  Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in
the breasts. Sequences obtained with different settings can capture the
specific characteristics of lesions. Such multi-parameter MRI information has
been shown to improve radiologist performance in lesion classification, as well
as improving the performance of artificial intelligence models in various
tasks. However, obtaining multi-parameter MRI makes the examination costly in
both financial and time perspectives, and there may be safety concerns for
special populations, thus making acquisition of the full spectrum of MRI
sequences less durable. In this study, different than naive input fusion or
feature concatenation from existing MRI parameters, a novel
$\textbf{I}$ntegrated MRI $\textbf{M}$ulti-$\textbf{P}$arameter
reinf$\textbf{O}$rcement fusion generato$\textbf{R}$ wi$\textbf{T}$h
$\textbf{A}$tte$\textbf{NT}$ion Network (IMPORTANT-Net) is developed to
generate missing parameters. First, the parameter reconstruction module is used
to encode and restore the existing MRI parameters to obtain the corresponding
latent representation information at any scale level. Then the multi-parameter
fusion with attention module enables the interaction of the encoded information
from different parameters through a set of algorithmic strategies, and applies
different weights to the information through the attention mechanism after
information fusion to obtain refined representation information. Finally, a
reinforcement fusion scheme embedded in a $V^{-}$-shape generation module is
used to combine the hierarchical representations to generate the missing MRI
parameter. Results showed that our IMPORTANT-Net is capable of generating
missing MRI parameters and outperforms comparable state-of-the-art networks.
Our code is available at
https://github.com/Netherlands-Cancer-Institute/MRI_IMPORTANT_NET.
