Tools for Robust Generative Diffeomorphic Slice to Volume Reconstructions (RGDSVR)

This repository provides tools to implement the methods in the manuscript ''Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction'', L Cordero-Grande, JE Ortuño-Fisac, A Uus, M Deprez, A Santos, JV Hajnal, and MJ Ledesma-Carbayo, arXiv, 2021.

The code has been developed in MATLAB and has the following structure:

contains a script to run a reconstruction of the provided example data: rgdsvr_example.m and another to import the Python code loadPythonDeepFetal.m.

contains files to perform SVR reconstructions: svrAlternateMinimization.m, svrCG.m, svrDD.m, svrDecode.m, svrEncode.m, svrExcitationStructures.m, svrRearrangeAxes.m, svrSetUp.m, svrSliceWeights.m, svrSolveDPack.m, svrSolveDVolu.m, svrSolveTVolu.m.

contains common functions used by SVR methods: computeDeformableTransforms.m, finalizeConvergenceControl.m, initializeConvergenceControl.m, initializeDEstimation.m, modulateGradient.m, prepareLineSearch.m, updateRule.m.

contains functions for registration.

contains functions for elastic registration: adAdjointOperator.m, adDualOperator.m, buildDifferentialOperator.m, buildGradientOperator.m, buildMapSpace.m, computeGradientHessianElastic.m, computeJacobian.m, computeRiemannianMetric.m, deformationGradientTensor.m, deformationGradientTensorSpace.m, elasticTransform.m, geodesicShooting.m, integrateReducedAdjointJacobi.m, integrateVelocityFields.m, invertElasticTransform.m, mapSpace.m, precomputeFactorsElasticTransform.m.

contains functions for metrics used in registration: computeMetricDerivativeHessianRigid.m, metricFiltering.m, metricMasking.m, msdMetric.m.

contains functions for rigid registration: convertRotation.m, factorizeHomogeneousMatrix.m, generatePrincipalAxesRotations.m, generateTransformGrids.m, jacobianQuaternionEuler.m, jacobianShearQuaternion.m, mapVolume.m, modifyGeometryROI.m, precomputeFactorsSincRigidTransformQuick.m, quaternionToShear.m, restrictTransform.m, rotationDistance.m, shearQuaternion.m, sincRigidTransformGradientQuick.m, sincRigidTransformQuick.m.

contains functions that replace, extend or adapt some MATLAB built-in functions: aplGPU.m, det2x2m.m, det3x3m.m, diagm.m, dynInd.m, eigm.m, eultorotm.m, gridv.m, ind2subV.m, indDim.m, matfun.m, multDimMax.m, multDimMin.m, multDimSum.m, numDims.m, parUnaFun.m, quattoeul.m, resPop.m, resSub.m, rotmtoquat.m, sub2indV.m, svdm.m.

contains functions to control the implementation and parameters of the algorithm: channelsDeepDecoder.m, parametersDeepDecoder.m, svrAlgorithm.m, useGPU.m.

contains functions that implement generic methods for reconstruction: build1DCTM.m, build1DFTM.m, buildFilter.m, buildStandardDCTM.m, buildStandardDFTM.m, computeROI.m, extractROI.m, fctGPU.m, fftGPU.m, filtering.m, flipping.m, fold.m, generateGrid.m, ifctGPU.m, ifftGPU.m, ifold.m, mirroring.m, resampling.m, shifting.m.

contains python methods.

contains python methods to build deep architectures: deepdecoder.py.

contains python methods with generic functions: bmul.py, complex.py, dynind.py, matcharrays.py, shift.py.

contains python methods to build deep layers: encode.py, resample.py, sinc.py, sine.py, swish.py, tanh.py.

contains python methods with generic deep methodologies: apl.py, resampling.py, tmtx.py, t.py.

contains python methods for optimization: cost.py, fit.py.

contains python methods to build deep units: atac.py decoder.py.

contains auxiliary tools: findString.m, removeExtension.m, writenii.m.

from https://uk.mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-image

NOTE 1: Example data provided in the dataset svr_inp_034.mat. For runs without changing the paths, it should be placed in folder

Data generated when running the example script appears in this folder with names svr_out_034.mat and x_034.mat.

NOTE 2: Instructions for linking the python code in loadPythonDeepFetal.m.

NOTE 3: pathAnaconda variable in rgdsvr_example.m needs to point to parent of python environment.

NOTE 4: Example reconstruction takes about half an hour in a system equipped with a GPU NVIDIA GeForce RTX 3090.