The combination of maximal possible resection and additional SRS improves functional outcomes in patients with skull base meningioma. A TRR greater than 97% in volume can be achieved with satisfactory functional preservation and will lead to excellent tumor control in combined treatment of skull base meningioma.
Results of the present study indicated that left-sided TSA for hippocampal sclerosis tends to improve verbal memory function with the preservation of other types of memory function. Moreover, right-sided TSA for hippocampal sclerosis can lead to significant improvement in memory function, with memory improvement observed 1 month after right-sided TSA and persisting 1 year after surgery.
Recent advances in deep learning (DL) (4,5) have led to several radiologic applications (6), specifically Background: Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures.
Purpose:To train and to validate a deep learning (DL)-based model to produce DSA-like cerebral angiograms directly from dynamic angiograms and then quantitatively and visually evaluate these angiograms for clinical usefulness.
Materials and Methods:A retrospective model development and validation study was conducted on dynamic and DSA image pairs consecutively collected from January 2019 through April 2019. Angiograms showing misregistration were first separated per patient by two radiologists and sorted into the misregistration test data set. Nonmisregistration angiograms were divided into development and external test data sets at a ratio of 8:1 per patient. The development data set was divided into training and validation data sets at ratio of 3:1 per patient. The DL model was created by using the training data set, tuned with the validation data set, and then evaluated quantitatively with the external test data set and visually with the misregistration test data set. Quantitative evaluations used the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) with mixed liner models. Visual evaluation was conducted by using a numerical rating scale.
Results:The training, validation, nonmisregistration test, and misregistration test data sets included 10 751, 2784, 1346, and 711 paired images collected from 40 patients (mean age, 62 years 11 [standard deviation]; 33 women). In the quantitative evaluation, DL-generated angiograms showed a mean PSNR value of 40.2 dB 4.05 and a mean SSIM value of 0.97 0.02, indicating high coincidence with the paired DSA images. In the visual evaluation, the median ratings of the DL-generated angiograms were similar to or better than those of the original DSA images for all 24 sequences.
Conclusion:The deep learning-based model provided clinically useful cerebral angiograms free from clinically significant artifacts directly from dynamic angiograms.
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