Purpose: Outputs flow parameters from raw 2D (aorta) cardiac MRI
Docker
NVIDIA Driver (GPU version)
NVIDIA Docker Container utilities (GPU version)
Clone the Repository
git clone https://github.com/Urban90/deepFlow.git
Change directory into the newly cloned DeepFlow folder
cd deepflow
Linux/ Unix and macOS
GPU Version (default)
(Nvidia GPU ONLY)
bash deepflow <folder of DICOM files> gpu
CPU Version
bash deepflow <folder of DICOM ZIP files> cpu
ARM Version
aarch64 is now supported. The latest Apple M1 (and later M2, M3 and so on) based macOS systems are now compatible with DeepFlow AI!
Tested on a MacBook Pro with M1 Max and 64 GB RAM
bash deepflow <folder of DICOM ZIP files> arm
First run will pull the required Docker image which might take some time depending on the internet connection and the bandwidth.
Windows
Make sure you have Windows 10 or higher with WSL (recommended) or Hyper-V configured Docker service.
GPU
deepflow_windows.bat <folder of DICOM ZIP files> gpu
CPU
deepflow_windows.bat <folder of DICOM ZIP files> cpu
Logs are not written to the disk in the Windows version
In our tests, the CPU and GPU versions didn't have much of a difference in overall time but your mileage might vary.
We selected ten random samples from the UK Biobank repository and perfomed the test on the host system in triplicate.
Image version Mean Time Uncompressed size
GPU 2min 37s 6.71 GB
CPU 2min 31s 2.34 GB
ARM 1min 53s 2.79GB
Host System
Windows & Linux
Alienware Area 51m R2
Windows 11 | Arch Linux
Kernel: 5.17.5-arch1-1
CPU: Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz
GPU: NVIDIA RTX 2080 Super (notebook)
Docker version 20.10.14, build a224086349
macOS
MacBook Pro
M1 Max 10 cores
64 GB RAM
When using multiple GPUs or a powerful desktop/ server GPU, the speeds can go up drastically.
A single ZIP file per sample is expected.
Results will be written in the output directory of the current folder.
deepflow: Caller script that pulls (if required) and runs the deepFlow Docker image with correct parameters and mounts directories
deepflow_windows.bat: Windows caller script that pulls (if required) and runs the deepFlow Docker image with correct parameters and mounts directories
concat.sh: Internal Docker VM script which compiles the results per run.
deepFlow.py: Main script that does all the calculations inside the Docker VM.
CompiledReportdate:time.tsv: Tab-delimited report of all the samples from the given folder.
<sample_name>.png: Plot of the blood flow (mL/s) over the aortic valve in one heart cycle.
If you have used DeepFlow in any of your publications (thank you), don't forget to cite any one (or more) of the following papers:
Genetic architecture of cardiac dynamic flow volumes (2022)
Bruna Gomes, Aditya Singh, Jack W O’Sullivan, David Amar, Mikhailo Kostur, Francois Haddad, Michael Salerno, Victoria N. Parikh, Benjamin Meder, Euan A. Ashley (Pre-print soon. Publication to follow.)