Low-field nuclear magnetic resonance (NMR) is a widely employed technique in geoscience. However, signal-to-noise ratio (SNR) is always an issue in low-field NMR measurement, which should be carefully addressed to ensure the accuracy of relaxation spectrum reconstruction for subsequent petro-physical interpretation and applications. This paper presents a novel denoising method for low-field NMR measurements utilizing double sparsity dictionary learning (DSDL), which is an unsupervised machine learning approach. The elaborate trained dictionary models could be directly implemented to denoise raw spin echoes with different signal-to-noise ratios (SNRs). After denoising, digital phase-sensitive detection (DPSD) and phase rotation are conducted to obtain the multi-exponential decay signals and fundamental noise signals for subsequent spectrum reconstruction. In this study, numerical simulations is mainly conducted. The pre-set T2 spectrum models are built to derive raw spin echoes with different SNRs through forward modeling, and then are used to train the double sparsity dictionary models. The dictionary models are trained on raw echo datasets with different Gaussian distributed noise and same porosity, and tested on raw echo datasets with same Gaussian noise and different porosities. The echo data before and after denosing are all inverted by using Singular Value Decomposition (SVD) method, which is a non-objective inversion algorithm to avoid the parameter selection like commonly used regularization inversion algorithm. All the inverted results are compared with the forwarding spectrum models. It is demonstrated that the DSDL method could effectively improve the quality of low-field NMR measurements, resulting in accurate relaxation spectrum.

Accurate tissue segmentation of infant brain in magnetic resonance (MR) images is crucial for charting early brain development and identifying biomarkers. Due to ongoing myelination and maturation, in the isointense phase (6-9 months of age), the gray and white matters of infant brain exhibit similar intensity levels in MR images, posing significant challenges for tissue segmentation. Meanwhile, in the adult-like phase around 12 months of age, the MR images show high tissue contrast and can be easily segmented. In this paper, we propose to effectively exploit adult-like phase images to achieve robust multi-view isointense infant brain segmentation. Specifically, in one way, we transfer adult-like phase images to the isointense view, which have similar tissue contrast as the isointense phase images, and use the transferred images to train an isointense-view segmentation network. On the other way, we transfer isointense phase images to the adult-like view, which have enhanced tissue contrast, for training a segmentation network in the adult-like view. The segmentation networks of different views form a multi-path architecture that performs multi-view learning to further boost the segmentation performance. Since anatomy-preserving style transfer is key to the downstream segmentation task, we develop a Disentangled Cycle-consistent Adversarial Network (DCAN) with strong regularization terms to accurately transfer realistic tissue contrast between isointense and adult-like phase images while still maintaining their structural consistency. Experiments on both NDAR and iSeg-2019 datasets demonstrate a significant superior performance of our method over the state-of-the-art methods.

Many deep learning based methods have been proposed for brain tumor segmentation. Most studies focus on deep network internal structure to improve the segmentation accuracy, while valuable external information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists often screen lesion regions with normal appearance as reference in mind, in this paper, we propose a novel deep framework for brain tumor segmentation, where normal brain images are adopted as reference to compare with tumor brain images in a learned feature space. In this way, features at tumor regions, i.e., tumor-related features, can be highlighted and enhanced for accurate tumor segmentation. It is known that routine tumor brain images are multimodal, while normal brain images are often monomodal. This causes the feature comparison a big issue, i.e., multimodal vs. monomodal. To this end, we present a new feature alignment module (FAM) to make the feature distribution of monomodal normal brain images consistent/inconsistent with multimodal tumor brain images at normal/tumor regions, making the feature comparison effective. Both public (BraTS2022) and in-house tumor brain image datasets are used to evaluate our framework. Experimental results demonstrate that for both datasets, our framework can effectively improve the segmentation accuracy and outperforms the state-of-the-art segmentation methods. Codes are available at https://github.com/hb-liu/Normal-Brain-Boost-Tumor-Segmentation.

Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art performance in many medical image segmentation tasks, e.g., brain parcellation. In DL-MA methods, atlas-target correspondence is the key for accurate segmentation. In most existing DL-MA methods, such correspondence is usually established using traditional or deep learning based registration methods at image level with no further feature level adaption. This could cause possible atlas-target feature inconsistency. As a result, the information from atlases often has limited positive and even counteractive impact on the final segmentation results. To tackle this issue, in this paper, we propose a new DL-MA framework, where a novel differentiable atlas feature warping module with a new smooth regularization term is presented to establish feature level atlas-target correspondence. Comparing with the existing DL-MA methods, in our framework, atlas features containing anatomical prior knowledge are more relevant to the target image feature, leading the final segmentation results to a high accuracy level. We evaluate our framework in the context of brain parcellation using two public MR brain image datasets: LPBA40 and NIREP-NA0. The experimental results demonstrate that our framework outperforms both traditional multi-atlas segmentation (MAS) and state-of-the-art DL-MA methods with statistical significance. Further ablation studies confirm the effectiveness of the proposed differentiable atlas feature warping module.

Currently, commercial downhole nuclear magnetic resonance (NMR) tools can measure depth (along with borehole axis) and radial (perpendicular with borehole axis) profile information simultaneously. However, in many cases, the lack of information due to the strong heterogeneity, partial invasion of the drilling mud around the borehole or borehole collapse will have a serious impact on applications. In order to ensure the validity of each measurement, acquisition of azimuthal information is as important as the depth and radial profile information. In this paper, we described the performance and response simulation of an innovative centralized downhole NMR scanning probe (SMRT). This probe uses hollow cylinder magnets as main magnets to produce azimuthally symmetrical polarized (related to the z-axis) static magnetic field B0. Circular focusing magnets and high permeability materials are added between the main magnets to simultaneously adjust the field homogeneity along with z-axis, increase the height of sensitive region and enhance the strength of B0field in sensitive region. Coil array is composed of eight independent coil units with the same performance but different azimuthal selection function used to scan the formation around the probe so that eight azimuthally distinguishable sensitive regions can be investigated. Magnets and coil array configuration are optimized with the finite-element method, and B0field and B1field is calculated to obtain the spin dynamic response. Numerical simulation results show that NMR signals from different azimuthal sections had no overlapped feature (sensitive volume of each azimuthal direction is thin shell of arc length approximate 45°) and high azimuthal resolution is feasible.