Standardized volume-rendering of contrast-enhanced renal magnetic resonance angiography

Smedby, Örjan; Öberg, R; Åsberg, B.; Stenström, Hugo; Eriksson, Per

doi:10.1080/02841850510021454

Cited by 6 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ordinal logistic regression models easily handle situations involving several factors potentially influencing the outcome variable, and the technique now belongs to the standard statistical armamentarium. We have, however, been able to find only two publications in which it was applied to visual grading studies of image quality [17,18]. Occasionally, researchers have applied dichotomisation of ordinal visual grading data prior to analysis with binary logistic regression, i.e.…”

mentioning

confidence: 99%

Visual grading regression: analysing data from visual grading experiments with regression models

Smedby¹,

Fredrikson²

2010

BJR

View full text Add to dashboard Cite

For visual grading experiments, which are an easy and increasingly popular way of studying image quality, hitherto used data analysis methods are often inadequate. Visual grading analysis makes assumptions that are not statistically appropriate for ordinal data, and visual grading characteristic curves are difficult to apply in more complex experimental designs. The approach proposed in this paper, visual grading regression (VGR), consists of an established statistical technique, ordinal logistic regression, applied to data from single-image and image-pair experiments with visual grading scores selected on an ordinal scale. The approach is applicable for situations in which, for example, the effects of the choice of imaging equipment and post-processing method are to be studied simultaneously, while controlling for potentially confounding variables such as patient and observer identity. The analysis can be performed with standard statistical software packages using straightforward coding of the data. We conclude that the proposed statistical technique is useful in a wide range of visual grading studies.

show abstract

mentioning

confidence: 99%

Visual grading regression: analysing data from visual grading experiments with regression models

Smedby¹,

Fredrikson²

2010

BJR

View full text Add to dashboard Cite

show abstract

“…However, it remains to be seen whether more standardized methods can be applied to attain the same goal. A recent study from our center has implemented such an approach with promising results in renal artery MRA without, however, studying quantitative diameter measurements (12).…”

Section: Discussionmentioning

confidence: 99%

Volume rendering compared with maximum intensity projection for magnetic resonance angiography measurements of the abdominal aorta

Persson

Dahlström²,

Engellau³

et al. 2004

Acta Radiol

Self Cite

View full text Add to dashboard Cite

show abstract

Renal artery stenosis: Extracting quantitative parameters with a mathematical model fitted to magnetic resonance blood flow data

Larsson

Persson

Eriksson

et al. 2007

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Purpose:To investigate the feasibility of quantitative parameter extraction from a mathematical model fitted to renal artery magnetic resonance flow data. Material and Methods:A total of 16 subjects, eight patients, and eight normal controls, were examined with cine phase-contrast velocity measurements, and blood flow data from the aorta and both renal arteries were extracted by means of contour detection. A mathematical model with eight parameters describing the time, duration, and amplitude of the systolic acceleration and the diastolic deceleration was fitted to the aorta and renal artery blood flow data from each subject. The curve fitting was evaluated with R 2 values. Statistical analysis was performed with unpaired Wilcoxon tests and stepwise logistic regression. Results:A total of three data sets out of 48 yielded R 2 values below 0.80 and were considered unreliable for parameter estimation. Basal flow was significantly, and systolic peak amplitude almost significantly, lower in stenotic arteries. Logistic regression indicated that two parameters describing basal flow and the duration of acceleration can accurately predict stenosis. Conclusion:The results suggest that it is technically feasible to fit a mathematical model to renal blood flow data, extracting quantitative parameters that may prove useful for quantification and diagnosis of renal artery stenosis.

show abstract

Standardized volume-rendering of contrast-enhanced renal magnetic resonance angiography

Abstract: Presenting renal MRA images with VRT gave results in good agreement with MIP. With VRT protocols defined from the histogram of the image, the lack of an absolute gray scale in MRI need not be a major problem.

Cited by 6 publications

References 19 publications

Visual grading regression: analysing data from visual grading experiments with regression models

Visual grading regression: analysing data from visual grading experiments with regression models

Volume rendering compared with maximum intensity projection for magnetic resonance angiography measurements of the abdominal aorta

Renal artery stenosis: Extracting quantitative parameters with a mathematical model fitted to magnetic resonance blood flow data

Contact Info

Product

Resources

About