Biomedical subjects

Yue Min Zhu

Publications and source records attributed to Yue Min Zhu.

4 recordsLinked to original sources

PubMed2006

MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information.

A novel approach for correcting intensity nonuniformity in magnetic resonance imaging (MRI) is presented. This approach is based on the simultaneous use of spatial and gray-level histogram information. Spatial information about intensity nonuniformity is obtained using cubic B-spline smoothing. Gray-level histogram information of the image corrupted by intensity nonuniformity is exploited from a frequential point of view. The proposed correction method is illustrated using both physical phantom and human brain images. The results are consistent with theoretical prediction, and demonstrate a new way of dealing with intensity nonuniformity problems. They are all the more significant as the ground truth on intensity nonuniformity is unknown in clinical images.

Algorithms↗

PubMed2006

Computation of transmitted and received B1 fields in magnetic resonance imaging.

Computation of B1 fields is a key issue for determination and correction of intensity nonuniformity in magnetic resonance images. This paper presents a new method for computing transmitted and received B1 fields. Our method combines a modified MRI acquisition protocol and an estimation technique based on the Levenberg-Marquardt algorithm and spatial filtering. It enables accurate estimation of transmitted and received B1 fields for both homogeneous and heterogeneous objects. The method is validated using numerical simulations and experimental data from phantom and human scans. The experimental results are in agreement with theoretical expectations.

Algorithms↗

PubMed2005

Intensity non-uniformity correction in MRI: existing methods and their validation.

Magnetic resonance imaging is a popular and powerful non-invasive imaging technique. Automated analysis has become mandatory to efficiently cope with the large amount of data generated using this modality. However, several artifacts, such as intensity non-uniformity, can degrade the quality of acquired data. Intensity non-uniformity consists in anatomically irrelevant intensity variation throughout data. It can be induced by the choice of the radio-frequency coil, the acquisition pulse sequence and by the nature and geometry of the sample itself. Numerous methods have been proposed to correct this artifact. In this paper, we propose an overview of existing methods. We first sort them according to their location in the acquisition/processing pipeline. Sorting is then refined based on the assumptions those methods rely on. Next, we present the validation protocols used to evaluate these different correction schemes both from a qualitative and a quantitative point of view. Finally, availability and usability of the presented methods is discussed.

Algorithms↗

PubMed2004

A new method for analyzing histograms of brain magnetization transfer ratios: comparison with existing techniques.

BACKGROUND AND PURPOSE: Previously reported quantitative parameters for the magnetization transfer ratio (MTR) do not give identical results, which can limit their ability to differentiate normal from diseased tissue and render them vulnerable to variations among MR systems. Our purpose was to systematically study different MTR metrics; propose a new MTR histogram parameter, AMTR(2/3); and compare AMTR(2/3) with existing parameters in a study of multiple sclerosis (MS). METHODS: Seven conventional MTR parameters were proposed: global and mean MTR; peak height and position of the histogram; and percentiles MTR25, MTR50, and MTR75. Additionally, we investigated a parameter, AMTR(2/3), to indicate the normalized pixel count (area under the histogram curve) inside the band size of two-thirds MTR histogram peak height. All parameters were measured in 10 patients with relapsing-remitting MS (group A), 10 healthy control subjects from the same imaging center as that of patients (group B), and four healthy control subjects from an outside institution (group C). Comparison of findings was performed between groups A and B to assess the discriminating ability of MTR parameters and groups B and C to evaluate intersystem variations. RESULTS: All MTR parameters differed between groups A and B, but the difference was significant for only global MTR, mean MTR, MTR25, and AMTR(2/3). With the exception of AMTR(2/3), all parameters differed significantly between the two control groups. CONCLUSION: AMTR(2/3) is less sensitive to MR imaging system variations than are other MTR parameters and was most effective in differentiating patients with MS from healthy control subjects. This finding supports the use of AMTR(2/3) in multicenter MT MR imaging studies of MS.

Adult↗