sConv02 = cl.convBlock(128, 3, 2)(sConv01) sConv03 = cl.convBlock(256, 3, 2)(sConv02) res01 = cl.resBlock(256, 3)(sConv03) res02 = cl.resBlock(256, 3)(res01) tConv01 = cl.convTransBlock(128, 3, 2)(res02) add = layers.Add()([tConv01, sConv02]) tConv02 = cl.convTransBlock(64, 3, 2)(add) tConv03 = cl.convTransBlock(24, 3, 4)(tConv02) synt = layers.Conv2D(24, 3, padding="same")(tConv03) con = cl.conBlock()([mul, dMZ, synt]) ifft = cl.ifftBlock()(con) sConv01 = cl.convBlock(32, 3, 4)(ifft) sConv02 = cl.convBlock(128, 3, 2)(sConv01) sConv03 = cl.convBlock(256, 3, 2)(sConv02) res01 = cl.resBlock(256, 3)(sConv03) res02 = cl.resBlock(256, 3)(res01) tConv01 = cl.convTransBlock(128, 3, 2)(res02) add = layers.Add()([tConv01, sConv02]) tConv02 = cl.convTransBlock(64, 3, 2)(add) tConv03 = cl.convTransBlock(24, 3, 4)(tConv02) synt = layers.Conv2D(24, 3, padding="same")(tConv03)
from tensorflow.keras import layers, models, losses
import matplotlib.pyplot as plt
dVal = dataOp.data_loader("C:/Datasets/MRI_Data/Recon_v4/Val", 8, 4, 10, False)
dTrain = dataOp.data_loader("C:/Datasets/MRI_Data/Recon_v4/Train", 8, 4, 10,
False)
dL = dataOp.data_loader("C:/Datasets/MRI_Data/Recon_v4/Val", 1, 4, 10, False)
d = dL.__getitem__(200)
dIn = layers.Input(shape=(256, 256, 2))
mIn = layers.Input(shape=(256, 256, 2))
mul = cl.mulBlock()([dIn, mIn])
# Dumbell Image Domain
ifft = cl.ifftBlock()(mul)
dumbell = cl.dumbellXL()(ifft)
fft = cl.fftBlock()(dumbell)
con = cl.conBlock()([mul, mIn, fft])
ifft = cl.ifftBlock()(con)
# Dumbell Image Domain
dumbell = cl.dumbellXL()(ifft)
fft = cl.fftBlock()(dumbell)
con = cl.conBlock()([mul, mIn, fft])
ifft = cl.ifftBlock()(con)
# Dumbell Image Domain
dumbell = cl.dumbellXL()(ifft)
def ifftModel():
dIn = layers.Input(shape=(256, 256, 2))
ifft = cl.ifftBlock()(dIn)
model = models.Model(inputs=[dIn], outputs=[ifft])
return (model)