Retrospective correction of intensity inhomogeneity with sparsity constraints in transform-domain: Application to brain MRI

Jointly estimating bias field and reconstructing uniform MRI image by deep learning

Bias field is one of the main artifacts that degrade the quality of magnetic resonance images. It introduces intensity inhomogeneity and affects image analysis such as segmentation. In this work, we proposed a deep learning approach to jointly estimate bias field and reconstruct uniform image. By modeling the quality degradation process as the product of a spatially varying field and a uniform image, the network was trained on 800 images with true bias fields from 12 healthy subjects. A network structure of bias field estimation and uniform image reconstruction was designed to compensate for the intensity loss. To further evaluate the benefit of bias field correction, a quantitative analysis was made on image segmentation. Experimental results show that the proposed BFCNet improves the image uniformity by 8.3% and 10.1%, the segmentation accuracy by 4.1% and 6.8% on white and grey matter in T2-weighted brain images. Moreover, BFCNet outperforms the state-of-the-art traditional methods and deep learning methods on estimating bias field and preserving image structure, and BFCNet is robust to different levels of bias field and noise.

AVLSM: Adaptive Variational Level Set Model for Image Segmentation in the Presence of Severe Intensity Inhomogeneity and High Noise

Intensity inhomogeneity and noise are two common issues in images but inevitably lead to significant challenges for image segmentation and is particularly pronounced when the two issues simultaneously appear in one image. As a result, most existing level set models yield poor performance when applied to this images. To this end, this paper proposes a novel hybrid level set model, named adaptive variational level set model (AVLSM) by integrating an adaptive scale bias field correction term and a denoising term into one level set framework, which can simultaneously correct the severe inhomogeneous intensity and denoise in segmentation. Specifically, an adaptive scale bias field correction term is first defined to correct the severe inhomogeneous intensity by adaptively adjusting the scale according to the degree of intensity inhomogeneity while segmentation. More importantly, the proposed adaptive scale truncation function in the term is model-agnostic, which can be applied to most off-the-shelf models and improves their performance for image segmentation with severe intensity inhomogeneity. Then, a denoising energy term is constructed based on the variational model, which can remove not only common additive noise but also multiplicative noise often occurred in medical image during segmentation. Finally, by integrating the two proposed energy terms into a variational level set framework, the AVLSM is proposed. The experimental results on synthetic and real images demonstrate the superiority of AVLSM over most state-of-the-art level set models in terms of accuracy, robustness and running time.

Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI

BACKGROUND

Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time.

OBJECTIVE

In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR).

METHODS

The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods.

RESULTS

Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil.

CONCLUSION

The VSSR may be considered suitable for general implementation.

7-T MRI for brain virtual autopsy: a proof of concept in comparison to 3-T MRI and CT

The detection and assessment of cerebral lesions and traumatic brain injuries are of particular interest in forensic investigations in order to differentiate between natural and traumatic deaths and to reconstruct the course of events in case of traumatic deaths. For this purpose, computed tomography (CT) and magnetic resonance imaging (MRI) are applied to supplement autopsy (traumatic death) or to supplant autopsy (natural deaths). This approach is termed “virtual autopsy.” The value of this approach increases as more microlesions and traumatic brain injuries are detected and assessed. Focusing on these findings, this article describes the examination of two decedents using CT, 3-T, and 7-T MRI. The main question asked was whether there is a benefit in using 7-T over 3-T MRI. To answer this question, the 3-T and 7-T images were graded regarding the detectability and the assessability of coup/contrecoup injuries and microlesions using 3-point Likert scales. While CT missed these findings, they were detectable on 3-T and 7-T MRI. However, the 3-T images appeared blurry in direct comparison with the 7-T images; thus, the detectability and assessability of small findings were hampered on 3-T MRI. The potential benefit of 7-T over 3-T MRI is discussed.