Dataset

This dataset was created by Iyad

Released under Apache 2.0

Contents

Dataset

This dataset was created by Muhammad Rizwan

Contents

It contains the following files:

Analysis of ‘headbrain’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jemishdonda/headbrain on 28 January 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

The Magnetic Resonance - Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki).

The dataset consists of 2D image slices extracted using the RadiAnt DICOM viewer software. The extracted images are transformed to DICOM image data format with a resolution of 256x256 pixels. There are a total of 179 2D axial image slices referring to 20 patient volumes (90 MR and 89 CT 2D axial image slices). The dataset contains MR and CT brain tumour images with corresponding segmentation masks. The MR images of each patient were acquired with a 5.00mm T Siemens Verio 3T using a T2-weighted without contrast agent, 3 Fat sat pulses (FS), 2500-4000 TR, 20-30 TE, and 90/180 flip angle. The CT images were acquired with Siemens Somatom scanner with 2.46mGY.cm dose length, 130KV voltage, 113-327 mAs tube current, topogram acquisition protocol, 64 dual source, one projection, and slice thickness of 7.0mm. Smooth and sharp filters have been applied to the CT images. The MR scans have a resolution of 0.7x0.6x5 mm^3, while the CT scans have a resolution of 0.6x0.6x7 mm^3.

More information and the application of the dataset can be found in the following research paper:

Alaa Abu-Srhan; Israa Almallahi; Mohammad Abushariah; Waleed Mahafza; Omar S. Al-Kadi. Paired-Unpaired Unsupervised Attention Guided GAN with Transfer Learning for Bidirectional Brain MR-CT Synthesis. Comput. Biol. Med. 136, 2021. doi: https://doi.org/10.1016/j.compbiomed.2021.104763.

Computed tomography scans for 11 pigs, supporting the manuscript:

Geometric and inertial properties of the pig head and brain in an anatomical coordinate system.

CQ500 dataset of 491 Computed tomography scans with 193,317 slices Anonymized dicoms for all the scans and the corresponding radiologists reads. ![]() Paper:

Code to generate video shown here https://www.youtube.com/watch?v=Lwl51aLvDRQ

"A Tour Through Brain Circuits"

Includes the unedited output video file in high resolution, as well.

Needs Lead-DBS as dependency (www.lead-dbs.org).

This dataset contains over 9,000 head CT scans, each labeled as normal or abnormal. Each scan contains a reconstructed image (stored in our institution’s PACS and saved as DICOMs) and a corresponding sinogram (simulated via GE’s CatSim software and saved as numpy arrays). The reconstructed images are 512x512 pixels with a variable number of axial slices per scan. The sinograms are 984x888 pixels with a variable number of axial slices per scan. The full dataset is 1.3T. We retrospectively collected the head CT scans (acquired between 2001 – 2014) from our institution’s PACS, selected according to the following criteria: non-contrast CT of the head acquired in axial mode on a GE scanner and pixel spacing of 0.49 or 0.60 mm in the axial plane. The reading radiologist designated each CT scan as normal or abnormal at the time of original image interpretation; these designations were given as part of standard clinical procedure and not modified during dataset curation. We used GE’s CatSim, a validated simulation software for GE machines, to simulate high-fidelity sinograms of each head CT scan. If you use this dataset, please cite our paper (https://pubs.rsna.org/doi/abs/10.1148/ryai.2021200229). Additionally, part of this dataset was used in the RSNA Intracranial Hemorrhage Detection Challenge (https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection). Labels for hemorrhage can be found in the Kaggle download.

7T structural scans acquired with focus on subcortical contrast.

1) Voxel data for head and brain masks for 11 pigs. Columns 1-3: x, y, z CT voxel coordinates, Column 4: Hounsfield units.

2) CT voxel coordinates for key anatomical landmarks

3) Hounsfield to mass density calibration equations

Supporting the manuscript:

Geometric and inertial properties of the pig head and brain in an anatomical coordinate system.

A brain MRI dataset to develop and test improved methods for detection and segmentation of brain metastases. The dataset includes 156 whole brain MRI studies, including high-resolution, multi-modal pre- and post-contrast sequences in patients with at least 1 brain metastasis accompanied by ground-truth segmentations by radiologists.

About 2% of all patients with a primary neoplasm will be diagnosed with brain metastases at the time of their initial diagnosis. As we are getting better at controlling primary cancers, even more patients eventually present with such lesions. Given that brain metastases are often quite treatable with surgery or stereotactic radiosurgery, accurate segmentation of brain metastases is a common job for radiologists. Having algorithms to help detect and localize brain metastasis could relieve radiologists from this tedious but crucial task. Given the success of recent AI techniques on other segmentation tasks, we have put together this gold-standard, labeled MRI dataset to allow for the development and testing of new techniques in these patients with the hopes of spurring research in this area.

