Magnetic resonance imaging (MRI) is the most sensitive technique for breast
cancer detection among current clinical imaging modalities. Contrast-enhanced
MRI (CE-MRI) provides superior differentiation between tumors and invaded
healthy tissue, and has become an indispensable technique in the detection and
evaluation of cancer. However, the use of gadolinium-based contrast agents
(GBCA) to obtain CE-MRI may be associated with nephrogenic systemic fibrosis
and may lead to bioaccumulation in the brain, posing a potential risk to human
health. Moreover, and likely more important, the use of gadolinium-based
contrast agents requires the cannulation of a vein, and the injection of the
contrast media which is cumbersome and places a burden on the patient. To
reduce the use of contrast agents, diffusion-weighted imaging (DWI) is emerging
as a key imaging technique, although currently usually complementing breast
CE-MRI. In this study, we develop a multi-sequence fusion network to synthesize
CE-MRI based on T1-weighted MRI and DWIs. DWIs with different b-values are
fused to efficiently utilize the difference features of DWIs. Rather than
proposing a pure data-driven approach, we invent a multi-sequence attention
module to obtain refined feature maps, and leverage hierarchical representation
information fused at different scales while utilizing the contributions from
different sequences from a model-driven approach by introducing the weighted
difference module. The results show that the multi-b-value DWI-based fusion
model can potentially be used to synthesize CE-MRI, thus theoretically reducing
or avoiding the use of GBCA, thereby minimizing the burden to patients. Our
code is available at
https://github.com/Netherlands-Cancer-Institute/CE-MRI.

本研究开发了一种多序列融合网络，基于 T1 加权 MRI 和 DWI 合成对比增强 MRI，可以潜在地用于合成 CE-MRI，从而在理论上减少使用 GBCA，降低对患者的负担。

使用多 b 值 DWI 为基础的分层融合网络与关注机制综合合成增强对比度的乳腺磁共振影像

Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based  Hierarchical Fusion Network with Attention Mechanism

Purpose: To investigate feasibility of accelerating prostate
diffusion-weighted imaging (DWI) by reducing the number of acquired averages
and denoising the resulting image using a proposed guided denoising
convolutional neural network (DnCNN). Materials and Methods: Raw data from the
prostate DWI scans were retrospectively gathered (between July 2018 and July
2019) from six single-vendor MRI scanners. 118 data sets were used for training
and validation (age: 64.3 +- 8 years) and 37 - for testing (age: 65.1 +- 7.3
years). High b-value diffusion-weighted (hb-DW) data were reconstructed into
noisy images using two averages and reference images using all sixteen
averages. A conventional DnCNN was modified into a guided DnCNN, which uses the
low b-value DWI image as a guidance input. Quantitative and qualitative reader
evaluations were performed on the denoised hb-DW images. A cumulative link
mixed regression model was used to compare the readers scores. The agreement
between the apparent diffusion coefficient (ADC) maps (denoised vs reference)
was analyzed using Bland Altman analysis. Results: Compared to the DnCNN, the
guided DnCNN produced denoised hb-DW images with higher peak signal-to-noise
ratio and structural similarity index and lower normalized mean square error (p
< 0.001). Compared to the reference images, the denoised images received higher
image quality scores (p < 0.0001). The ADC values based on the denoised hb-DW
images were in good agreement with the reference ADC values. Conclusion:
Accelerating prostate DWI by reducing the number of acquired averages and
denoising the resulting image using the proposed guided DnCNN is technically
feasible.

本研究旨在探究通过减少采集平均数和使用一种被命名为引导噪声卷积神经网络（DnCNN）的方法，对前列腺扩散加权成像（DWI）进行加速。结果表明，相较于使用传统的 DnCNN，使用引导 DnCNN 可以产生结构相似度指数和峰值信噪比更高、规范化均方误差更低的去噪图像，并且 ADC 值与参考 ADC 值达成了很好的一致性，因此这种加速前列腺 DWI 的方法在技术上是可行的。