scholarly journals A detailed analysis of anatomical plausibility of crossed and uncrossed streamline rendition of the dentato-rubro-thalamic tract (DRT(T)) in a commercial stereotactic planning system

2021 ◽  
Author(s):  
Volker A. Coenen ◽  
Bastian E. Sajonz ◽  
Peter C. Reinacher ◽  
Christoph P. Kaller ◽  
Horst Urbach ◽  
...  
Keyword(s):  
Surgical Planning ◽  
Ground Truth ◽  
Planning System ◽  
Single Subject ◽  
Fiber Structure ◽  
Native Space ◽  
Volume Of Interest ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

Abstract Background An increasing number of neurosurgeons use display of the dentato-rubro-thalamic tract (DRT) based on diffusion weighted imaging (dMRI) as basis for their routine planning of stimulation or lesioning approaches in stereotactic tremor surgery. An evaluation of the anatomical validity of the display of the DRT with respect to modern stereotactic planning systems and across different tracking environments has not been performed. Methods Distinct dMRI and anatomical magnetic resonance imaging (MRI) data of high and low quality from 9 subjects were used. Six subjects had repeated MRI scans and therefore entered the analysis twice. Standardized DICOM structure templates for volume of interest definition were applied in native space for all investigations. For tracking BrainLab Elements (BrainLab, Munich, Germany), two tensor deterministic tracking (FT2), MRtrix IFOD2 (https://www.mrtrix.org), and a global tracking (GT) approach were used to compare the display of the uncrossed (DRTu) and crossed (DRTx) fiber structure after transformation into MNI space. The resulting streamlines were investigated for congruence, reproducibility, anatomical validity, and penetration of anatomical way point structures. Results In general, the DRTu can be depicted with good quality (as judged by waypoints). FT2 (surgical) and GT (neuroscientific) show high congruence. While GT shows partly reproducible results for DRTx, the crossed pathway cannot be reliably reconstructed with the other (iFOD2 and FT2) algorithms. Conclusion Since a direct anatomical comparison is difficult in the individual subjects, we chose a comparison with two research tracking environments as the best possible “ground truth.” FT2 is useful especially because of its manual editing possibilities of cutting erroneous fibers on the single subject level. An uncertainty of 2 mm as mean displacement of DRTu is expectable and should be respected when using this approach for surgical planning. Tractographic renditions of the DRTx on the single subject level seem to be still illusive.

scholarly journals 3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Philippe Boutinaud ◽  
Ami Tsuchida ◽  
Alexandre Laurent ◽  
Filipa Adonias ◽  
Zahra Hanifehlou ◽  
...  
Keyword(s):  
Learning Performance ◽  
Similar Distribution ◽  
Perivascular Spaces ◽  
Visual Rating ◽  
Independent Expert ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Experienced Operator ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all “visible” PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

scholarly journals 3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

2020 ◽  
Author(s):  
Philippe Boutinaud ◽  
Ami Tsuchida ◽  
Alexandre Laurent ◽  
Filipa Adonias ◽  
Zahra Hanifehlou ◽  
...  
Keyword(s):  
Learning Performance ◽  
Similar Distribution ◽  
Perivascular Spaces ◽  
Visual Rating ◽  
Independent Expert ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Experienced Operator ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

AbstractWe implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all “visible” PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

scholarly journals Regional medical practice variation in high-cost healthcare services

2021 ◽  
Author(s):  
Michael Berger ◽  
Thomas Czypionka
Keyword(s):  
Medical Practice ◽  
Regional Variation ◽  
Multivariate Regression ◽  
Healthcare Services ◽  
Practice Variation ◽  
District Level ◽  
Healthcare Data ◽  
Regional Medical ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

