Aberrant Motility in Unaffected Small Bowel is Linked to Inflammatory Burden and Patient Symptoms in Crohn's Disease.

BACKGROUND: Inflammation-related enteric dysmotility has been postulated as a cause for abdominal symptoms in Crohn's disease (CD). We investigated the relationship between magnetic resonance imaging-quantified small bowel (SB) motility, inflammatory activity, and patient symptom burden. METHODS: The Harvey-Bradshaw index (HBI) and fecal calprotectin were prospectively measured in 53 patients with CD (median age, 35; range, 18-78 years) the day before magnetic resonance enterography, which included a dynamic (cine), breath-hold motility sequence, repeated to encompass the whole SB volume. A validated registration-based motility quantitation technique produced motility maps, and regions of interest were drawn to include all morphologically normal SB (i.e., excluding diseased bowel). Global SB motility was correlated with calprotectin, HBI, and symptom components (well-being, pain, and diarrhea). Adjustment for age, sex, smoking, and surgical history was made using multivariate linear regression. RESULTS: Median calprotectin was 336 (range, 0-1280). Median HBI, motility mean, and motility variance were 3 (range, 0-16), 0.33 (0.18-0.51), and 0.01 (0.0014-0.034), respectively. Motility variance was significantly negatively correlated with calprotectin (rho = -0.33, P = 0.015), total HBI (rho = -0.45, P 0.05). CONCLUSIONS: Reduced motility variance in morphologically normal SB is associated with patient symptoms and fecal calprotectin levels, supporting the hypothesis that inflammation-related enteric dysmotility may explain refractory abdominal symptoms in CD

MRI texture analysis parameters of contrast-enhanced T1-weighted images of Crohn's disease differ according to the presence or absence of histological markers of hypoxia and angiogenesis

PURPOSE: To investigate if texture analysis parameters of contrast-enhanced MRI differ according to the presence of histological markers of hypoxia and angiogenesis in Crohn's disease (CD). METHODS: Seven CD patients (mean age 38 (19-75), 3 male)) undergoing ileal resection underwent 3T MR enterography including axial ultrafast spoiled gradient-echo T1 post IV gadolinium chelate. Regions of interest were placed in bowel destined for resection and registered to trans-mural histological sections (n = 28 across 7 bowel sections) via MRI of the resected specimen. Microvessel density (MVD) and staining for markers of hypoxia (HIF 1α) and angiogenesis (VEGF) were performed. Texture analysis features were derived utilizing an image filtration-histogram technique at spatial scaling factor (SSF) 0-6 mm, including mean, standard deviation, mean of positive pixels, entropy, kurtosis and skewness and compared according to the presence or absence of histological markers of hypoxia/angiogenesis using Mann-Whitney U/Kruskal-Wallis tests and with the log of MVD using simple linear regression. RESULTS: Mean, standard deviation and mean of positive pixels were significantly lower in sections expressing VEGF. For example at SSF 6 mm, median (inter-quartile range) of mean, standard deviation and mean of positive pixels in those with VEGF expression were 150.1 (134.7), 132.4 (49.2) and 184.0 (91.4) vs. 362.5 (150.2), 216.3 (100.1) and 416.6 (80.0) in those without (p = 0.001, p = 0.004 and p = 0.001), respectively. There was a significant association between skewness and MVD (ratio 1.97 (1.15-3.41)) at SSF = 2 mm. CONCLUSIONS: Contrast-enhanced MRI texture analysis features significantly differ according to the presence or absence of histological markers of hypoxia and angiogenesis in CD

Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI.

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement

Dual registration of abdominal motion for motility assessment in free-breathing data sets acquired using dynamic MRI.

At present, registration-based quantification of bowel motility from dynamic MRI is limited to breath-hold studies. Here we validate a dual-registration technique robust to respiratory motion for the assessment of small bowel and colonic motility. Small bowel datasets were acquired in breath-hold and free-breathing in 20 healthy individuals. A pre-processing step using an iterative registration of the low rank component of the data was applied to remove respiratory motion from the free breathing data. Motility was then quantified with an existing optic-flow (OF) based registration technique to form a dual-stage approach, termed Dual Registration of Abdominal Motion (DRAM). The benefit of respiratory motion correction was assessed by (1) assessing the fidelity of automatically propagated segmental regions of interest (ROIs) in the small bowel and colon and (2) comparing parametric motility maps to a breath-hold ground truth. DRAM demonstrated an improved ability to propagate ROIs through free-breathing small bowel and colonic motility data, with median error decreased by 90% and 55%, respectively. Comparison between global parametric maps showed high concordance between breath-hold data and free-breathing DRAM. Quantification of segmental and global motility in dynamic MR data is more accurate and robust to respiration when using the DRAM approach

MRI texture analysis (MRTA) of T2-weighted images in Crohn's disease may provide information on histological and MRI disease activity in patients undergoing ileal resection

