MRI texture analysis of subchondral bone at the tibial plateau

Three-dimensional texture analyses of multi-quantitative relaxation time maps for evaluating cartilage repair with the treatment of allogeneic human adipose-derived mesenchymal progenitor cells

BACKGROUND

To explore the ability of three-dimensional texture analyses based on gray-level run-length matrix (GLRLM) for examining the spatial distribution of pixel values on magnetic resonance imaging (MRI) relaxation time maps and detecting the compositional variation of cartilage repair following treatment with allogeneic human adipose-derived mesenchymal progenitor cells (haMPCs).

METHODS

Participants with knee osteoarthritis were randomly divided into three groups with intra-articular haMPCs injections: low-, medium-, and high-dose groups. We analyzed five GLRLM parameters in the T1rho, T2 and T2star maps, including run length non-uniformity (RLNonUni), gray-level non-uniformity (GLevNonU), long run emphasis (LngREmph), short run emphasis (ShrtREmp), and fraction of images in runs. We used the relative D values (the ratio of difference values to baseline) as the objective to avoid errors caused by individual differences. We calculated the two-tailed Pearson's linear correlation coefficient (r) to investigate the correlations of the texture parameters with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores.

RESULTS

Compared with the base time, significant reduction of WOMAC score was observed in both high and medium doses groups at terminal time, indicating relief of pain symptoms in high and medium groups with the treatment of allogeneic haMPCs. Significant differences were observed in the GLRLM parameters of cartilage MR relaxation time maps in different doses groups. In both T1rho and T2 relaxation time maps, the high-dose group showed significant increases in relative D values of RLNonUni, GLevNonU, LngREmph and ShrtREmp, which indicated significant changes in the uniformity of relaxation time maps. For T2star map, GLRLM parameters such as GLevNonU and ShrtREmp, especially LngREmph, showed significant increases in relative D values in high-dose group. Among all GLRLM features, LngREmph of three relaxation time maps had performed excellent linear correlations with WOMAC scores.

CONCLUSIONS

Texture analysis of the cartilage may allow the detection of compositional variation in cartilage repair with the treatment of allogeneic haMPCs. This technique displays potential applications in understanding the mechanism of stem cell repair of the cartilage and assessing the treatment response.

Muscle texture features on preoperative MRI for diagnosis and assessment of severity of congenital muscular torticollis

OBJECTIVES

To develop an objective method based on texture analysis on MRI for diagnosis of congenital muscular torticollis (CMT).

MATERIAL AND METHODS

The T1- and T2-weighted imaging, Q-dixon, and T1-mapping MRI data of 38 children with CMT were retrospectively analyzed. The region of interest (ROI) was manually drawn at the level of the largest cross-sectional area of the SCM on the affected side. MaZda software was used to obtain the texture features of the T2WI sequences of the ROI in healthy and affected SCM. A radiomics diagnostic model based on muscle texture features was constructed using logistic regression analysis. Fatty infiltration grade was calculated by hematoxylin and eosin staining, and fibrosis ratio by Masson staining. Correlation between the MRI parameters and pathological indicators was analyzed.

RESULTS

There was positive correlation between fatty infiltration grade and mean value, standard deviation, and maximum value of the Q-dixon sequence of the affected SCM (correlation coefficients, 0.65, 0.59, and 0.58, respectively, P < 0.05).Three muscle texture features-S(2,2)SumAverg, S(3,3)SumVarnc, and T2WI extreme difference-were selected to construct the diagnostic model. The model showed significant diagnostic value for CMT (P < 0.05). The area under the curve of the multivariate conditional logistic regression model was 0.828 (95% confidence interval 0.735-0.922); the sensitivity was 0.684 and the specificity 0.868.

CONCLUSION

The radiomics diagnostic model constructed using T2WI muscle texture features and MRI signal values appears to have good diagnostic efficiency. Q-dixon sequence can reflect the fatty infiltration grade of CMT.

CartiMorph: A framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient ρ∈[0.82,0.97]), surface area (ρ∈[0.82,0.98]) and volume (ρ∈[0.89,0.98]) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.

MRI texture analysis of acetabular cancellous bone can discriminate between normal, cam positive, and cam-FAI hips

OBJECTIVES

To compare the MRI texture profile of acetabular subchondral bone in normal, asymptomatic cam positive, and symptomatic cam-FAI hips and determine the accuracy of a machine learning model for discriminating between the three hip classes.