AbstractMagnetic resonance imaging (MRI) is a popular yet cost-intensive diagnostic measure whose strengths compared to other medical imaging technologies have led to increased application. But the benefits of aggressive testing are doubtful. The comparatively high MRI usage in Austria in combination with substantial regional variation has hence become a concern for its policy makers. We use a set of routine healthcare data on outpatient MRI service consumption of Austrian patients between Q3-2015 and Q2-2016 on the district level to investigate the extent of medical practice variation in a two-step statistical analysis combining multivariate regression models and Blinder–Oaxaca decomposition. District-level MRI exam rates per 1.000 inhabitants range from 52.38 to 128.69. Controlling for a set of regional characteristics in a multivariate regression model, we identify payer autonomy in regulating access to MRI scans as the biggest contributor to regional variation. Nevertheless, the statistical decomposition highlights that more than 70% of the regional variation remains unexplained by differences between the observable district characteristics. In the absence of epidemiological explanations, the substantial regional medical practice variation calls the efficiency of resource deployment into question.

scholarly journals Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study

2021 ◽  
Vol 7 (1) ◽  
pp. 205521732199239
Author(s):  
Cecilie Jacobsen ◽  
Robert Zivadinov ◽  
Kjell-Morten Myhr ◽  
Turi O Dalaker ◽  
Ingvild Dalen ◽  
...  
Keyword(s):  
Clinical Data ◽  
Grey Matter Volume ◽  
Lesion Volume ◽  
Information Processing Speed ◽  
Disability Status ◽  
Mri Scans ◽  
Patients At Risk ◽  
Magnetic Resonance Imaging Mri ◽  
Specific Volumes

Objectives To identify Magnetic Resonance Imaging (MRI), clinical and demographic biomarkers predictive of worsening information processing speed (IPS) as measured by Symbol Digit Modalities Test (SDMT). Methods Demographic, clinical data and 1.5 T MRI scans were collected in 76 patients at time of inclusion, and after 5 and 10 years. Global and tissue-specific volumes were calculated at each time point. For the primary outcome of analysis, SDMT was used. Results Worsening SDMT at 5-year follow-up was predicted by baseline age, Expanded Disability Status Scale (EDSS), SDMT, whole brain volume (WBV) and T2 lesion volume (LV), explaining 30.2% of the variance of SDMT. At 10-year follow-up, age, EDSS, grey matter volume (GMV) and T1 LV explained 39.4% of the variance of SDMT change. Conclusion This longitudinal study shows that baseline MRI-markers, demographic and clinical data can help predict worsening IPS. Identification of patients at risk of IPS decline is of importance as follow-up, treatment and rehabilitation can be optimized.

scholarly journals Cross-sectional analysis of follow-up chest MRI and chest CT scans in patients previously affected by COVID-19

2021 ◽  
Author(s):  
Martina Pecoraro ◽  
Stefano Cipollari ◽  
Livia Marchitelli ◽  
Emanuele Messina ◽  
Maurizio Del Monte ◽  
...  
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
Imaging Biomarker ◽  
Cross Sectional ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Sectional Analysis ◽  
Ct And Mri ◽  
Interlobular Septal Thickening

Abstract Purpose The aim of the study was to prospectively evaluate the agreement between chest magnetic resonance imaging (MRI) and computed tomography (CT) and to assess the diagnostic performance of chest MRI relative to that of CT during the follow-up of patients recovered from coronavirus disease 2019. Materials and methods Fifty-two patients underwent both follow-up chest CT and MRI scans, evaluated for ground-glass opacities (GGOs), consolidation, interlobular septal thickening, fibrosis, pleural indentation, vessel enlargement, bronchiolar ectasia, and changes compared to prior CT scans. DWI/ADC was evaluated for signal abnormalities suspicious for inflammation. Agreement between CT and MRI was assessed with Cohen’s k and weighted k. Measures of diagnostic accuracy of MRI were calculated. Results The agreement between CT and MRI was almost perfect for consolidation (k = 1.00) and change from prior CT (k = 0.857); substantial for predominant pattern (k = 0.764) and interlobular septal thickening (k = 0.734); and poor for GGOs (k = 0.339), fibrosis (k = 0.224), pleural indentation (k = 0.231), and vessel enlargement (k = 0.339). Meanwhile, the sensitivity of MRI was high for GGOs (1.00), interlobular septal thickening (1.00), and consolidation (1.00) but poor for fibrotic changes (0.18), pleural indentation (0.23), and vessel enlargement (0.50) and the specificity was overall high. DWI was positive in 46.0% of cases. Conclusions The agreement between MRI and CT was overall good. MRI was very sensitive for GGOs, consolidation and interlobular septal thickening and overall specific for most findings. DWI could be a reputable imaging biomarker of inflammatory activity.