OBJECTIVES: To associate MRI textural analysis (MRTA) with MRI and histological Crohn's disease (CD) activity. METHODS: Sixteen patients (mean age 39.5 years, 9 male) undergoing MR enterography before ileal resection were retrospectively analysed. Thirty-six small (≤3 mm) ROIs were placed on T2-weighted images and location-matched histological acute inflammatory scores (AIS) measured. MRI activity (mural thickness, T2 signal, T1 enhancement) (CDA) was scored in large ROIs. MRTA features (mean, standard deviation, mean of positive pixels (MPP), entropy, kurtosis, skewness) were extracted using a filtration histogram technique. Spatial scale filtration (SSF) ranged from 2 to 5 mm. Regression (linear/logistic) tested associations between MRTA and AIS (small ROIs), and CDA/constituent parameters (large ROIs). RESULTS: Skewness (SSF = 2 mm) was associated with AIS [regression coefficient (rc) 4.27, p = 0.02]. Of 120 large ROI analyses (for each MRI, MRTA feature and SSF), 15 were significant. Entropy (SSF = 2, 3 mm) and kurtosis (SSF = 3 mm) were associated with CDA (rc 0.9, 1.0, -0.45, p = 0.006-0.01). Entropy and mean (SSF = 2-4 mm) were associated with T2 signal [odds ratio (OR) 2.32-3.16, p = 0.02-0.004], [OR 1.22-1.28, p = 0.03-0.04]. MPP (SSF = 2 mm) was associated with mural thickness (OR 0.91, p = 0.04). Kurtosis (SSF = 3 mm), standard deviation (SSF = 5 mm) were associated with decreased T1 enhancement (OR 0.59, 0.42, p = 0.004, 0.007). CONCLUSIONS: MRTA features may be associated with CD activity

Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project)

Rationale and Objectives: The objective of this study was to develop and validate a predictive magnetic resonance imaging (MRI) activity score for ileocolonic Crohn disease activity based on both subjective and semiautomatic MRI features. Materials and Methods: An MRI activity score (the “virtual gastrointestinal tract [VIGOR]” score) was developed from 27 validated magnetic resonance enterography datasets, including subjective radiologist observation of mural T2 signal and semiautomatic measurements of bowel wall thickness, excess volume, and dynamic contrast enhancement (initial slope of increase). A second subjective score was developed based on only radiologist observations. For validation, two observers applied both scores and three existing scores to a prospective dataset of 106 patients (59 women, median age 33) with known Crohn disease, using the endoscopic Crohn's Disease Endoscopic Index of Severity (CDEIS) as a reference standard. Results: The VIGOR score (17.1 × initial slope of increase + 0.2 × excess volume + 2.3 × mural T2) and other activity scores all had comparable correlation to the CDEIS scores (observer 1: r = 0.58 and 0.59, and observer 2: r = 0.34–0.40 and 0.43–0.51, respectively). The VIGOR score, however, improved interobserver agreement compared to the other activity scores (intraclass correlation coefficient = 0.81 vs 0.44–0.59). A diagnostic accuracy of 80%–81% was seen for the VIGOR score, similar to the other scores. Conclusions: The VIGOR score achieves comparable accuracy to conventional MRI activity scores, but with significantly improved reproducibility, favoring its use for disease monitoring and therapy evaluation

Dual registration of abdominal motion for motility assessment in free-breathing data sets acquired using dynamic MRI

International audienceAt present, registration-based quantification of bowel motility from dynamic MRI is limited to breath-hold studies. Here we validate a dual-registration technique robust to respiratory motion for the assessment of small bowel and colonic motility. Small bowel datasets were acquired in breath-hold and free-breathing in 20 healthy individuals. A pre-processing step using an iterative registration of the low rank component of the data was applied to remove respiratory motion from the free breathing data. Motility was then quantified with an existing optic-flow (OF) based registration technique to form a dual-stage approach, termed Dual Registration of Abdominal Motion (DRAM). The benefit of respiratory motion correction was assessed by (1) assessing the fidelity of automatically propagated segmental regions of interest (ROIs) in the small bowel and colon and (2) comparing parametric motility maps to a breath-hold ground truth. DRAM demonstrated an improved ability to propagate ROIs through free-breathing small bowel and colonic motility data, with median error decreased by 90% and 55%, respectively. Comparison between global parametric maps showed high concordance between breath-hold data and free-breathing DRAM. Quantification of segmental and global motility in dynamic MR data is more accurate and robust to respiration when using the DRAM approach

Small bowel strictures in Crohn's disease:a quantitative investigation of intestinal motility using MR enterography

Intestinal stricturing and aberrant small bowel motility are common complications in patients with Crohn's disease (CD) leading to significant morbidity. A retrospective study was performed quantifying small bowel motility within and upstream of strictures in CD patients using magnetic resonance enterography (MRE)

Active learning based segmentation of Crohn's disease using principles of visual saliency

We propose a active learning (AL) approach to segment Crohn's disease (CD) affected regions in abdominal magnetic resonance (MR) images. Our label query strategy is inspired from the principles of visual saliency which has similar considerations for choosing the most salient region. These similarities are encoded in a graph using classification maps and low level features. The most informative node is determined using random walks. Experimental results on real patient datasets show the superior performance of our approach and highlight the importance of different features to determine a region's importance