MRI texture analysis of subchondral bone at the tibial plateau

Mackay, James; Murray, Philip J.; Kasmai, Bahman; Johnson, Glyn; Donell, Simon; Toms, Andoni P.

doi:10.1007/s00330-015-4142-0

Cited by 26 publications

(20 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7,25,26 Similarly, texture analysis has also been used for examining potential differences in tissue architecture on CT in oropharyngeal squamous cell carcinomas and for help in evaluating changes in cartilage. 8,27 In these studies, a single CT protocol scanning algorithm has been used, limiting potential variations in the texture analysis feature related to scanning technique. This has future implications in multi-institutional research, such as for the imaging-based studies from The Cancer Imaging Archive, in which different scanning techniques could potentially reflect quantitative differences related to scanning techniques across different equipment manufacturers.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

Buch¹,

Li²,

Qureshi

et al. 2017

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

Our aim was to evaluate changes in texture features based on variations in CT parameters on a phantom. Scans were performed with varying milliampere, kilovolt, section thickness, pitch, and acquisition mode. Forty-two texture features were extracted by using an in-house-developed Matlab program. Two-tailed tests and false-detection analyses were performed with significant differences in texture features based on detector array configurations ( values = 0.001-0.006), section thickness ( values = 0.0002-0.001), and acquisition mode ( values = 0.003-0.006). Variations in milliampere and kilovolt had no significant effect.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

Buch¹,

Li²,

Qureshi

et al. 2017

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

show abstract

“…Because traditional visual inspection of images might not allow for recognizing subtle differences in textural information, the application of TA has the potential to further enhance the diagnostic and prognostic value of imaging. The increase of interest in evaluating TA in radiology is reflected by recent literature describing potential applications of the technique in oncologic (11)(12)(13), neuroradiologic (14), musculoskeletal (15,16), and lung imaging (17). To our knowledge, only one preliminary study, published as a conference abstract, has evaluated the potential of TA for cardiac MR imaging so far (18).…”

Section: Cardiac Mr Imaging Data Acquisitionmentioning

confidence: 99%

“…Texture Analysis TA represents a statistical image analysis technique that quantifies the texture of an image on the basis of pixel signal intensity distributions and relationships of values between neighboring pixels (16). Generally speaking, textures are complex patterns composed of entities or subpatterns that have characteristic brightness, slope, size, et cetera.…”

Section: Study Populationmentioning

confidence: 99%

Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images

et al. 2018

View full text Add to dashboard Cite

Purpose To test whether texture analysis (TA) allows for the diagnosis of subacute and chronic myocardial infarction (MI) on noncontrast material-enhanced cine cardiac magnetic resonance (MR) images. Materials and Methods In this retrospective, institutional review board-approved study, 120 patients who underwent cardiac MR imaging and showed large transmural (volume of enhancement on late gadolinium enhancement [LGE] images >20%, n = 72) or small (enhanced volume 20%, n = 48) subacute or chronic ischemic scars were included. Sixty patients with normal cardiac MR imaging findings served as control subjects. Regions of interest for TA encompassing the left ventricle were drawn by two blinded, independent readers on cine images in end systole by using a freely available software package.Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning, and correlation analyses were performed for selecting features, enabling the diagnosis of MI on nonenhanced cine MR images by using LGE imaging as the standard of reference. Results Five independent texture features allowed for differentiation between ischemic scar and normal myocardium on cine MR images in both subgroups: Teta1, Perc.01, Variance, WavEnHH.s-3, and S(5,5)SumEntrp (in patients with large MI: all P values < .001; in patients with small MI: Teta1 and Perc.01, P < .001; Variance, P = .026; WavEnHH.s-3, P = .007; S[5,5]SumEntrp, P = .045). Multiple logistic regression models revealed that combining the features Teta1 and Perc.01 resulted in the highest accuracy for diagnosing large and small MI on cine MR images, with an area under the curve of 0.93 and 0.92, respectively. Conclusion This proof-of-concept study indicates that TA of nonenhanced cine MR images allows for the diagnosis of subacute and chronic MI with high accuracy. (©) RSNA, 2017 Online supplemental material is available for this article. Purpose:To test whether texture analysis (TA) allows for the diagnosis of subacute and chronic myocardial infarction (MI) on noncontrast material-enhanced cine cardiac magnetic resonance (MR) images. Materials and Methods:In this retrospective, institutional review board-approved study, 120 patients who underwent cardiac MR imaging and showed large transmural (volume of enhancement on late gadolinium enhancement [LGE] images .20%, n = 72) or small (enhanced volume 20%, n = 48) subacute or chronic ischemic scars were included. Sixty patients with normal cardiac MR imaging findings served as control subjects. Regions of interest for TA encompassing the left ventricle were drawn by two blinded, independent readers on cine images in end systole by using a freely available software package. Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning, and correlation analyses were performed for selecting features, enabling the diagnosis of MI on nonenhanced cine MR images by using LGE imaging as the standard of reference. Results:Five independent texture features allowed ...