METHODS

A case-control, retrospective study was performed including 68 subjects (19 normal, 26 asymptomatic cam, 23 symptomatic cam-FAI). Acetabular subchondral bone of unilateral hip was contoured on 1.5 T MR images. Nine first-order 3D histogram and 16 s-order texture features were evaluated using specialized texture analysis software. Between-group differences were assessed using Kruskal-Wallis and Mann-Whitney U tests, and differences in proportions compared using chi-square and Fisher's exact tests. Gradient-boosted ensemble methods of decision trees were created and trained to discriminate between the three groups of hips, with percent accuracy calculated.

RESULTS

Sixty-eight subjects (median age 32 (28-40), 60 male) were evaluated. Significant differences among all three groups were identified with first-order (4 features, all p ≤ 0.002) and second-order (11 features, all p ≤ 0.002) texture analyses. First-order texture analysis could differentiate between control and cam positive hip groups (4 features, all p ≤ 0.002). Second-order texture analysis could additionally differentiate between asymptomatic cam and symptomatic cam-FAI groups (10 features, all p ≤ 0.02). Machine learning models demonstrated high classification accuracy of 79% (SD 16) for discriminating among all three groups.

CONCLUSION

Normal, asymptomatic cam positive, and cam-FAI hips can be discriminated based on their MRI texture profile of subchondral bone using descriptive statistics and machine learning algorithms.

CLINICAL RELEVANCE STATEMENT

Texture analysis can be performed on routine MR images of the hip and used to identify early changes in bone architecture, differentiating morphologically abnormal from normal hips, prior to onset of symptoms.

KEY POINTS

• MRI texture analysis is a technique for extracting quantitative data from routine MRI images. • MRI texture analysis demonstrates that there are different bone profiles between normal hips and those with femoroacetabular impingement. • Machine learning models can be used in conjunction with MRI texture analysis to accurately differentiate between normal hips and those with femoroacetabular impingement.

Texture analysis of native T1 images as a novel method for non-invasive assessment of heart failure with preserved ejection fraction in end-stage renal disease patients

OBJECTIVES

To explore the diagnostic potential of texture analysis applied to native T1 maps obtained from cardiac magnetic resonance (CMR) images for the assessment of heart failure with preserved ejection fraction (HFpEF) among patients with end-stage renal disease (ESRD).

METHODS

This study, conducted from June 2018 to November 2020, included 119 patients (35 on hemodialysis, 55 on peritoneal dialysis, and 29 with kidney transplants) in Renji Hospital. Native T1 maps were assessed with texture analysis, using a freely available software package, in participants who underwent cardiac MRI at 3.0 T. Four texture features, selected by dimension reduction specific to the diagnosis of HFpEF, were analyzed. Multivariate logistic regression was performed to examine the independent association between the selected features and HFpEF in ESRD patients.

RESULTS

Seventy-six of 119 patients were diagnosed with HFpEF. Demographic, laboratory, cardiac MRI, and echocardiogram characteristics were compared between HFpEF and non-HFpEF groups. The four texture features that were analyzed showed statistically significant differences between groups. In multivariate analysis, age, left atrial volume index (LAVI), and sum average 4 (SA4) turned out to be independent predictors for HFpEF in ESRD patients. Combining the texture feature, SA4, with typical predictive factors resulted in higher C-index (0.923 vs. 0.898, p = 0.045) and a sensitivity and specificity of 79.2% and 95.2%, respectively.

CONCLUSIONS

Texture analysis of T1 maps adds diagnostic value to typical clinical parameters for the assessment of heart failure with preserved ejection fraction in patients with end-stage renal disease.

KEY POINTS

• Non-invasive assessment of HFpEF can help predict prognosis in ESRD patients and help them take timely preventative measures. • Texture analysis of native T1 maps adds diagnostic value to the typical clinical parameters for the assessment of HFpEF in patients with ESRD.

Radiomics analysis using MR imaging of subchondral bone for identification of knee osteoarthritis

Background

To develop a magnetic resonance imaging (MRI)-based radiomics predictive model for the identification of knee osteoarthritis (OA), based on the tibial and femoral subchondral bone, and compare with the trabecular structural parameter-based model.

