with 18F-fluoro-2-deoxy-D-glucose (FDG) was imaged and evaluated on slices in which the frontal and temporal lobes, bilateral lateral ventricles, and basal ganglia were drawn. Sixteen images with different pixel numbers and acquisition times were created and evaluated from the same slice. For the images obtained, visual evaluation criteria were developed for two endpoints: separation of white and gray matter boundaries, and uniformity of accumulation. Five evaluators assessed all the images using a paired comparison method, and then scored and ranked them. The images were also physically evaluated by the PIQE and a natural image quality evaluator (NIQE). The rankings obtained from the visual and physical evaluations were statistically compared.Results Based on Spearman's test of significance, visual evaluation rankings showed a strong correlation with those obtained with PIQE (rs = 0.9559, p < 0.0001) and no correlation with those obtained with NIQE (rs = 0.2324 and p = 0.3865).Conclusions Evaluation of images by PIQE is as effective as visual evaluation.

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Image Enhancement In Video Analytics Systems

Control Systems and Computers ◽

10.15407/csc.2020.06.003 ◽

2020 ◽

pp. 3-20

Author(s):

Oleksandr. M. Golovin ◽

Keyword(s):

Image Quality ◽

Negative Impact ◽

Weather Conditions ◽

Practical Method ◽

Video Analytics ◽

Technical Parameters ◽

Automatic Mode ◽

Corrected Image ◽

Video Sensors

Recently, video analytics systems are rapidly evolving, and the effectiveness of their work depends primarily on the quality of operations at the initial level of the entire processing process, namely the quality of segmentation of objects in the scene and their recognition. Successful performance of these procedures is primarily due to image quality, which depends on many factors: technical parameters of video sensors, low or uneven lighting, changes in lighting levels of the scene due to weather conditions, time changes in illumination, or changes in scenarios in the scene. This paper presents a new, accurate, and practical method for assessing the improvement of image quality in automatic mode. The method is based on the use of nonlinear transformation function, namely, gamma correction, which reflects properties of a human visual system, effectively reduces the negative impact of changes in scene illumination and due to simple adjustment and effective implementation is widely used in practice. The technique of selection in an automatic mode of the optimum value of the gamma parameter at which the corrected image reaches the maximum quality is developed.

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Does a cleansing enema improve image quality of 3T surface coil multiparametric prostate MRI?

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.24833 ◽

2014 ◽

Vol 42 (3) ◽

pp. 689-697 ◽

Cited By ~ 27

Author(s):

Christopher Lim ◽

Jeff Quon ◽

Matt McInnes ◽

Wael M. Shabana ◽

Mohamed El-Khodary ◽

...

Keyword(s):

Image Quality ◽

Surface Coil ◽

Improve Image Quality ◽

Prostate Mri ◽

Multiparametric Prostate Mri

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Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score

British Journal of Radiology ◽

10.1259/bjr.20210415 ◽

2021 ◽

pp. 20210415

Author(s):

Francesco Giganti ◽

Veeru Kasivisvanathan ◽

Alex Kirkham ◽

Shonit Punwani ◽

Mark Emberton ◽

...

Keyword(s):

Prostate Cancer ◽

Image Quality ◽

Critical Review ◽

Resonance Imaging ◽

Technical Standards ◽

Prostate Imaging ◽

Technical Requirements ◽

Patient Preparation ◽

Clinically Significant

There is increasing interest in the use of multiparametric magnetic resonance imaging (mpMRI) in the prostate cancer pathway. The European Association of Urology (EAU) and the British Association of Urological Surgeons (BAUS) now advise mpMRI prior to biopsy, and the Prostate Imaging Reporting and Data System (PI-RADS) recommendations set out the minimal technical requirements for the acquisition of mpMRI of the prostate. The widespread and swift adoption of this technique has led to variability in image quality. Suboptimal image acquisition reduces the sensitivity and specificity of mpMRI for the detection and staging of clinically significant prostate cancer. This critical review outlines the studies aimed at improving prostate MR quality that have been published over the last 5 years. These span from the use of specific MR sequences, magnets and coils to patient preparation. The rates of adherence of prostate mpMRI to technical standards in different cohorts across the world are also discussed. Finally, we discuss the first standardised scoring system (i.e., Prostate Imaging Quality, PI-QUAL) that has been created to evaluate image quality, although further iterations of this score are expected in the future.

