multi-modal-pulmonary-imaging-using-complementary-information-from-ct

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized <sup>129</sup>Xe MRI to Evaluate Lung Structure-Function

Hyperpolarized 129Xe gas MRI is an emerging technique to evaluate and measure regional lung function including pulmonary gas distribution and gas ...

University of British Columbia

In MRI scans that are acquired in a slice-by-slice manner, patient motion during scanning can cause adjacent slices to overlap, resulting in duplicate coverage in some areas and missing coverage in others. Scans in which multiple slices are acquired simultaneously and interleaved with other sets of slices are particularly vulnerable because a single movement can result in the misalignment and overlap of many slices. Despite the fact that considerable data losses can occur even with few visible artifacts, this problem has received very little attention from MRI researchers. The primary goals of this paper are: (1) to raise awareness of the problem in the MRI community and (2) to present an efficient multiscale algorithm that accurately quantifies the amount of data loss. Validation of the algorithm's accuracy is performed on 200 scans with simulated patient motion so that the true amount of data loss is known for each scan. The motion parameters are chosen to simulate scans that have significant data loss (mean missing coverage=14.39% of head volume, SD=6.61%, range=2.76-32.98%) but with few visual indications of the problem. The algorithm is shown to be very accurate, yielding estimates that differ from the true values by a mean of only 1.1% point (SD=0.98pt, range=0.00-6.54pt). The algorithm is also shown to be consistent and robust when tested on a large set of scans from a recent multiple sclerosis clinical trial.

Detection and measurement of coverage loss in interleaved multi-acquisition brain MRIs due to motion-induced inter-slice misalignment.

The change in T 1-hypointense lesion ("black hole") volume is an important marker of pathological progression in multiple sclerosis (MS). Black hole boundaries often have low contrast and are difficult to determine accurately and most (semi-)automated segmentation methods first compute the T 2-hyperintense lesions, which are a superset of the black holes and are typically more distinct, to form a search space for the T 1w lesions. Two main potential sources of measurement noise in longitudinal black hole volume computation are partial volume and variability in the T 2w lesion segmentation. A paired analysis approach is proposed herein that uses registration to equalize partial volume and lesion mask processing to combine T 2w lesion segmentations across time. The scans of 247 MS patients are used to compare a selected black hole computation method with an enhanced version incorporating paired analysis, using rank correlation to a clinical variable (MS functional composite) as the primary outcome measure. The comparison is done at nine different levels of intensity as a previous study suggests that darker black holes may yield stronger correlations. The results demonstrate that paired analysis can strongly improve longitudinal correlation (from -0.148 to -0.303 in this sample) and may produce segmentations that are more sensitive to clinically relevant changes.

Improving the clinical correlation of multiple sclerosis black hole volume change by paired-scan analysis.

To develop a fast algorithm for computing myelin maps from multiecho T2 relaxation data using parallel computation with multicore CPUs and graphics processing units (GPUs).

Fast computation of myelin maps from MRI T₂ relaxation data using multicore CPU and graphics card parallelization.

Mixed pathology, particularly Alzheimer's disease with cerebrovascular lesions, is reported as the second most common cause of dementia. Research on mixed dementia typically includes people with a primary AD diagnosis and hence, little is known about the effects of co-existing amyloid pathology in people with vascular cognitive impairment (VCI). The purpose of this study was to understand whether individual differences in amyloid pathology might explain variations in cognitive impairment among individuals with clinical subcortical VCI (SVCI).

Exploring the effects of coexisting amyloid in subcortical vascular cognitive impairment.

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise -test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD = 8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD = 13.7%) and T1w measurements (66.7%, SD = 10.6%), or deep-learned features in either the myelin (83.8%, SD = 11.0%) or T1w (70.1%, SD = 13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.