Methods

Eighty-eight consecutive knees were scanned with 3T MRI and scored using MRI osteoarthritis Knee Scores (MOAKS), in which 56 knees were diagnosed to have OA. The modality of sagittal three-dimensional balanced fast-field echo sequence (3D BFFE) was used to image the subchondral bone. Four trabecular structural parameters (bone volume fraction [BV/TV], trabecular thickness [Tb.Th], trabecular separation [Tb.Sp], and trabecular number) and 93 radiomics features were extracted from four regions of the lateral and medial aspects of the femur condyle and tibial plateau. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Machine learning-based support vector machine models were constructed to identify knee OA. The performance of the models was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC). The correlation between radiomics features and trabecular structural parameters was analyzed using Pearson’s correlation coefficient.

Results

Our radiomics-based classification model achieved the AUC score of 0.961 (95% confidence interval [CI], 0.912–1.000) when distinguishing between normal and knee OA, which was higher than that of the trabecular parameter-based model (AUC, 0.873; 95% CI, 0.788–0.957). The first-order, texture, and Laplacian of Gaussian-based radiomics features correlated positively with Tb.Th and BV/TV, but negatively with Tb.Sp (P < 0.05).

Conclusions

Our results suggested that our MRI-based radiomics models can be used as biomarkers for the classification of OA and are superior to the conventional structural parameter-based model.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-022-03314-y.

Evaluating the Impact of High Intensity Interval Training on Axial Psoriatic Arthritis Based on MR Images

High intensity interval training (HIIT) has been shown to benefit patients with psoriatic arthritis (PsA). However, magnetic resonance (MR) imaging has uncovered bone marrow edema (BME) in healthy volunteers after vigorous exercise. The purpose of this study was to investigate MR images of the spine of PsA patients for changes in BME after HIIT. PsA patients went through 11 weeks of HIIT (N = 19, 4 men, median age 52 years) or no change in physical exercise habits (N = 20, 8 men, median age 45 years). We acquired scores for joint affection and pain and short tau inversion recovery (STIR) and T1-weighted MR images of the spine at baseline and after 11 weeks. MR images were evaluated for BME by a trained radiologist, by SpondyloArthritis Research Consortium of Canada (SPARCC) scoring, and by extraction of textural features. No significant changes of BME were detected in MR images of the spine after HIIT. This was consistent for MR image evaluation by a radiologist, by SPARCC, and by texture analysis. Values of textural features were significantly different in BME compared to healthy bone marrow. In conclusion, BME in spine was not changed after HIIT, supporting that HIIT is safe for PsA patients.

Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy

BACKGROUND

To elucidate the value of texture analysis (TA) in detecting and differentiating myocardial tissue alterations on T2-weighted CMR (cardiovascular magnetic resonance imaging) in patients with cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM).

METHODS

In this retrospective study, 100 CA (58.5 ± 10.7 years; 41 (41%) females) and 217 HCM (50.7 ± 14.8 years, 101 (46.5%) females) patients who underwent CMR scans were included. Regions of interest for TA were delineated by two radiologists independently on T2-weighted imaging (T2WI). Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning algorithms, and correlation analyses were performed to select features. Both the CA and HCM groups were randomly divided into a training dataset and a testing dataset (7:3). After the TA model was established in the training set, the diagnostic performance of the model was validated in the testing set and further validated in a subgroup of patients with similar hypertrophy.

RESULTS

The 7 independent texture features provided, in combination, a diagnostic accuracy of 86.0% (AUC = 0.915; 95% CI 0.879-0.951) in the training dataset and 79.2% (AUC = 0.842; 95% CI 0.759-0.924) in the testing dataset. The differential diagnostic accuracy in the similar hypertrophy subgroup was 82.2% (AUC = 0.864, 95% CI 0.805-0.922). The significance of the difference between the AUCs of the TA model and late gadolinium enhancement (LGE) was verified by Delong's test (p = 0.898). All seven texture features showed significant differences between CA and HCM (all p < 0.001).

CONCLUSIONS

Our study demonstrated that texture analysis based on T2-weighted images could feasibly differentiate CA from HCM, even in patients with similar hypertrophy. The selected final texture features could achieve a comparable diagnostic capacity to the quantification of LGE. Trial registration Since this study is a retrospective observational study and no intervention had been involved, trial registration is waived.

A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone

Objectives

Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis.

Methods

The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC).

Results

Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87).

Conclusion

Our results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously.

