A patch-based super resolution algorithm for improving image resolution in clinical mass spectrometry

Abstract Mass spectrometry imaging (MSI) and histology are complementary analytical tools. Integration of the two imaging modalities can enhance the spatial resolution of the MSI beyond its experimental limits. Patch-based super resolution (PBSR) is a method where high spatial resolution features from one image modality guide the reconstruction of a low resolution image from a second modality. The principle of PBSR lies in image redundancy and aims at finding similar pixels in the neighborhood of a central pixel that are then used to guide reconstruction of the central pixel. In this work, we employed PBSR to increase the resolution of MSI. We validated the proposed pipeline by using a phantom image (micro-dissected logo within a tissue) and mouse cerebellum samples. We compared the performance of the PBSR with other well-known methods: linear interpolation (LI) and image fusion (IF). Quantitative and qualitative assessment showed advantage over the former and comparability with the latter. Furthermore, we demonstrated the potential applicability of PBSR in a clinical setting by accurately integrating structural (i.e., histological) and molecular (i.e., MSI) information from a case study of a dog liver

Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury

Traumatic brain injury is a complex and diverse medical condition with a high frequency of intracranial abnormalities. These can typically be visualized on a computed tomography (CT) scan, which provides important information for further patient management, such as the need for operative intervention. In order to quantify the extent of acute intracranial lesions and associated secondary injuries, such as midline shift and cisternal compression, visual assessment of CT images has limitations, including observer variability and lack of quantitative interpretation. Automated image analysis can quantify the extent of intracranial abnormalities and provide added value in routine clinical practice. In this article, we present icobrain, a fully automated method that reliably computes acute intracranial lesions volume based on deep learning, cistern volume, and midline shift on the noncontrast CT image of a patient. The accuracy of our method is evaluated on a subset of the multi-center data set from the CENTER-TBI (Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury) study for which expert annotations were used as a reference. Median volume differences between expert assessments and icobrain are 0.07 mL for acute intracranial lesions and -0.01 mL for cistern segmentation. Correlation between expert assessments and icobrain is 0.91 for volume of acute intracranial lesions and 0.94 for volume of the cisterns. For midline shift computations, median error is -0.22 mm, with a correlation of 0.93 with expert assessments.status: publishe

Reliable measurements of brain atrophy in individual patients with multiple sclerosis

INTRODUCTION: As neurodegeneration is recognized as a major contributor to disability in multiple sclerosis (MS), brain atrophy quantification could have a high added value in clinical practice to assess treatment efficacy and disease progression, provided that it has a sufficiently low measurement error to draw meaningful conclusions for an individual patient. METHOD: In this paper, we present an automated longitudinal method based on Jacobian integration for measuring whole‐brain and gray matter atrophy based on anatomical magnetic resonance images (MRI), named MSmetrix. MSmetrix is specifically designed to measure atrophy in patients with MS, by including iterative lesion segmentation and lesion filling based on FLAIR and T1‐weighted MRI scans. RESULTS: MS metrix is compared with SIENA with respect to test–retest error and consistency, resulting in an average test–retest error on an MS data set of 0.13% (MS metrix) and 0.17% (SIENA) and a consistency error of 0.07% (MS metrix) and 0.05% (SIENA). On a healthy subject data set including physiological variability the test–retest is 0.19% (MS metrix) and 0.31% (SIENA). CONCLUSION: Therefore, we can conclude that MSmetrix could be of added value in clinical practice for the follow‐up of treatment and disease progression in MS patients

Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-\u3b2-induced pathology

An overview of 5 horizontal T2-weighted MRI scans at different levels of depth in the mouse brain on which the 23 different ROIs investigated in this study have been marked. (JPG 1511 kb

Additional file 3: of Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-β-induced pathology

Graphs showing the Pearson correlations between the MD, AD, and RD with the %O.D. 4G8, GFAP, IBA1, and MBP. The graph includes data from the WT mice (triangles) and APP/PS1 mice (circles) at 2 months of age (grey), 4 months of age (green), 6 months of age (blue), and 8 months of age (red). (JPG 469 kb

Additional file 1: of Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-β-induced pathology

The evolution of the weight of the WT and APP/PS1 mice in the longitudinal study at 2, 4, 6, and 8 months of age. (TIF 87 kb