If you were ten times bolder - what big idea would you recommend on interplay between reconstruction and image analysis?
What first step would you take to get started?
22 - I would integrate DVC into the pipeline of QIM. The first step that I would do is to implement a tool for the registration of volumes.
19 - Idea: an interactive reconstruction GUI that shows a live volumetric rendering while the reconstruction is till being processed. Users can tweak parameters and see changes live in 3D.
19 - A system that allows all project members to utilize their expertise.
16 - Using image analysis to automatically choose the best reconstruction out of a set instead of manual judging. Find some IA (image analysis) method that gives a metric.
16 - Doing all the image analysis in the reconstruction step: Specifically: Doing segmentation as part of the reconstruction.
15- Notebook / pipeline for handling & reconstructing large datasets
perform “coarse” reconstruction (binning) limited n-projections and visualize to user.
Cut data in chunks and do high quality reconstruction (automatically)
15 - an easy way for visualization of time-resolved 3D-data of big sizes CT dataset - 1 timestep ca = 80 GB; also changing visualization eg segmented vs raw; vol vs clip. Step 1: easy handling of big data, both for visualizing and analysis.
14 - Include qim3d in CIL Library (or vice versa) such that e.g. segmentation/central component outputs can be seen using a single (or few) functions working directly on the raw data.
14 - Specialized reconstruction for specific tasks ie a reconstruction that lends itself to local thickness for example.
14 - ASTRA, TIGRE, tomopy, etc. There is now many open source softwares for tomography, and the same goes for 3D analysis platforms (cf ILASTIK). What international efforts are undertaken to merge this, ensuring that resources are not wasted implementing the same things over and over again? Can QIM and CIL grow by joining forces?
14 - Combined and/or sequential pipelines to perform image reconstruction and analysis live during beamtime. Leave the beamtime with publication ready figures. First step: Do pre-scanning of the given sample with lab-based micro-CT (or 1 test scan with SR [synchrotron radiation?] micro CT in advance) and set up the pipeline. Then do final beamtime.
13 - Have an optical camera in the (CT) scanner. Take pictures during acquisition. Use computer vision/image analysis to extract object exterior surface geometry and appearance. Find calibration between CT source/detector & camera to enhance reconstruction.
12 - A system for brain motion simulation.
12 - A complete CT scan of the earth and build a virtual replica of it, so can explore every corner of it.
12 - Run concurrent training as data analysis is not independent of data processing. First step: Ease of software installation.
9 - Automatic or learned changes in discretization for coarse/fine features and prior expectations.
8 - What would you implement? Unet segmentation (neural network segmentation) AI.