Key Points

• Subchondral bone plays a role in the osteoarthritis disease processes.

• MRI radiomics is a potential method for quantifying changes in subchondral bone.

• Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07951-5.

Comparison between computed tomography and magnetic resonance imaging in clinical diagnosis and treatment of tibial platform fractures

BACKGROUND

Tibial plateau fracture is one of the common fracture types. It occurs mainly in teenagers and is usually caused by a fall. After the occurrence of fracture, knee swelling, pain, limited activity, etc. greatly affect the patient’s exercise habits and lifestyle. X-ray, computed tomography (CT) and magnetic resonance imaging (MRI) were used in this examination. X-rays are relatively new and easy to operate. However, there are some errors in the observation of fracture collapse and fracture displacement. In recent years, CT and MRI have been actively used to diagnose various types of clinical fractures. They have more diagnostic power than X-ray film. However, some scholars believe that CT is also prone to errors in clinical application. The volume effect leads to missed diagnosis and misdiagnosis in some cases, while the multidirection scanning of MRI technology can effectively overcome the shortcomings of CT. To facilitate the selection of clinical examination regimens, this study further observed the diagnostic ability of these two regimens in the diagnosis of tibial plateau fractures.

AIM

To explore the value of nuclear MRI and CT in the clinical diagnosis of tibial plateau fractures.

METHODS

A total of 120 patients with tibial plateau fractures admitted from September 2017 to August 2019 were included. All patients were examined by nuclear MRI and CT scanning. The results were sent to senior physicians in our hospital to complete the diagnosis.

RESULTS

Nuclear magnetic resonance showed the same effects as CT in four aspects: fracture displacement, bone defect, fracture site and fracture comminution. There was no significant difference in the score data (P > 0.05). Nuclear magnetic resonance and CT tended to be consistent in the B3, C2 and C3 fracture diagnosis coincidence rate, combined injury detection rate and fracture detection rate. The diagnostic coincidence rate of type B1, B2 and C1 fractures and the accuracy rate of overall fracture classification indicated that the MRI technique was significantly better than that of CT (P > 0.05).

CONCLUSION

MRI and CT have good diagnostic typing in the diagnosis of tibial plateau fractures, but MRI is more accurate and may be preferred.

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

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.

Texture features and pharmacokinetic parameters in differentiating benign and malignant breast lesions by dynamic contrast enhanced magnetic resonance imaging

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has become a powerful tool for the diagnosis of breast cancer in the clinical setting due to its high sensitivity and specificity. Pharmacokinetic parameters, including Ktrans and area under the curve (AUC), and texture features derived from DCE-MRI have been used to specify the characteristics inside tumors. In the present study, 56 patients (average age 45.3±11.1; range 25-69 years) with histopathologically proved breast tumors were analyzed using the pharmacokinetic parameters and texture features. Malignant tumors displayed higher Ktrans and AUC values than the benign, Ktrans exhibited a significantly difference between the malignant and benign tumors (P=0.001) compared with the AUC values (P=0.029); texture features from DCE-MRI images and pharmacokinetic parameter maps also showed a good diagnostic ability. Alongside the routine method, principal components analysis (PCA) and Fisher discriminant analysis (FDA) were employed on these texture features to differentiate the breast lesions automatically. The Factor-1 scores of PCA were used to divide the patients into two groups, and the diagnosing accuracies of the FDA method on the texture features from DCE-MRI images, Ktrans maps, AUC maps were 93, 98 and 98%, with a cross validation accuracies of 82, 77 and 77%, respectively. To conclude, pharmacokinetic parameters, texture features and the combined computer-assisted classification method were discussed. All method involved in this study may be a potential assisted tool for radiological analysis on breast.

Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study

Objectives

To assess whether initial or 12–18-month change in magnetic resonance imaging (MRI) subchondral bone texture is predictive of radiographic knee osteoarthritis (OA) progression over 36 months.

Methods

This was a nested case-control study including 122 knees/122 participants in the Osteoarthritis Initiative (OAI) Bone Ancillary Study, who underwent MRI optimised for subchondral bone assessment at either the 30- or 36-month and 48-month OAI visits. Case knees (n = 61) had radiographic OA progression between the 36- and 72-month OAI visits, defined as ≥ 0.7 mm minimum medial tibiofemoral radiographic joint space (minJSW) loss. Control knees (n = 61) without radiographic OA progression were matched (1:1) to cases for age, sex, body mass index and initial medial minJSW. Texture analysis was performed on the medial femoral and tibial subchondral bone. We assessed the association of texture features with radiographic progression by creating a composite texture score using penalised logistic regression and calculating odds ratios. We evaluated the predictive performance of texture features for predicting radiographic progression using c-statistics.

