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.

Objective: To evaluate a semi-automatic method for delineation of the bowel wall and measurement of the wall thickness in patients with Crohn's disease. Methods: 53 patients with suspected or proven Crohn's disease were selected. Two radiologists independently supervised the delineation of regions with active Crohn's disease on MRI, yielding manual annotations (Ano1, Ano2). Three observers manually measured the maximal bowel wall thickness of each annotated segment. An active contour segmentation approach semi-automatically delineated the bowel wall. For each active region, two segmentations (Seg1, Seg2) were obtained by independent observers, in which the maximum wall thickness was automatically determined. The overlap between (Seg1, Seg2) was compared with the overlap of (Ano1, Ano2) using Wilcoxon's signed rank test. The corresponding variances were compared using the Brown-Forsythe test. The variance of the semi-automatic thickness measurements was compared with the overall variance of manual measurements through an F-test. Furthermore, the intraclass correlation coefficient (ICC) of semiautomatic thickness measurements was compared with the ICC of manual measurements through a likelihood-ratio test. Results: Patient demographics: median age, 30 years; interquartile range, 25-38 years; 33 females. The median overlap of the semi-automatic segmentations (Seg1 vs Seg2: 0.89) was significantly larger than the median overlap of the manual annotations (Ano1 vs Ano2: 0.72); p=1.4×1025. The variance in overlap of the semiautomatic segmentations was significantly smaller than the variance in overlap of the manual annotations (p=1.1×1029). The variance of the semi-automated measurements (0.46mm2) was significantly smaller than the variance of the manual measurements (2.90mm2, p=1.1×1027). The ICC of semi-automatic measurement (0.88) was significantly higher than the ICC of manual measurement (0.45); p=0.005. Conclusion: The semi-automatic technique facilitates reproducible delineation of regions with active Crohn's disease. The semi-automatic thickness measurement sustains significantly improved interobserver agreement. Advances in knowledge: Automation of bowel wall thickness measurements strongly increases reproducibility of these measurements, which are commonly used in MRI scoring systems of Crohn's disease activity.

Purpose:
Simulating low-­dose Computed Tomography (CT) facilitates in-­silico studies into the required dose for a diagnostic task. Conventionally, low-­‐dose CT images are created by adding noise to the projection data. However, in practice the raw data is often simply not available. This paper presents a new method for simulating patient-­‐specific, low-dose CT images without the need
of the original projection data.

Methods:
The low-­dose CT simulation method included the following: (1) computation of a virtual sinogram from a high dose CT image through a
radon transform; (2) simulation of a 'reduced'­‐dose sinogram with appropriate
amounts of noise; (3) subtraction of the high-­‐dose virtual sinogram from the
reduced-­‐dose sinogram; (4) reconstruction of a noise volume via filtered back-projection; (5) addition of the noise image to the original high-dose image. The
required scanner-­Specific parameters, such as the apodization window, bowtie
filter, the X-ray tube output parameter (reflecting the photon flux) and the detector read-­out noise, were retrieved from calibration images of a water
cylinder. The low-­‐dose simulation method was evaluated by comparing the
noise characteristics in simulated images with experimentally acquired
data.

Results:
The models used to recover the scanner-­specific parameters fitted accurately to
the calibration data, and the values of the parameters were comparable to values
reported in literature. Finally, the simulated low-dose images accurately reproduced the noise characteristics in experimentally acquired low-dose­‐volumes.

Conclusion:
The developed methods truthfully simulate low-­dose CT imaging for a specific
scanner and reconstruction using filtered backprojection. The scanner-­‐specific
parameters can be estimated from calibration data.