Automated low-contrast pattern recognition algorithm for magnetic resonance image quality assessment.

PURPOSE: Low contrast (LC) detectability is a common test criterion for diagnostic radiologic quality control (QC) programs. Automation of this test is desirable in order to reduce human variability and to speed up analysis. However, automation is challenging due to the complexity of the human visual perception system and the ability to create algorithms that mimic this response. This paper describes the development and testing of an automated LC detection algorithm for use in the analysis of magnetic resonance (MR) images of the American College of Radiology (ACR) QC phantom. METHODS: The detection algorithm includes fuzzy logic decision processes and various edge detection methods to quantify LC detectability. Algorithm performance was first evaluated using a single LC phantom MR image with the addition of incremental zero mean Gaussian noise resulting in a total of 200 images. A c-statistic was calculated to determine the role of CNR to indicate when the algorithm would detect ten spokes. To evaluate inter-rater agreement between experienced observers and the algorithm, a blinded observer study was performed on 196 LC phantom images acquired from nine clinical MR scanners. The nine scanners included two MR manufacturers and two field strengths (1.5 T, 3.0 T). Inter-rater and algorithm-rater agreement was quantified using Krippendorff's alpha. RESULTS: For the Gaussian noise added data, CNR ranged from 0.519 to 11.7 with CNR being considered an excellent discriminator of algorithm performance (c-statistic = 0.9777). Reviewer scoring of the clinical phantom data resulted in an inter-rater agreement of 0.673 with the agreement between observers and algorithm equal to 0.652, both of which indicate significant agreement. CONCLUSIONS: This study demonstrates that the detection of LC test patterns for MR imaging QC programs can be successfully developed and that their response can model the human visual detection system of expert MR QC readers.

Magnetic resonance assessment of parenchymal elasticity in normal and edematous, ventilator-injured lung.

Magnetic resonance elastography (MRE) is a MR imaging method capable of spatially resolving the intrinsic mechanical properties of normal lung parenchyma. We tested the hypothesis that the mechanical properties of edematous lung exhibit local properties similar to those of a fluid-filled lung at transpulmonary pressures (P(tp)) up to 25 cm H(2)O. Pulmonary edema was induced in anesthetized female adult Sprague-Dawley rats by mechanical ventilation to a pressure of 40 cm H(2)O for ≈ 30 min. Prior to imaging the wet weight of each ex vivo lung set was measured. MRE, high-resolution T(1)-weighted spin echo and T(2)* gradient echo data were acquired at each P(tp) for both normal and injured ex vivo lungs. At P(tp)s of 6 cm H(2)O and greater, the shear stiffness of normal lungs was greater than injured lungs (P ≤ 0.0003). For P(tp)s up to 12 cm H(2)O, shear stiffness was equal to 1.00, 1.07, 1.16, and 1.26 kPa for the injured and 1.31, 1.89, 2.41, and 2.93 kPa for normal lungs at 3, 6, 9, and 12 cm H(2)O, respectively. For injured lungs MRE magnitude signal and shear stiffness within regions of differing degrees of alveolar flooding were calculated as a function of P(tp). Differences in shear stiffness were statistically significant between groups (P < 0.001) with regions of lower magnitude signal being stiffer than those of higher signal. These data demonstrate that when the alveolar space filling material is fluid, MRE-derived parenchymal shear stiffness of the lung decreases, and the lung becomes inherently softer compared with normal lung.

Sparsity and low-contrast object detectability.

The application of sparsity-driven reconstruction methods to MRI to date has largely focused on situations where high-contrast features (e.g., gadolinium-enhanced vessels) are of primary interest. In clinical practice, however, low contrast features such as subtle lesions are often of equal or greater interest. Using an American College of Radiology MR quality assurance phantom and test, we describe a novel framework for systematically and automatically evaluating the low-contrast object detectability performance of different undersampled image reconstruction methods. This platform is used to evaluate three such methods, two based on classic Tikhonov regularization and one sparsity-driven method based on ℓ(1) -norm minimization (which is commonly used in compressive sensing, also known as compressed sensing, applications), across a wide range of sampling rates and parameterizations. Both the automated evaluation system and a manual evaluation of anatomical images with numerically-generated low contrast inserts demonstrate that sparse reconstructions exhibit superior low-contrast object detectability performance compared to both Tikhonov-regularized reconstructions. The implications of this result, and potential applications of both the described low-contrast object detectability platform and generalizations of it are then discussed.