scholarly journals Fully Data-Driven Pseudohealthy Synthesis for Planning Valve-Sparing Aortic Root Reconstruction using Conditional Variational Autoencoders

2020 ◽  
Vol 6 (3) ◽  
pp. 284-287
Author(s):  
Jannis Hagenah ◽  
Mohamad Mehdi ◽  
Floris Ernst
Keyword(s):  
Aortic Root ◽  
Similarity Index ◽  
Ground Truth ◽  
Representation Learning ◽  
Patient Specific ◽  
Ultrasound Images ◽  
Specific Geometry ◽  
The Individual ◽  
Native Root ◽  
Original Information

AbstractAortic root aneurysm is treated by replacing the dilated root by a grafted prosthesis which mimics the native root morphology of the individual patient. The challenge in predicting the optimal prosthesis size rises from the highly patient-specific geometry as well as the absence of the original information on the healthy root. Therefore, the estimation is only possible based on the available pathological data. In this paper, we show that representation learning with Conditional Variational Autoencoders is capable of turning the distorted geometry of the aortic root into smoother shapes while the information on the individual anatomy is preserved. We evaluated this method using ultrasound images of the porcine aortic root alongside their labels. The observed results show highly realistic resemblance in shape and size to the ground truth images. Furthermore, the similarity index has noticeably improved compared to the pathological images. This provides a promising technique in planning individual aortic root replacement.

scholarly journals Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Elin Wallstén ◽  
Jan Axelsson ◽  
Joakim Jonsson ◽  
Camilla Thellenberg Karlsson ◽  
Tufve Nyholm ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Research Study ◽  
Standardized Uptake Value ◽  
Ground Truth ◽  
Whole Body ◽  
Mr Images ◽  
Pelvic Region ◽  
Mri Scans ◽  
The Mean ◽  
Prostate Cancer Research

Abstract Background Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region. Materials and method Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth. Results The mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET. Conclusion Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.

scholarly journals Magnetic resonance imaging (MRI) scans in patients with dorsal root ganglion stimulation

Pain Practice ◽  
2021 ◽  
Author(s):  
Marco Reining ◽  
Dirk Winkler ◽  
Joachim Boettcher ◽  
Juergen Meixensberger ◽  
Michael Kretzschmar
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Dorsal Root Ganglion ◽  
Dorsal Root ◽  
Resonance Imaging ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

scholarly journals WattScale

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Srinivasan Iyengar ◽  
Stephen Lee ◽  
David Irwin ◽  
Prashant Shenoy ◽  
Benjamin Weil
Keyword(s):  
Cooling System ◽  
Large Population ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Building Envelope ◽  
Ground Truth Data ◽  
Heating And Cooling ◽  
Data Driven Approach ◽  
Execution Mode ◽  
The Individual

Buildings consume over 40% of the total energy in modern societies, and improving their energy efficiency can significantly reduce our energy footprint. In this article, we present WattScale, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region. Unlike previous methods such as least-squares that use point estimates, WattScale uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the distribution of parameters that affect a building. Further, it compares them with similar homes in a given population. WattScale also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in various settings. WattScale has two execution modes—(i) individual and (ii) region-based, which we highlight using two case studies. For the individual execution mode, we present results from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults, respectively. For the region-based execution mode, we show that WattScale can be extended to millions of homes in the U.S. due to the recent availability of representative energy datasets.

Sign in / Sign up

Export Citation Format

Share Document