Image analysis methods for assessing levels of image plane nonuniformity and stochastic noise in a magnetic resonance image of a homogeneous phantom

Abstract

Magnetic response image plane nonuniformity and stochastic noise are properties that greatly influence the outcome of quantitative magnetic resonance imaging (MRI) evaluations such as gel dosimetry measurements using MRI. To study these properties, robust and accurate image analysis methods are required. New nonuniformity level assessment methods were designed, since previous methods were found to be insufficiently robust and accurate. The new and previously reported nonuniformity level assessment methods were analyzed with respect to, for example, insensitivity to stochastic noise; and previously reported stochastic noise level assessment methods with respect to insensitivity to nonuniformity. Using the same image data, different methods were found to assess significantly different levels of nonuniformity. Nonuniformity levels obtained using methods that count pixels in an intensity interval, and obtained using methods that use only intensity values, were found not to be comparable. The latter were found preferable, since they assess the quantity intrinsically sought. A new method which calculates a deviation image, with every pixel representing the deviation from a reference intensity, was least sensitive to stochastic noise. Furthermore, unlike any other analyzed method, it includes all intensity variations across the phantom area and allows for studies of nonuniformity shapes. This new method was designed for accurate studies of nonuniformities in gel dosimetry measurements, but could also be used with benefit in quality assurance and acceptance testing of MRI, scintillation camera, and computer tomography systems. The stochastic noise level was found to be greatly method dependent. Two methods were found to be insensitive to nonuniformity and also simple to use in practice. One method assesses the stochastic noise level as the average of the levels at five different positions within the phantom area, and the other assesses the stochastic noise in a region outside the phantom area.

Simple methods for the correction of T2 maps of phantoms

“…The CV of the T 2 values in a region of interest (ROI) placed on a T 2 map of a uniform phantom is a way of characterizing the in‐slice variations of T 2 . Other methods have been described recently (9). Analyses performed on the T 2 maps are summarized in Table 1, where the CV before and after correction are tabulated for each phantom.…”

Establishment and Results of a Magnetic Resonance Quality Assurance Program for the Pediatric Brain Tumor Consortium

“…It is thus desirable for all sites to produce sufficiently high and relatively similar image intensity uniformities. Detailed knowledge of image intensity uniformity also plays an important role in calculating quantitative gel dosimetry measurements (14) utilized in radiation treatment planning. In addition, image intensity uniformity will likely be influenced by the growing use of multiple-receiver coil arrays and associated parallel imaging techniques.…”

MR Scanner Systems Should Be Adequately Characterized in Diffusion-MRI of the Breast

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