A Feature-based Affine Registration Method for Capturing Background Lung Tissue Deformation for Ground Glass Nodule Tracking.

Lung nodule tracking assessment relies on cross-sectional measurements of the largest lesion profile depicted in initial and follow-up computed tomography (CT) images. However, apparent changes in nodule size assessed via simple image-based measurements may also be compromised by the effect of the background lung tissue deformation on the GGN between the initial and follow-up images, leading to erroneous conclusions about nodule changes due to disease. To compensate for the lung deformation and enable consistent nodule tracking, here we propose a feature-based affine registration method and study its performance vis-a-vis several other registration methods. We implement and test each registration method using both a lung- and a lesion-centered region of interest on ten patient CT datasets featuring twelve nodules, including both benign and malignant GGO lesions containing pure GGNs, part-solid, or solid nodules. We evaluate each registration method according to the target registration error (TRE) computed across 30 - 50 homologous fiducial landmarks surrounding the lesions and selected by expert radiologists in both the initial and follow-up patient CT images. Our results show that the proposed feature-based affine lesion-centered registration yielded a 1.1 ± 1.2 mm TRE, while a Symmetric Normalization deformable registration yielded a 1.2 ± 1.2 mm TRE, and a least-square fit registration of the 30-50 validation fiducial landmark set yielded a 1.5 ± 1.2 mm TRE. Although the deformable registration yielded a slightly higher registration accuracy than the feature-based affine registration, it is significantly more computationally efficient, eliminates the need for ambiguous segmentation of GGNs featuring ill-defined borders, and reduces the susceptibility of artificial deformations introduced by the deformable registration, which may lead to increased similarity between the registered initial and follow-up images, over-compensating for the background lung tissue deformation, and, in turn, compromising the true disease-induced nodule change assessment. We also assessed the registration qualitatively, by visual inspection of the subtraction images, and conducted a pilot pre-clinical study that showed the proposed feature-based lesion-centered affine registration effectively compensates for the background lung tissue deformation between the initial and follow-up images and also serves as a reliable baseline registration method prior to assessing lung nodule changes due to disease.

Progesterone Treatment Does Not Decrease Serum Levels of Biomarkers of Glial and Neuronal Cell Injury in Moderate and Severe Traumatic Brain Injury Subjects: A Secondary Analysis of the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment (ProTECT) III Trial.

Early treatment of moderate/severe traumatic brain injury (TBI) with progesterone does not improve clinical outcomes. This is in contrast with findings from pre-clinical studies of progesterone in TBI. To understand the reasons for the negative clinical trial, we investigated whether progesterone treatment has the desired biological effect of decreasing brain cell death. We quantified brain cell death using serum levels of biomarkers of glial and neuronal cell death (glial fibrillary acidic protein [GFAP], ubiquitin carboxy-terminal hydrolase-L1 [UCH-L1], S100 calcium-binding protein B [S100B], and Alpha II Spectrin Breakdown Product 150 [SBDP]) in the Biomarkers of Injury and Outcome-Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment (BIO-ProTECT) study. Serum levels of GFAP, UCHL1, S100B, and SBDP were measured at baseline (≤4 h post-injury and before administration of study drug) and at 24 and 48 h post-injury. Serum progesterone levels were measured at 24 and 48 h post-injury. The primary outcome of ProTECT was based on the Glasgow Outcome Scale-Extended assessed at 6 months post-randomization. We found that at baseline, there were no differences in biomarker levels between subjects randomized to progesterone treatment and those randomized to placebo (p > 0.10). Similarly, at 24 and 48 h post-injury, there were no differences in biomarker levels in the progesterone versus placebo groups (p > 0.15). There was no statistically significant correlation between serum progesterone concentrations and biomarker values obtained at 24 and 48 h. When examined as a continuous variable, baseline biomarker levels did not modify the association between progesterone treatment and neurological outcome (p of interaction term >0.39 for all biomarkers). We conclude that progesterone treatment does not decrease levels of biomarkers of glial and neuronal cell death during the first 48 h post-injury.

Deep Volumetric Segmentation of Murine Cochlear Compartments from Micro-Computed Tomography Images.

Local drug delivery to the inner ear via micropump implants has the potential to be much more effective than oral drug delivery for treating patients with sensorineural hearing loss and to protect hearing from ototoxic insult due to noise exposure or cancer treatments. Designing micropumps to deliver appropriate concentrations of drugs to the necessary cochlear compartments is of paramount importance; however, directly measuring local drug concentrations over time throughout the cochlea is not possible. Recent approaches for indirectly quantifying local drug concentrations in animal models capture a series of magnetic resonance (MR) or micro computed tomography (µCT) images before and after infusion of a contrast agent into the cochlea. These approaches require accurately segmenting important cochlear components (scala tympani (ST), scala media (SM) and scala vestibuli (SV)) in each scan and ensuring that they are registered longitudinally across scans. In this paper, we focus on segmenting cochlear compartments from µCT volumes using V-Net, a convolutional neural network (CNN) architecture for 3-D segmentation. We show that by modifying the V-Net architecture to decrease the numbers of encoder and decoder blocks and to use dilated convolutions enables extracting local estimates of drug concentration that are comparable to those extracted using atlas-based segmentation (3.37%, 4.81%, and 19.65% average relative error in ST, SM, and SV), but in a fraction of the time. We also test the feasibility of training our network on a larger MRI dataset, and then using transfer learning to perform segmentation on a smaller number of µCT volumes, which would enable this technique to be used in the future to characterize drug delivery in the cochlea of larger mammals.

Toward an Affine Feature-Based Registration Method for Ground Glass Lung Nodule Tracking.

Lung nodule progression assessment from medical imaging is a critical biomarker for assessing the course of the disease or the patient's response to therapy. CT images are routinely used to identify the location and size and rack the progression of lung nodules. However, nodule segmentation is challenging and prone to error, due to the irregular nodule boundaries, therefore introducing error in the lung nodule quantification process. Here, we describe the development and evaluation of a feature-based affine image registration framework that enables us to register two time point thoracic CT images as a means to account for the back-ground lung tissue deformation, then use digital subtraction images to assess tumor progression/regression. We have demonstrated this method on twelve de-identified patient datasets and showed that the proposed method yielded a better than 1.5mm registration accuracy vis-à-vis the widely accepted non-rigid image registration techniques. To demonstrate the potential clinical value of our described technique, we conducted a study in which our collaborating clinician was asked to provide an assessment of nodule progression/regression using the digital subtraction images post-registration. This assessment was consistent, yet provided more confidence, than the traditional lung nodule tracking based on visual analysis of the CT images.