Mitigating undersampling errors in MR fingerprinting by sequence optimization.

PURPOSE: To develop a method for MR Fingerprinting (MRF) sequence optimization that takes both the applied undersampling pattern and a realistic reference map into account. METHODS: A predictive model for the undersampling error leveraging on perturbation theory was exploited to optimize the MRF flip angle sequence for improved robustness against undersampling artifacts. In this framework parameter maps from a previously acquired MRF scan were used as reference. Sequences were optimized for different sequence lengths, smoothness constraints and undersampling factors. Numerical simulations and in vivo measurements in eight healthy subjects were performed to assess the effect of the performed optimization. The optimized MRF sequences were compared to a conventionally shaped flip angle pattern and an optimized pattern based on the Cramér-Rao lower bound (CRB). RESULTS: Numerical simulations and in vivo results demonstrate that the undersampling errors can be suppressed by flip angle optimization. Analysis of the in vivo results show that a sequence optimized for improved robustness against undersampling with a flip angle train of length 400 yielded significantly lower median absolute errors in T 1 : 5 . 6 % ± 2 . 9 % and T 2 : 7 . 9 % ± 2 . 3 % compared to the conventional ( T 1 : 8 . 0 % ± 1 . 9 % , T 2 : 14 . 5 % ± 2 . 6 % ) and CRB-based ( T 1 : 21 . 6 % ± 4 . 1 % , T 2 : 31 . 4 % ± 4 . 4 % ) sequences. CONCLUSION: The proposed method is able to optimize the MRF flip angle pattern such that significant mitigation of the artifacts from strong k-space undersampling in MRF is achieved.

A supervised learning approach for Crohn's disease detection using higher-order image statistics and a novel shape asymmetry measure.

Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess rate of contrast enhancement and bowel wall thickness, and rely on extensive manual segmentation for accurate analysis. We propose a supervised learning method for the identification and localization of regions in abdominal magnetic resonance images that have been affected by CD. Low-level features like intensity and texture are used with shape asymmetry information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy-based shape asymmetry method and higher-order statistics like skewness and kurtosis. Multi-scale feature extraction renders the method robust. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.

Lesion conspicuity and efficiency of CT colonography with electronic cleansing based on a three-material transition model.

OBJECTIVE: The purpose of this article is to report the effect on lesion conspicuity and the practical efficiency of electronic cleansing for CT colonography (CTC). MATERIALS AND METHODS: Patients were included from the Walter Reed Army Medical Center public database. All patients had undergone extensive bowel preparation with fecal tagging. A primary 3D display method was used. For study I, the data consisted of all patients with polyps > or = 6 mm. Two experienced CTC observers (observer 1 and observer 2) scored the lesion conspicuity considering supine and prone positions separately. For study II, data consisted of 19 randomly chosen patients from the database. The same observers evaluated the data before and after electronic cleansing. Evaluation time, assessment effort, and observer confidence were recorded. RESULTS: In study I, there were 59 lesions partly or completely covered by tagged material (to be uncovered by electronic cleansing) and 70 lesions surrounded by air (no electronic cleansing required). The conspicuity did not differ significantly between lesions that were uncovered by electronic cleansing and lesions surrounded by air (observer 1, p < 0.5; observer 2, p < 0.6). In study II, the median evaluation time per patient after electronic cleansing was significantly shorter than for original data (observer 1, 20 reduced to 12 minutes; observer 2, 17 reduced to 12 minutes). Assessment effort was significantly smaller for both observers (p < 0.0000001), and observer confidence was significantly larger (observer 1, p < 0.007; observer 2, p < 0.0002) after electronic cleansing. CONCLUSION: Lesions uncovered by electronic cleansing have comparable conspicuity with lesions surrounded by air. CTC with electronic cleansing sustains a shorter evaluation time, lower assessment effort, and larger observer confidence than without electronic cleansing.

Active learning based segmentation of Crohns disease from abdominal MRI.

This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort.

Semi-supervised and active learning for automatic segmentation of Crohn's disease.

Our proposed method combines semi supervised learning (SSL) and active learning (AL) for automatic detection and segmentation of Crohn's disease (CD) from abdominal magnetic resonance (MR) images. Random forest (RF) classifiers are used due to fast SSL classification and capacity to interpret learned knowledge. Query samples for AL are selected by a novel information density weighted approach using context information, semantic knowledge and labeling uncertainty. Experimental results show that our proposed method combines the advantages of SSL and AL, and with fewer samples achieves higher classification and segmentation accuracy over fully supervised methods.

Automatic Detection and Segmentation of Crohn's Disease Tissues From Abdominal MRI.

We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.

Computational modeling for assessment of IBD: to be or not to be?

The grading of inflammatory bowel disease (IBD) severity is important to determine the proper treatment strategy and to quantify the response to treatment. Traditionally, ileocolonoscopy is considered the reference standard for assessment of IBD. However, the procedure is invasive and requires extensive bowel preparation. Magnetic resonance imaging (MRI) has become an important tool for determining the presence of disease activity. Unfortunately, only moderate interobserver agreement is reported for most of the radiological severity measures. There is a clear demand for automated evaluation of MR images in Crohn's disease (CD). This paper aims to introduce a preliminary suite of fundamental tools for assessment of CD severity. It involves procedures for image analysis, classification and visualization to predict the colonoscopy disease scores.