show abstract

“…TA represents different statistical image analysis techniques that quantify the texture of an image based on pixel signal intensity distributions and relationships of values between neighboring pixels (19). Unlike means and standard deviations that often assess more global characteristics, TA analyzes more locally to small neighborhoods in the images (20).…”

Section: Ta Of Imagesmentioning

confidence: 99%

Cardiac MRI Texture Analysis of T1 and T2 Maps in Patients with Infarctlike Acute Myocarditis

Baeßler¹,

Luecke²,

Lurz³

et al. 2018

Radiology

108

View full text Add to dashboard Cite

Purpose To assess the diagnostic potential of texture analysis applied to T1 and T2 maps obtained with cardiac MRI for the diagnosis of acute infarctlike myocarditis. Materials and Methods This prospective study from August 2012 to May 2015 included 39 participants (overall mean age ± standard deviation, 34.7 years ± 12.2 [range, 18-63 years]; mean age of women, 46.1 years ± 10.8 [range, 24-63 years]; mean age of men, 29.8 years ± 9.2 [range, 18-56 years]) from the Magnetic Resonance Imaging in Myocarditis (MyoRacer) trial with clinical suspicion of acute myocarditis and infarctlike presentation. Participants underwent biventricular endomyocardial biopsy, cardiac catheterization, and cardiac MRI at 1.5 T, in which native T1 and T2 mapping as well as Lake Louise criteria (LLC) were assessed. Texture analysis was applied on T1 and T2 maps by using a freely available software package. Stepwise dimension reduction and texture feature selection was performed for selecting features enabling the diagnosis of myocarditis by using endomyocardial biopsy as the reference standard. Results Endomyocardial biopsy confirmed the diagnosis of acute myocarditis in 26 patients, whereas 13 participants had no signs of acute inflammation. Mean T1 and T2 values and LLC showed a low diagnostic performance, with area under the curve in receiver operating curve analyses as follows: 0.65 (95% confidence interval [CI]: 0.45, 0.85) for T1, 0.67 (95% CI: 0.49, 0.85) for T2, and 0.62 (95% CI: 0.42, 0.79) for LLC. Combining the texture features T2 run-length nonuniformity and gray-level nonuniformity resulted in higher diagnostic performance with an area under the curve of 0.88 (95% CI: 0.73, 1.00) (P < .001) and a sensitivity and specificity of 89% [95% CI: 81%, 93%] and 92% [95% CI: 77%, 93%], respectively. Conclusion Texture analysis of T2 maps shows high sensitivity and specificity for the diagnosis of acute infarctlike myocarditis. © RSNA, 2018 Online supplemental material is available for this article.

show abstract

MRI texture analysis of subchondral bone at the tibial plateau

Cited by 26 publications

References 44 publications

Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images

Cardiac MRI Texture Analysis of T1 and T2 Maps in Patients with Infarctlike Acute Myocarditis

Contact Info

Product

Resources

About