Results

Initial (odds ratio [95% confidence interval] = 2.13 [1.41–3.40]) and 12– 18-month change (3.76 [2.04–7.82]) texture scores were significantly associated with radiographic OA progression. Combinations of texture features were significant predictors of radiographic progression using initial (c-statistic [95% confidence interval] = 0.65 [0.64–0.65], p = 0.003) and 12–18-month change (0.68 [0.68-0.68], p < 0.001) data.

Conclusions

Initial and 12–18-month changes in MRI subchondral bone texture score were significantly associated with radiographic progression at 36 months, with better predictive performance for 12–18-month change in texture. These results suggest that texture analysis may be a useful biomarker of subchondral bone in OA.

Key Points

• Subchondral bone MRI texture analysis is a promising knee osteoarthritis imaging biomarker.

• In this study, subchondral bone texture was associated with knee osteoarthritis progression.

• This demonstrates predictive and concurrent validity of MRI subchondral bone texture analysis.

• This method may be useful in clinical trials with interventions targeting bone.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5444-9) contains supplementary material, which is available to authorized users.

Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results

PURPOSE

To test in a first proof-of-concept study whether texture analysis (TA) allows for the detection of myocardial tissue alterations in hypertrophic cardiomyopathy (HCM) on non-contrast T1-weighted cardiac magnetic resonance (CMR) images using machine learning based approaches.

METHODS

This retrospective, IRB-approved study included 32 patients with known HCM. Thirty patients with normal CMR served as controls. Regions-of-interest for TA encompassing the left ventricle were drawn on short-axis non-contrast T1-weighted images using a freely available software package. Step-wise dimension reduction and texture feature selection was performed for selecting features enabling the detection of myocardial tissue alterations in HCM patients on non-contrast T1-weighted CMR images.

RESULTS

Comparing HCM patients and controls, four texture features were identified showing significant differences between groups (Grey-level Non-uniformity [GLevNonU]: 74 ± 17 vs. 38 ± 9, p < .001; Energy of wavelet coefficients in low-frequency sub-bands [WavEnLL]: 58 ± 5 vs. 48 ± 10, p < .001; Fraction: 0.70 ± 0.07 vs. 0.78 ± 0.05, p < .001; Sum Average: 16.6 ± 0.4 vs. 17.0 ± 0.5, p = .007). A model containing the single parameter GLevNonU proved to be the best for differentiating between HCM patients and controls with a sensitivity/specificity of 91%/93%. A cut-off of GLevNonU ≥46 allowed for distinguishing HCM patients from controls with a sensitivity/specificity of 94%/90%. Even in patients without late gadolinium enhancement (LGE), the defined cut-off led to a differentiation of LGE- patients from healthy controls with 100% sensitivity and 90% specificity.

CONCLUSIONS

TA on non-contrast T1-weighted images allows for the detection of myocardial tissue alterations in the setting of HCM with excellent accuracy, delivering potential novel parameters for a non-contrast assessment of myocardial texture alterations.

Normative values for CT-based texture analysis of vertebral bodies in dual X-ray absorptiometry-confirmed, normally mineralized subjects

OBJECTIVES

To develop age-, gender-, and regional-specific normative values for texture analysis (TA) of spinal computed tomography (CT) in subjects with normal bone mineral density (BMD), as defined by dual X-ray absorptiometry (DXA), and to determine age-, gender-, and regional-specific differences.

MATERIALS AND METHODS

In this retrospective, IRB-approved study, TA was performed on sagittal CT bone images of the thoracic and lumbar spine using dedicated software (MaZda) in 141 individuals with normal DXA BMD findings. Numbers of female and male subjects were balanced in each of six age decades. Three hundred and five TA features were analyzed in thoracic and lumbar vertebrae using free-hand regions-of-interest. Intraclass correlation (ICC) coefficients were calculated for determining intra- and inter-observer agreement of each feature. Further dimension reduction was performed with correlation analyses.

