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Recurrent middle ear cholesteatomas are commonly preoperatively assessed using MR imaging (non-EPI-DWI) and CT. Both modalities are used with the aim of distinguishing scar tissue from cholesteatoma and determining the extent of bone erosions. Inflammation and scar tissue associated with the lesions might hamper a proper delineation of the corresponding extensions on CT images. Using surgical findings as the criterion standard, we assessed the recurrent middle ear cholesteatoma extent using either uncoregistered or fused CT–MR imaging datasets and determined the corresponding accuracy and repeatability.MATERIALS AND METHODS:
Twenty consecutive patients with suspected recurrent middle ear cholesteatoma and preoperative CT–MR imaging datasets were prospectively included. A double-blind assessment and coregistration of the recurrent middle ear cholesteatoma extent and manual delineation of 18 presumed recurrent middle ear cholesteatomas were performed by 2 radiologists and compared with the criterion standard. "Reliability score" was defined to qualify radiologists' confidence. For each volume, segmentation repeatability was assessed on the basis of intraclass correlation coefficient and overlap indices.RESULTS:
For the whole set of patients, recurrent middle ear cholesteatoma was further supported by surgical results. Two lesions were excluded from the analysis, given that MR imaging did not show a restricted diffusion. Lesions were accurately localized using the fused datasets, whereas significantly fewer lesions (85%) were correctly localized using uncoregistered images. Reliability scores were larger for fused datasets. Segmentation repeatability showed an almost perfect intraclass correlation coefficient regarding volumes, while overlaps were significantly lower in uncoregistered (52%) compared with fused (60%, P < .001) datasets.CONCLUSIONS:
The use of coregistered CT–MR images significantly improved the assessment of recurrent middle ear cholesteatoma with a greater accuracy and better reliability and repeatability.
Image-based classification of lower-grade glioma molecular subtypes has substantial prognostic value. Diffusion tensor imaging has shown promise in lower-grade glioma subtyping but currently requires lengthy, nonstandard acquisitions. Our goal was to investigate lower-grade glioma classification using a machine learning technique that estimates fractional anisotropy from accelerated diffusion MR imaging scans containing only 3 diffusion-encoding directions.MATERIALS AND METHODS:
Patients with lower-grade gliomas (n = 41) (World Health Organization grades II and III) with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status were imaged preoperatively with DTI. Whole-tumor volumes were autodelineated using conventional anatomic MR imaging sequences. In addition to conventional ADC and fractional anisotropy reconstructions, fractional anisotropy estimates were computed from 3-direction DTI subsets using DiffNet, a neural network that directly computes fractional anisotropy from raw DTI data. Differences in whole-tumor ADC, fractional anisotropy, and estimated fractional anisotropy were assessed between IDH-wild-type and IDH-mutant lower-grade gliomas with and without 1p/19q codeletion. Multivariate classification models were developed using whole-tumor histogram and texture features from ADC, ADC + fractional anisotropy, and ADC + estimated fractional anisotropy to identify the added value provided by fractional anisotropy and estimated fractional anisotropy.RESULTS:
ADC (P = .008), fractional anisotropy (P < .001), and estimated fractional anisotropy (P < .001) significantly differed between IDH-wild-type and IDH-mutant lower-grade gliomas. ADC (P < .001) significantly differed between IDH-mutant gliomas with and without codeletion. ADC-only multivariate classification predicted IDH mutation status with an area under the curve of 0.81 and codeletion status with an area under the curve of 0.83. Performance improved to area under the curve = 0.90/0.94 for the ADC + fractional anisotropy classification and to area under the curve = 0.89/0.89 for the ADC + estimated fractional anisotropy classification.CONCLUSIONS:
Fractional anisotropy estimates made from accelerated 3-direction DTI scans add value in classifying lower-grade glioma molecular status.
The Bayesian probabilistic method has shown promising results to offset noise-related variability in perfusion analysis. Using CTP, we aimed to find optimal Bayesian-estimated thresholds based on multiparametric voxel-level models to estimate the ischemic core in patients with acute ischemic stroke.MATERIALS AND METHODS:
Patients with anterior circulation acute ischemic stroke who had baseline CTP and achieved successful recanalization were included. In a subset of patients, multiparametric voxel-based models were constructed between Bayesian-processed CTP maps and follow-up MRIs to identify pretreatment CTP parameters that were predictive of infarction using robust logistic regression. Subsequently CTP-estimated ischemic core volumes from our Bayesian model were compared against routine clinical practice oscillation singular value decomposition–relative cerebral blood flow <30%, and the volumetric accuracy was assessed against final infarct volume.RESULTS:
In the constructed multivariate voxel-based model, 4 variables were identified as independent predictors of infarction: TTP, relative CBF, differential arterial tissue delay, and differential mean transit time. At an optimal cutoff point of 0.109, this model identified infarcted voxels with nearly 80% accuracy. The limits of agreement between CTP-estimated ischemic core and final infarct volume ranged from –25 to 27 mL for the Bayesian model, compared with –61 to 52 mL for oscillation singular value decomposition–relative CBF.CONCLUSIONS:
We established thresholds for the Bayesian model to estimate the ischemic core. The described multiparametric Bayesian-based model improved consistency in CTP estimation of the ischemic core compared with the methodology used in current clinical routine.
Pericortical enhancement on postcontrast FLAIR images is a marker for subtle leptomeningeal blood-brain barrier leakage. We explored the optimal FLAIR sequence parameters for the detection of low gadolinium concentrations within the CSF. On the basis of phantom experiments and human in vivo data, we showed that detection of subtle pericortical enhancement can be facilitated by using a relatively long TE. Future studies should choose their FLAIR sequence parameters carefully when assessing pericortical enhancement due to subtle blood-brain barrier leakage.
4D CT angiography is increasingly used in clinical practice for the assessment of different neurovascular disorders. Optimized processing of 4D-CTA is crucial for diagnostic interpretation because of the large amount of data that is generated. A color-mapping method for 4D-CTA is presented for improved and enhanced visualization of the cerebral vasculature hemodynamics. This method was applied to detect cranial AVFs.MATERIALS AND METHODS:
All patients who underwent both 4D-CTA and DSA in our hospital from 2011 to 2018 for the clinical suspicion of a cranial AVF or carotid cavernous fistula were retrospectively collected. Temporal information in the cerebral vasculature was visualized using a patient-specific color scale. All color-maps were evaluated by 3 observers for the presence or absence of an AVF or carotid cavernous fistula. The presence or absence of cortical venous reflux was evaluated as a secondary outcome measure.RESULTS:
In total, 31 patients were included, 21 patients with and 10 without an AVF. Arterialization of venous structures in AVFs was accurately visualized using color-mapping. There was high sensitivity (86%–100%) and moderate-to-high specificity (70%–100%) for the detection of AVFs on color-mapping 4D-CTA, even without the availability of dynamic subtraction rendering. The diagnostic performance of the 3 observers in the detection of cortical venous reflux was variable (sensitivity, 43%–88%; specificity, 60%–80%).CONCLUSIONS:
Arterialization of venous structures can be visualized using color-mapping of 4D-CTA and proves to be accurate for the detection of cranial AVFs. This finding makes color-mapping a promising visualization technique for assessing temporal hemodynamics in 4D-CTA.