In neuroimaging, generally, brain CT is more cost-effective and accessible
imaging option compared to MRI. Nevertheless, CT exhibits inferior soft-tissue
contrast and higher noise levels, yielding less precise structural clarity. In
response, leveraging more readily available CT to construct its counterpart
MRI, namely, medical image-to-image translation (I2I), serves as a promising
solution. Particularly, while diffusion models (DMs) have recently risen as a
powerhouse, they also come with a few practical caveats for medical I2I. First,
DMs' inherent stochasticity from random noise sampling cannot guarantee
consistent MRI generation that faithfully reflects its CT. Second, for 3D
volumetric images which are prevalent in medical imaging, naively using 2D DMs
leads to slice inconsistency, e.g., abnormal structural and brightness changes.
While 3D DMs do exist, significant training costs and data dependency bring
hesitation. As a solution, we propose novel style key conditioning (SKC) and
inter-slice trajectory alignment (ISTA) sampling for the 2D Brownian bridge
diffusion model. Specifically, SKC ensures a consistent imaging style (e.g.,
contrast) across slices, and ISTA interconnects the independent sampling of
each slice, deterministically achieving style and shape consistent 3D CT-to-MRI
translation. To the best of our knowledge, this study is the first to achieve
high-quality 3D medical I2I based only on a 2D DM with no extra architectural
models. Our experimental results show superior 3D medical I2I than existing 2D
and 3D baselines, using in-house CT-MRI dataset and BraTS2023 FLAIR-T1 MRI
dataset.

本研究提出了一种基于 2D Brownian bridge 扩散模型的创新风格关键条件和切片间轨迹对齐采样方法，以实现一致的 3D CT 到 MRI 转换。实验结果表明，相较于现有的 2D 和 3D 基线模型，我们的方法在 3D 医学图像转换方面表现出更高的质量，使用了内部 CT-MRI 数据集和 BraTS2023 FLAIR-T1 MRI 数据集。

2D 布朗桥扩散模型下的切片一致性 3D 脑 CT 到 MRI 转换

Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D  Brownian Bridge Diffusion Model

Machine unlearning is a promising paradigm for removing unwanted data samples
from a trained model, towards ensuring compliance with privacy regulations and
limiting harmful biases. Although unlearning has been shown in, e.g.,
classification and recommendation systems, its potential in medical
image-to-image translation, specifically in image recon-struction, has not been
thoroughly investigated. This paper shows that machine unlearning is possible
in MRI tasks and has the potential to benefit for bias removal. We set up a
protocol to study how much shared knowledge exists between datasets of
different organs, allowing us to effectively quantify the effect of unlearning.
Our study reveals that combining training data can lead to hallucinations and
reduced image quality in the reconstructed data. We use unlearning to remove
hallucinations as a proxy exemplar of undesired data removal. Indeed, we show
that machine unlearning is possible without full retraining. Furthermore, our
observations indicate that maintaining high performance is feasible even when
using only a subset of retain data. We have made our code publicly accessible.

在医学图像转换中，机器消除学习是可能的，可以用于处理偏见移除，同时保持高性能。

擦除以增强：MRI 重建中的高效数据清除

Erase to Enhance: Data-Efficient Machine Unlearning in MRI  Reconstruction

Medical Image-to-image translation is a key task in computer vision and
generative artificial intelligence, and it is highly applicable to medical
image analysis. GAN-based methods are the mainstream image translation methods,
but they often ignore the variation and distribution of images in the frequency
domain, or only take simple measures to align high-frequency information, which
can lead to distortion and low quality of the generated images. To solve these
problems, we propose a novel method called frequency domain decomposition
translation (FDDT). This method decomposes the original image into a
high-frequency component and a low-frequency component, with the high-frequency
component containing the details and identity information, and the
low-frequency component containing the style information. Next, the
high-frequency and low-frequency components of the transformed image are
aligned with the transformed results of the high-frequency and low-frequency
components of the original image in the same frequency band in the spatial
domain, thus preserving the identity information of the image while destroying
as little stylistic information of the image as possible. We conduct extensive
experiments on MRI images and natural images with FDDT and several mainstream
baseline models, and we use four evaluation metrics to assess the quality of
the generated images. Compared with the baseline models, optimally, FDDT can
reduce Fr\'echet inception distance by up to 24.4%, structural similarity by up
to 4.4%, peak signal-to-noise ratio by up to 5.8%, and mean squared error by up
to 31%. Compared with the previous method, optimally, FDDT can reduce Fr\'echet
inception distance by up to 23.7%, structural similarity by up to 1.8%, peak
signal-to-noise ratio by up to 6.8%, and mean squared error by up to 31.6%.

提出了一种新颖的频域分解转译方法（FDDT），用于医学图像转译，通过高频和低频分量的对齐来平衡图像的身份和风格信息，并在 MRI 图像和自然图像上进行了广泛的实验评估。

基于 GAN 的医学图像增强的频域分解转换

Frequency Domain Decomposition Translation for Enhanced Medical Image  Translation Using GANs

Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that
dominate the field of medical image-to-image translation. However, neither
modes are ideal. The Pix2Pix mode has excellent performance. But it requires
paired and well pixel-wise aligned images, which may not always be achievable
due to respiratory motion or anatomy change between times that paired images
are acquired. The Cycle-consistency mode is less stringent with training data
and works well on unpaired or misaligned images. But its performance may not be
optimal. In order to break the dilemma of the existing modes, we propose a new
unsupervised mode called RegGAN for medical image-to-image translation. It is
based on the theory of "loss-correction". In RegGAN, the misaligned target
images are considered as noisy labels and the generator is trained with an
additional registration network to fit the misaligned noise distribution
adaptively. The goal is to search for the common optimal solution to both
image-to-image translation and registration tasks. We incorporated RegGAN into
a few state-of-the-art image-to-image translation methods and demonstrated that
RegGAN could be easily combined with these methods to improve their
performances. Such as a simple CycleGAN in our mode surpasses latest NICEGAN
even though using less network parameters. Based on our results, RegGAN
outperformed both Pix2Pix on aligned data and Cycle-consistency on misaligned
or unpaired data. RegGAN is insensitive to noises which makes it a better
choice for a wide range of scenarios, especially for medical image-to-image
translation tasks in which well pixel-wise aligned data are not available

本篇论文提出一种新的无监督模式 RegGAN，使用 loss-correction 理论解决 medical image-to-image translation 中图像对齐问题，对于包括医学影像在内的广泛场景中无须像素精确对齐的图像，RegGAN 表现比 Pix2Pix 和 Cycle-consistency 更好。