The analysis of leukocyte recruitment in intravital video microscopy (IVM) is essential to the understanding of inflammatory processes. However, because IVM images often present a large variety of visual characteristics, it is hard for an expert human or even conventional machine learning techniques to detect and count the massive amount of cells and extract statistical measures precisely. Convolutional neural networks are a promising approach to overcome this problem, but due to the difficulty of labeling cells, large data sets with ground truth are rare. The present work explores an adaptation of the RetinaNet model with a suite of augmentation techniques and transfer learning for detecting leukocytes in IVM data. The augmentation techniques include simulating the Airy pattern and motion artifacts present in microscopy imaging, followed by traditional photometric, geometric and smooth elastic transformations to reproduce color and shape changes in cells. In addition, we analyzed the use of different network backbones, feature pyramid levels, and image input scales. We have found that even with limited data, our strategy not only enables training without overfitting but also boosts generalization performance. Among several experiments, the model reached a value of 94.84 for the average precision (AP) metric as our best outcome when using data from different image modalities. We also compared our results with conventional image processing techniques and open-source tools. The results showed an outstanding precision of the method compared with other approaches, presenting low error rates for cell counting and centroid distances. Code is available at: https://github.com/brunoggregorio/retinanet-cell-detection.

Detecting cells in intravital video microscopy using a deep convolutional neural network.

Chronic obstructive pulmonary disease (COPD) is underdiagnosed in the community. Thoracic CT scans are widely used for diagnostic and screening purposes for lung cancer. In this proof-of-concept study, we aimed to evaluate a software pipeline for the automated detection of COPD, based on deep learning and a dataset of low-dose CTs that were performed for early detection of lung cancer.

Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT.

Subcortical ischemic vascular cognitive impairment (SIVCI) is the most common form of vascular cognitive impairment. Importantly, SIVCI is considered the most treatable form of cognitive impairment in older adults, due to its modifiable risk factors such as hypertension, diabetes mellitus, and hypercholesterolemia. Exercise training is a promising intervention to delay the progression of SIVCI, as it actively targets these cardiometabolic risk factors. Despite the demonstrated benefits of resistance training on cognitive function and emerging evidence suggesting resistance training may reduce the progression of white matter hyperintensities (WMHs), research on SIVCI has predominantly focused on the use of aerobic exercise. Thus, the primary aim of this proof-of-concept randomized controlled trial is to investigate the efficacy of a 12-month, twice-weekly progressive resistance training program on cognitive function and WMH progression in adults with SIVCI. We will also assess the efficiency of the intervention.

Reshaping the path of vascular cognitive impairment with resistance training: a study protocol for a randomized controlled trial.

Myelin loss is a feature of cerebral small vessel disease (cSVD). Although physical activity levels may exert protective effects over cSVD pathology, its specific relationship with myelin content in people living with the cSVD is unknown. Thus, we investigated whether physical activity levels are associated with myelin in community-dwelling older adults with cSVD and mild cognitive impairment.

Myelin and Physical Activity in Older Adults With Cerebral Small Vessel Disease and Mild Cognitive Impairment.

We investigated whether myelin is associated with gait parameters in older adults with cerebral small vessel disease (cSVD). Cross-sectional data from sixty-four participants with cSVD and mild cognitive impairment were analyzed. Myelin was assessed via MRI multi-echo gradient and spin echo T relaxation sequence, indexed as myelin water fraction (MWF). Gait was assessed using an electronic walkway. Hierarchical regression models adjusting for total intracranial volume, age, sex, Mini-Mental State Examination, and body mass index were conducted to determine associations between MWF and gait parameters. Significant models were further adjusted for white matter hyperintensities. Sixty-four participants were included (mean [SD], age = 75.2y [5.4], 62.5% female). In adjusted models, lower MWF in the cingulum (p = 0.015), superior longitudinal fasciculus (p = 0.034), posterior corona radiata (p = 0.039), and body of the corpus callosum (p = 0.040) was associated with higher cycle time variability. White matter hyperintensities weakened these associations. Lower myelin in specific white matter tracts may contribute to higher gait variability, increasing the overall risk of mobility impairment.

Myelin Content and Gait Impairment in Older Adults with Cerebral Small Vessel Disease and Mild Cognitive Impairment.

Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In this study, we have compared three ML models to determine whether accounting for label uncertainty can improve a model's predictions.

Roger C Tam

School of Biomedical Engineering,
Department of Radiology

University of British Columbia

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

Roger C Tam

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}School of Biomedical Engineering,Department of Radiology

University of British Columbia

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

School of Biomedical Engineering,
Department of Radiology

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function