Late lesion growth following endovascular therapy: is 24 hours too early to assess acute infarct size including the effects of secondary injury?

Introduction Stroke lesion volume on MRI or CT provides objective evidence of tissue injury as a consequence of ischemic stroke. Measurement of "final" lesion volume at 24hr following endovascular therapy (post-EVT) has been used in multiple studies as a surrogate for clinical outcome. However, despite successful recanalization, a significant proportion of patients do not experience favorable clinical outcome. The goals of this study were to quantify lesion growth during the first week after treatment, identify early predictors, and explore the association with clinical outcome. Methods This is a prospective study of stroke patients at two centers who met the following criteria: i) anterior large vessel occlusion (LVO) acute ischemic stroke, ii) attempted EVT, and iii) had 3T MRI post-EVT at 24hr and 5-day. We defined "Early" and "Late" lesion growth as ≥10mL lesion growth between baseline and 24hr DWI, and between 24hr DWI and 5-day FLAIR, respectively. Complete reperfusion was defined as >90% reduction of the volume of tissue with perfusion delay (Tmax>6sec) between pre-EVT and 24hr post-EVT. Favorable clinical outcome was defined as modified Rankin scale (mRS) of 0-2 at 30 or 90 days. Results One hundred twelve patients met study criteria with median age 67 years, 56% female, median admit NIHSS 19, 54% received IV or IA thrombolysis, 66% with M1 occlusion, and median baseline DWI volume 21.2mL. Successful recanalization was achieved in 87% and 68% had complete reperfusion, with an overall favorable clinical outcome rate of 53%. Nearly two thirds (65%) of the patients did not have Late lesion growth with a median volume change of -0.3mL between 24hr and 5-days and an associated high rate of favorable clinical outcome (64%). However, ~1/3 of patients (35%) did have significant Late lesion growth despite successful recanalization (87%: 46% mTICI 2b/ 41% mTICI 3). Late lesion growth patients had a 27.4mL change in Late lesion volume and 30.1mL change in Early lesion volume. These patients had an increased hemorrhagic transformation rate of 68% with only 1 in 3 patients having favorable clinical outcome. Late lesion growth was independently associated with incomplete reperfusion, hemorrhagic transformation, and unfavorable outcome. Conclusion Approximately 1 out of 3 patients had Late lesion growth following EVT, with a favorable clinical outcome occurring in only 1 out of 3 of these patients. Most patients with no Early lesion growth had no Late lesion growth. Identification of patients with Late lesion growth could be critical to guide clinical management and inform prognosis post-EVT. Additionally, it can serve as an imaging biomarker for the development of adjunctive therapies to mitigate reperfusion injury.

Association of Multiple Passes during Mechanical Thrombectomy with Incomplete Reperfusion and Lesion Growth.

INTRODUCTION: Despite complete recanalization by mechanical thrombectomy, abnormal perfusion can be detected on MRI obtained post-endovascular therapy (EVT). The presence of residual perfusion abnormalities post-EVT may be associated with blood-brain barrier breakdown in response to mechanical disruption of the endothelium from multiple-pass thrombectomy. We hypothesize that multiple-pass versus single-pass thrombectomy is associated with a higher rate of residual hypoperfusion and increased lesion growth at 24 h. MATERIALS AND METHODS: For this analysis, we included patients presenting to one of two stroke centers between January 2015 and February 2018 with an acute ischemic stroke within 12 h from symptom onset if they had a large vessel occlusion of the anterior circulation documented on magnetic resonance angiography or CTA, baseline MRI pre-EVT with imaging evidence of hypoperfusion, underwent EVT, and had a post-EVT MRI with qualitatively interpretable perfusion-weighted imaging data at 24 h. MRI Tmax maps using a time delay threshold of >6 s were used to quantitate hypoperfusion volumes. Residual hypoperfusion at 24 h was solely defined as Tmax volume >10 mL with >6 s delay. Complete recanalization was defined as modified treatment in cerebral infarction visualized on angiography at EVT completion. Hyperintense acute reperfusion injury marker was assessed on post-EVT pre-contrast fluid-attenuated inversion recovery at 24 h. Major early neurological improvement was defined as a reduction of the admission National Institutes of Health Stroke Scale by ≥8 points or a score of 0-1 at 24 h. Good functional outcome was defined as 0-2 on the modified Rankin Scale on day 30 or 90. RESULTS: Fifty-five patients were included with median age 67 years, 58% female, 45% Black/African American, 36% White/Caucasian, median admission National Institutes of Health Stroke Scale 19, large vessel occlusion locations: 71% M1, 14.5% iICA, 14.5% M2, 69% treated with intravenous recombinant tissue plasminogen activator. Of these, 58% had multiple-pass thrombectomy, 39% had residual perfusion abnormalities at 24 h, and 64% had severe hyperintense acute reperfusion injury marker at 24 h. After adjusting for complete recanalization, only multiple-pass thrombectomy (odds ratio, 4.3 95% CI, 1.07-17.2; p = 0.04) was an independent predictor of residual hypoperfusion at 24 h. Patients with residual hypoperfusion had larger lesion growth on diffusion-weighted imaging (59 mL vs. 8 mL, p < 0.001), lower rate of major early neurological improvement (24% vs. 70%, p = 0.002) at 24 h, and worse long-term outcome based on the modified Rankin Scale at 30 or 90 days, 5 versus 2 (p < 0.001). CONCLUSIONS: Our findings suggest that incomplete reperfusion on post-EVT MRI is present even in some patients with successful recanalization at the time of EVT and is associated with multiple-pass thrombectomy, lesion growth, and worse outcome. Future studies are needed to investigate whether patients with residual hypoperfusion may benefit from immediate adjunctive therapy to limit lesion growth and improve clinical outcome.

Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections.

Accurate and automated prostate whole gland and central gland segmentations on MR images are essential for aiding any prostate cancer diagnosis system. Our work presents a 2-D orthogonal deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial-only MR images. The proposed method can generate high-density 3-D surfaces from low-resolution ( z axis) MR images. In the past, most methods have focused on axial images alone, e.g., 2-D based segmentation of the prostate from each 2-D slice. Those methods suffer the problems of over-segmenting or under-segmenting the prostate at apex and base, which adds a major contribution for errors. The proposed method leverages the orthogonal context to effectively reduce the apex and base segmentation ambiguities. It also overcomes jittering or stair-step surface artifacts when constructing a 3-D surface from 2-D segmentation or direct 3-D segmentation approaches, such as 3-D U-Net. The experimental results demonstrate that the proposed method achieves 92.4 % ± 3 % Dice similarity coefficient (DSC) for prostate and DSC of 90.1 % ± 4.6 % for central gland without trimming any ending contours at apex and base. The experiments illustrate the feasibility and robustness of the 2-D-based holistically nested networks with short connections method for MR prostate and central gland segmentation. The proposed method achieves segmentation results on par with the current literature.

Analyzing Dendritic Morphology in Columns and Layers.

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks.

Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization. Second, we propose a method for end-to-end prostate segmentation by integrating holistically nested edge detection with fully convolutional networks. Holistically nested networks (HNN) automatically learn a hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 250 patients in fivefold cross-validation. The proposed enhanced HNN model achieves a mean ± standard deviation. A Dice similarity coefficient (DSC) of [Formula: see text] and a mean Jaccard similarity coefficient (IoU) of [Formula: see text] are used to calculate without trimming any end slices. The proposed holistic model significantly ([Formula: see text]) outperforms a patch-based AlexNet model by 9% in DSC and 13% in IoU. Overall, the method achieves state-of-the-art performance as compared with other MRI prostate segmentation methods in the literature.

Radio frequency ablation registration, segmentation, and fusion tool.

The radio frequency ablation segmentation tool (RFAST) is a software application developed using the National Institutes of Health's medical image processing analysis and visualization (MIPAV) API for the specific purpose of assisting physicians in the planning of radio frequency ablation (RFA) procedures. The RFAST application sequentially leads the physician through the steps necessary to register, fuse, segment, visualize, and plan the RFA treatment. Three-dimensional volume visualization of the CT dataset with segmented three dimensional (3-D) surface models enables the physician to interactively position the ablation probe to simulate burns and to semimanually simulate sphere packing in an attempt to optimize probe placement. This paper describes software systems contained in RFAST to address the needs of clinicians in planning, evaluating, and simulating RFA treatments of malignant hepatic tissue.

Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities.

Clinical research with medical imaging typically involves large-scale data analysis with interdependent software toolsets tied together in a processing workflow. Numerous, complementary platforms are available, but these are not readily compatible in terms of workflows or data formats. Both image scientists and clinical investigators could benefit from using the framework which is a most natural fit to the specific problem at hand, but pragmatic choices often dictate that a compromise platform is used for collaboration. Manual merging of platforms through carefully tuned scripts has been effective, but exceptionally time consuming and is not feasible for large-scale integration efforts. Hence, the benefits of innovation are constrained by platform dependence. Removing this constraint via integration of algorithms from one framework into another is the focus of this work. We propose and demonstrate a light-weight interface system to expose parameters across platforms and provide seamless integration. In this initial effort, we focus on four platforms Medical Image Analysis and Visualization (MIPAV), Java Image Science Toolkit (JIST), command line tools, and 3D Slicer. We explore three case studies: (1) providing a system for MIPAV to expose internal algorithms and utilize these algorithms within JIST, (2) exposing JIST modules through self-documenting command line interface for inclusion in scripting environments, and (3) detecting and using JIST modules in 3D Slicer. We review the challenges and opportunities for light-weight software integration both within development language (e.g., Java in MIPAV and JIST) and across languages (e.g., C/C++ in 3D Slicer and shell in command line tools).