This is a dataset of 156 pre- and post-contrast whole brain MRI studies in patients with at least 1 cerebral metastasis. Mean patient age was 63±12 years (range: 29–92 years). Primary malignancies included lung (n = 99), breast (n = 33), melanoma (n = 7), genitourinary (n = 7), gastrointestinal (n = 5), and miscellaneous cancers (n = 5). 64 (41%) had 1–3 metastases, 47 (30%) had 4–10 metastases, and 45 (29%) had >10 metastases. Lesion sizes varied from 2 mm to over 4 cm and were scattered in every region of the brain parenchyma, i.e., the supratentorial and infratentorial regions, as well as the cortical and subcortical structures. It includes 4 different 3D sequences (T1 spin-echo pre-contrast, T1 spin-echo post-contrast, T1 gradient-echo post (using an IR-prepped FSPGR sequence), T2 FLAIR post) in the axial plane, co-registered to each other, resampled to 256 x 256 pixels. Standard dose (0.1 mmol/kg) gadolinium contrast agents were used for all cases. All the images have been skull-stripped by using the Brain Extraction Tool (BET) (Smith SM. Fast robust automated brain extraction. Hum Brain Map. 2002;17:143–155). The brain masks were generated from the precontrast T1-weighted 3D CUBE imaging series using the nordicICE software package (NordicNeuroLab, Bergen, Norway) and propagated to the other sequences.

For 105 cases, we include radiologist-drawn segmentations of the metastatic lesions, stored in folder ‘mets_stanford_release_train’. The segmentations were based on the T1 gradient-echo post-contrast images. The remaining 51 cases are unlabeled and stored in ‘mets_stanford_release_test’. There are 5 folders for each subject in the training group – folder ‘0’ contains T1 gradient-echo post images; folder ‘1’ contains T1 spin-echo pre images; folder ‘2’ contains T1 spin-echo post images; folder ‘3’ contains T2 FLAIR post images; folder ‘seg’ contains a binary mask of the segmented metastases (0, 255). There are 4 folders for each subject in the testing group, which are labelled identically, except for the absence of folder ‘seg’.

More detailed information on this dataset and the Stanford group’s initial performance on this data set can be found in Grøvik et al., Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multisequence MRI, JMRI 2019; 51(1):175-182.

We would like to thank the team involved with labeling and preparing the data and for checking it for potential PHI: Darvin Yi, Endre Grovik, Elizabeth Tong, Michael Iv, Daniel Rubin, Greg Zaharchuk, and Ghiam Yamin, and the Division of Neuroimaging at Stanford for supporting this project.

Grøvik et al., Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multisequence MRI, JMRI 2019; 51(1):175-182 also available on ArXiv (https://arxiv.org/abs/1903.07988).

Series of structural scans incl. cardiac gated FLASH sequences with focus on basal ganglia. Scanned at 7T and 3T on the same day (allowing direct comparisons).

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Computed tomography images (DICOM format) of Merino wether heads.

Description： Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental trajectories. The present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging protocol. Two accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and China. Anatomical magnetic resonance imaging (MRI) of typically developing school-age children (TDC) was obtained up to three times at nominal intervals of 1.25 years. The protocol generated and compared population- and age-specific brain templates and growth charts, respectively. A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American TDC.Population- and age-specific brain templates were used to quantify warp cost, the differences between individual brains and brain templates. Volumetric growth charts for labeled brain network areas were generated. Shape analyses of cost functions supported the necessity of age-specific and ethnicity-matched brain templates, which was confirmed by growth chart analyses. These analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas, regions which are most variable across individuals in regard to their structure and function. Age- and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts, indicating the necessity of the brain charts and brain templates generated in tandem. These templates and growth charts as well as related codes have been made freely available to the public for open neuroscience

Usage: The age-specific head brain templates can be used for pediatric neuroimaging studies to provide a standard reference on head brain spaces. Sample codes for such uses can be found on Github(https://github.com/zuoxinian/CCS/tree/master/H3/GrowthCharts). The growth charts on various school-age children and adolescents can provide a normal growth standard on the brain development across school age, together with the normative modeling methods, they offer an analytic way of implementing individualized or personalized pediatrics. All the templates and growth charts are downloadable as NIFTI files. For a given NIFTI file in the dataset, IPCAS indicates the Chinese school age template,NKI named files indicate American template.Users can find the age-specific template in the name of (IPCAS/NKI)_age(X)_brain_template.nii.gz and the different tissue template are also provided by this dataset in the name of （IPCAS/NKI)_age(X)_brain_pve(_0/_1/_2/seg) in which 0 indicates CSF, 1 indicates gray matter, 2 indicates white matter, and seg indicates hard segmentatin.

We collected data from 167 patients with biopsy-confirmed thyroid nodules (n=192) at the Stanford University Medical Center. The dataset consists of ultrasound cine-clip images, radiologist-annotated segmentations, patient demographics, lesion size and location, TI-RADS descriptors, and histopathological diagnoses.

Univariate logistic regression analysis of variables associated with the development of ONFH in brain tumor patients.

Demographic characteristics of the 48 brain tumor patients with ONFH (case) compared to 96 matched brain tumor patients without ONFH (control).