The common practice of inserting as many coils as possible in cerebral aneurysms is sensible in trying to avoid compaction. In aneurysms with packing of 24% or more, no compaction occurred at 6-month angiographic follow-up. In aneurysms with a volume of more than 600 mm(3), high packing could not be achieved, which resulted in compaction in the majority of aneurysms.
Background and Purpose— Recent randomized trials have proven the benefit of intra-arterial treatment (IAT) with retrievable stents in acute ischemic stroke. Patients with poor or absent collaterals (preexistent anastomoses to maintain blood flow in case of a primary vessel occlusion) may gain less clinical benefit from IAT. In this post hoc analysis, we aimed to assess whether the effect of IAT was modified by collateral status on baseline computed tomographic angiography in the Multicenter Randomized Clinical Trial of Endovascular Treatment of Acute Ischemic Stroke in the Netherlands (MR CLEAN). Methods— MR CLEAN was a multicenter, randomized trial of IAT versus no IAT. Primary outcome was the modified Rankin Scale at 90 days. The primary effect parameter was the adjusted common odds ratio for a shift in direction of a better outcome on the modified Rankin Scale. Collaterals were graded from 0 (absent) to 3 (good). We used multivariable ordinal logistic regression analysis with interaction terms to estimate treatment effect modification by collateral status. Results— We found a significant modification of treatment effect by collaterals ( P =0.038). The strongest benefit (adjusted common odds ratio 3.2 [95% confidence intervals 1.7–6.2]) was found in patients with good collaterals (grade 3). The adjusted common odds ratio was 1.6 [95% confidence intervals 1.0–2.7] for moderate collaterals (grade 2), 1.2 [95% confidence intervals 0.7–2.3] for poor collaterals (grade 1), and 1.0 [95% confidence intervals 0.1–8.7] for patients with absent collaterals (grade 0). Conclusions— In MR CLEAN, baseline computed tomographic angiography collateral status modified the treatment effect. The benefit of IAT was greatest in patients with good collaterals on baseline computed tomographic angiography. Treatment benefit appeared less and may be absent in patients with absent or poor collaterals. Clinical Trial Registration— URL: http://www.trialregister.nl and http://www.controlled-trials.com . Unique identifier: (NTR)1804 and ISRCTN10888758, respectively.
Background 18F‐fluoro‐2‐deoxy‐D‐Glucose positron emission tomography (18F‐FDG PET) radiomics has the potential to guide the clinical decision making in cancer patients, but validation is required before radiomics can be implemented in the clinical setting. The aim of this study was to explore how feature space reduction and repeatability of 18F‐FDG PET radiomic features are affected by various sources of variation such as underlying data (e.g., object size and uptake), image reconstruction methods and settings, noise, discretization method, and delineation method. Methods The NEMA image quality phantom was scanned with various sphere‐to‐background ratios (SBR), simulating different activity uptakes, including spheres with low uptake, that is, SBR smaller than 1. Furthermore, images of a phantom containing 3D printed inserts reflecting realistic heterogeneity uptake patterns were acquired. Data were reconstructed using various matrix sizes, reconstruction algorithms, and scan durations (noise). For every specific reconstruction and noise level, ten statistically equal replicates were generated. The phantom inserts were delineated using CT and PET‐based segmentation methods. A total of 246 radiomic features was extracted from each image dataset. Images were discretized with a fixed number of 64 bins (FBN) and a fixed bin width (FBW) of 0.25 for the high and a FBW of 0.05 for the low uptake data. In terms of feature reduction, we determined the impact of these factors on the composition of feature clusters, which were defined on the basis of Spearman's correlation matrices. To assess feature repeatability, the intraclass correlation coefficient was calculated over the ten replicates. Results In general, larger spheres with high uptake resulted in better repeatability compared to smaller low uptake spheres. In terms of repeatability, features extracted from heterogeneous phantom inserts were comparable to features extracted from bigger high uptake spheres. For example, for an EARL‐compliant reconstruction, larger and smaller high uptake spheres yielded good repeatability for 32% and 30% of the features, while the heterogeneous inserts resulted in 34% repeatable features. For the low uptake spheres, this was the case for 22% and 20% of the features for bigger and smaller spheres, respectively. Images reconstructed with point‐spread‐function (PSF) resulted in the highest repeatability when compared with OSEM or time‐of‐flight, for example, 53%, 30%, and 32% of repeatable features, respectively (for unsmoothed data, discretized with FBN, 300 s scan duration). Reducing image noise (increasing scan duration and smoothing) and using CT‐based segmentation for the low uptake spheres yielded improved repeatability. FBW discretization resulted in higher repeatability than FBN discretization, for example, 89% and 35% of the features, respectively (for the EARL‐compliant reconstruction and larger high uptake spheres). Conclusion Feature space reduction and repeatability of 18F‐FDG PET radiomic features depended on all studied fa...
Nonrigid local image registration plays an important role in medical imaging. In this paper we focus on demon registration which is introduced by Thirion [1], and is comparable to fluid registration. Because demon registration cannot deal with multiple MRI modalities, we introduce a MRI modality transformation which changes the representation of a T1 scan into a T2 scan using the peaks in a joint histogram. We compare the performance between demon registration with modality transformation, demon registration with gradient images and Rueckerts [2] B-spline based free form deformation method in combination with mutual information. For this test we use perfectly aligned T1 and T2 slices from the BrainWeb database [3], which we local spherically distort. In conclusion demon registration with modality transformation gives the smallest registration errors, in case of local large spherical distortions and small bias fields.
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