RESULTS

The TA features with an ICC < 0.81 indicating compromised intra- and inter-observer agreement and with Pearson correlation scores r > 0.8 with other features were excluded from further analysis for dimension reduction. From the remaining 31 texture features, a significant correlation with age was found for the features mean (r = -0.489, p < 0.001), variance (r = -0.681, p < 0.001), kurtosis (r = 0.273, p < 0.001), and WavEnLL_s4 (r = 0.273, p < 0.001). Significant differences were found between genders for various higher-level texture features (p < 0.001). Regional differences among the thoracic spine, thoracic-lumbar junction, and lumbar spine were found for most TA features (p < 0.021).

CONCLUSION

This study established normative values of TA features on CT images of the spine and showed age-, gender-, and regional-specific differences in individuals with normal BMD as defined by DXA.

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

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.

Subchondral bone in osteoarthritis: association between MRI texture analysis and histomorphometry

OBJECTIVE

Magnetic resonance imaging (MRI) texture analysis is a method of analyzing subchondral bone alterations in osteoarthritis (OA). The objective of this study was to evaluate the association between MR texture analysis and ground-truth subchondral bone histomorphometry at the tibial plateau.

DESIGN

The local research ethics committee approved the study. All subjects provided written, informed consent. This was a cross-sectional study carried out at our institution between February and August 2014. Ten participants aged 57-84 with knee OA scheduled for total knee arthroplasty (TKA) underwent pre-operative MRI of the symptomatic knee at 3T using a high spatial-resolution coronal T1 weighted sequence. Tibial plateau explants obtained at the time of TKA underwent histological preparation to allow calculation of bone volume fraction (BV.TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp) and trabecular number (Tb.N). Texture analysis was performed on the tibial subchondral bone of MRI images matched to the histological sections. Regression models were created to assess the association of texture analysis features with BV.TV, Tb.Th, Tb.Sp and Tb.N.

RESULTS

MRI texture features were significantly associated with BV.TV (R² = 0.76), Tb.Th (R² = 0.47), Tb.Sp (R² = 0.75) and Tb.N (R² = 0.60, all P < 0.001). Simple gray-value histogram based texture features demonstrated the highest standardized regression coefficients for each model.

CONCLUSION

MRI texture analysis features were significantly associated with ground-truth subchondral bone histomorphometry at the tibial plateau.

Differences in tibial subchondral bone structure evaluated using plain radiographs between knees with and without cartilage damage or bone marrow lesions - the Oulu Knee Osteoarthritis study

Objectives

To investigate whether subchondral bone structure from plain radiographs is different between subjects with and without articular cartilage damage or bone marrow lesions (BMLs).

Methods

Radiography-based bone structure was assessed from 80 subjects with different stages of knee osteoarthritis using entropy of Laplacian-based image (E_Lap) and local binary patterns (E_LBP), homogeneity index of local angles (HI_Angles,mean), and horizontal (FD_Hor) and vertical fractal dimensions (FD_Ver). Medial tibial articular cartilage damage and BMLs were scored using the magnetic resonance imaging osteoarthritis knee score. Level of statistical significance was set to p < 0.05.

Results

Subjects with medial tibial cartilage damage had significantly higher FD_Ver and E_LBP as well as lower E_Lap and HI_Angles,mean in the medial tibial subchondral bone region than subjects without damage. FD_Hor, FD_Ver, and E_LBP were significantly higher, whereas E_Lap and HI_Angles,mean were lower in the medial trabecular bone region. Subjects with medial tibial BMLs had significantly higher FD_Ver and E_LBP as well as lower E_Lap and HI_Angles,mean in medial tibial subchondral bone. FD_Hor, FD_Ver, and E_LBP were higher, whereas E_Lap and HI_Angles,mean were lower in medial trabecular bone.

Conclusions

Our results support the use of bone structural analysis from radiographs when examining subjects with osteoarthritis or at risk of having it.

Key points

• Knee osteoarthritis causes changes in articular cartilage and subchondral bone

• Magnetic resonance imaging is a comprehensive imaging modality for knee osteoarthritis

• Radiography-based bone structure analysis can provide additional information of osteoarthritic subjects

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

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 t tests and false-detection analyses were performed with significant differences in texture features based on detector array configurations (Q values = 0.001-0.006), section thickness (Q values = 0.0002-0.001), and acquisition mode (Q values = 0.003-0.006). Variations in milliampere and kilovolt had no significant effect.