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Imaging quality and prostate MR: it is time to improve

British Journal of Radiology ◽

10.1259/bjr.20200934 ◽

2020 ◽

pp. 20200934

Author(s):

Francesco Giganti ◽

Clare Allen

Keyword(s):

Quality Criteria ◽

Multiparametric Mri ◽

Imaging Quality ◽

Prostate Imaging ◽

Technical Requirements ◽

The World ◽

Clinically Significant ◽

The Uk ◽

Robust Consensus

The Prostate Imaging Reporting and Data System (PI-RADS) guidelines set out the minimal technical requirements for the acquisition of multiparametric MRI (mpMRI) of the prostate. However, the rapid diffusion of this technique has inevitably led to variability in scan quality among centres across the UK and the world. Suboptimal image acquisition reduces the sensitivity and specificity of this technique for the detection of clinically significant prostate cancer and results in clinicians losing confidence in the technique. Two expert panels, one from the UK and one from the European Society of Urogenital Radiology (ESUR)/EAU Section of Urologic Imaging (ESUI), have stressed the importance to establish quality criteria for the acquisition of mpMRI of the prostate. A first attempt to address this issue has been the publication of the Prostate Imaging Quality (PI-QUAL) score, which assesses the mpMRI quality against a set of objective criteria (PI-RADS guidelines) together with criteria obtained from the image. PI-QUAL represents the first step towards the standardisation of a scoring system to assess the quality of prostate mpMRI prior to reporting and allows clinicians to have more confidence in using the scan to determine patient care. Further refinements after robust consensus among experts at an international level need to be agreed before its widespread adoption in the clinical setting.

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Determination of Technical Parameters in CTP Process and its Image Quality

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.197.462 ◽

2012 ◽

Vol 197 ◽

pp. 462-466 ◽

Cited By ~ 1

Author(s):

Wei Chen ◽

You Ming Li ◽

Xue Jin Zhang

Keyword(s):

Image Quality ◽

Focal Length ◽

Average Density ◽

Optimal Parameters ◽

Technical Parameters ◽

Laser Head

The focal length, zoom, irradiating power and cylinder speed of laser head influence the exposure quality directly in computer-to-plate (CTP) process. This paper determined the most optimal parameters which used screen thermal sensitive device PT-R4100 by changing the focal length, zoom, irradiating power and cylinder speed of laser head. The most optimal parameters of focal length, zoom, irradiating power and cylinder speed in CTP process were 1500puls, 6700puls, 90%, 600r/min respectively。Compared with computer-to-film (CTF), we found that the image quality of CTP process was better. The average density, register accuracy and ink trapping of CTP process were higher than that of CTF. Ink formation was also better.

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Image quality dependence on image processing software in computed radiography

South African Journal of Radiology ◽

10.4102/sajr.v16i2.305 ◽

2012 ◽

Vol 16 (2) ◽

pp. 44-48 ◽

Cited By ~ 4

Author(s):

Lourens Jochemus Strauss ◽

William ID Rae

Keyword(s):

Image Quality ◽

Transfer Functions ◽

Computed Radiography ◽

Visual Assessment ◽

Modulation Transfer ◽

Post Processing ◽

Software Packages ◽

Image Appearance ◽

Background Image

Background. Image post-processing gives computed radiography (CR) a considerable advantage over film-screen systems. After digitisation of information from CR plates, data are routinely processed using manufacturer-specific software. Agfa CR readers use MUSICA software, and an upgrade with significantly different image appearance was recently released: MUSICA2. Aim. This study quantitatively compares the image quality of images acquired without post-processing (flatfield) with images processed using these two software packages. Methods. Four aspects of image quality were evaluated. An aluminium step-wedge was imaged using constant mA at tube voltages varying from 40 to 117kV. Signal-to-noise ratios (SNRs) and contrast-to-noise Ratios (CNRs) were calculated from all steps. Contrast variation with object size was evaluated with visual assessment of images of a Perspex contrast-detail phantom, and an image quality figure (IQF) was calculated. Resolution was assessed using modulation transfer functions (MTFs). Results. SNRs for MUSICA2 were generally higher than the other two methods. The CNRs were comparable between the two software versions, although MUSICA2 had slightly higher values at lower kV. The flatfield CNR values were better than those for the processed images. All images showed a decrease in CNRs with tube voltage. The contrast-detail measurements showed that both MUSICA programmes improved the contrast of smaller objects. MUSICA2 was found to give the lowest (best) IQF; MTF measurements confirmed this, with values at 3.5 lp/mm of 10% for MUSICA2, 8% for MUSICA and 5% for flatfield. Conclusion. Both MUSICA software packages produced images with better contrast resolution than unprocessed images. MUSICA2 has slightly improved image quality than MUSICA.

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