Diffusion models face significant challenges when employed for large-scale
medical image reconstruction in real practice such as 3D Computed Tomography
(CT). Due to the demanding memory, time, and data requirements, it is difficult
to train a diffusion model directly on the entire volume of high-dimensional
data to obtain an efficient 3D diffusion prior. Existing works utilizing
diffusion priors on single 2D image slice with hand-crafted cross-slice
regularization would sacrifice the z-axis consistency, which results in severe
artifacts along the z-axis. In this work, we propose a novel framework that
enables learning the 3D image prior through position-aware 3D-patch diffusion
score blending for reconstructing large-scale 3D medical images. To the best of
our knowledge, we are the first to utilize a 3D-patch diffusion prior for 3D
medical image reconstruction. Extensive experiments on sparse view and limited
angle CT reconstruction show that our DiffusionBlend method significantly
outperforms previous methods and achieves state-of-the-art performance on
real-world CT reconstruction problems with high-dimensional 3D image (i.e.,
$256 \times 256 \times 500$). Our algorithm also comes with better or
comparable computational efficiency than previous state-of-the-art methods.

通过位置感知的三维图像块扩散评分融合，本文提出了一种新的框架，能够学习三维图像的先验进行大规模三维医学图像重建，实验表明我们的方法在稀疏视图和有限角度的 CT 重建上显著优于先前的方法，并且在高维三维图像（$256 	imes 256 	imes 500$）的实际 CT 重建问题上获得了最先进的性能，同时算法具有更好或相当的计算效率。

DiffusionBlend: 通过位置感知扩散分数混合学习三维图像先验，用于三维计算机断层扫描重建

DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion  Score Blending for 3D Computed Tomography Reconstruction

Mild Traumatic Brain Injury (mTBI) is a common and challenging condition to
diagnose accurately. Timely and precise diagnosis is essential for effective
treatment and improved patient outcomes. Traditional diagnostic methods for
mTBI often have limitations in terms of accuracy and sensitivity. In this
study, we introduce an innovative approach to enhance mTBI diagnosis using 3D
Computed Tomography (CT) images and a metric learning technique trained with
triplet loss. To address these challenges, we propose a Residual Triplet
Convolutional Neural Network (RTCNN) model to distinguish between mTBI cases
and healthy ones by embedding 3D CT scans into a feature space. The triplet
loss function maximizes the margin between similar and dissimilar image pairs,
optimizing feature representations. This facilitates better context placement
of individual cases, aids informed decision-making, and has the potential to
improve patient outcomes. Our RTCNN model shows promising performance in mTBI
diagnosis, achieving an average accuracy of 94.3%, a sensitivity of 94.1%, and
a specificity of 95.2%, as confirmed through a five-fold cross-validation.
Importantly, when compared to the conventional Residual Convolutional Neural
Network (RCNN) model, the RTCNN exhibits a significant improvement, showcasing
a remarkable 22.5% increase in specificity, a notable 16.2% boost in accuracy,
and an 11.3% enhancement in sensitivity. Moreover, RTCNN requires lower memory
resources, making it not only highly effective but also resource-efficient in
minimizing false positives while maximizing its diagnostic accuracy in
distinguishing normal CT scans from mTBI cases. The quantitative performance
metrics provided and utilization of occlusion sensitivity maps to visually
explain the model's decision-making process further enhance the
interpretability and transparency of our approach.

使用三维计算机断层扫描图像和基于度量学习技术的残余三元组卷积神经网络模型提高轻度创伤性脑损伤（mTBI）的诊断，通过嵌入特征空间将 CT 扫描者区分为 mTBI 病例和正常者，并实现 94.3% 的平均准确率、94.1% 的灵敏度和 95.2% 的特异度，并相较于传统的残余卷积神经网络模型，在诊断准确性上有显著提升，同时需要更少的内存资源，提高了资源利用率。

使用 3D CT 的剩余三元卷积神经网络增强轻度脑损伤诊断

Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural  Network Using 3D CT

Hierarchical shifted window transformers (Swin) are a computationally
efficient and more accurate alternative to plain vision transformers. Masked
image modeling (MIM)-based pretraining is highly effective in increasing
models' transferability to a variety of downstream tasks. However, more
accurate and efficient attention guided MIM approaches are difficult to
implement with Swin due to it's lack of an explicit global attention. We thus
architecturally enhanced Swin with semantic class attention for self-supervised
attention guided co-distillation with MIM. We also introduced a noise injected
momentum teacher, implemented with patch dropout of teacher's inputs for
improved training regularization and accuracy. Our approach, called
\underline{s}elf-distilled \underline{m}asked \underline{a}ttention MIM with
noise \underline{r}egularized \underline{t}eacher (SMART) was pretrained with
\textbf{10,412} unlabeled 3D computed tomography (CT)s of multiple disease
sites and sourced from institutional and public datasets. We evaluated SMART
for multiple downstream tasks involving analysis of 3D CTs of lung cancer (LC)
patients for: (i) [Task I] predicting immunotherapy response in advanced stage
LC (n = 200 internal dataset), (ii) [Task II] predicting LC recurrence in early
stage LC before surgery (n = 156 public dataset), (iii) [Task III] LC
segmentation (n = 200 internal, 21 public dataset), and (iv) [Task IV]
unsupervised clustering of organs in the chest and abdomen (n = 1,743 public
dataset) \underline{without} finetuning. SMART predicted immunotherapy response
with an AUC of 0.916, LC recurrence with an AUC of 0.793, segmented LC with
Dice accuracy of 0.81, and clustered organs with an inter-class cluster
distance of 5.94, indicating capability of attention guided MIM for Swin in
medical image analysis.

Hierarchical shifted window transformers (Swin) were architecturally enhanced with semantic class attention for self-supervised attention guided co-distillation with masked image modeling (MIM), resulting in SMART. SMART, pretrained with 10,412 unlabeled 3D computed tomography (CTs), demonstrated high performance in multiple downstream tasks involving lung cancer (LC) analysis, including predicting immunotherapy response, LC recurrence, LC segmentation, and unsupervised clustering of organs in the chest and abdomen, without finetuning.

自主训练的掩蔽关注引导的掩蔽图像建模与噪音约束教师 (SMART) 用于医学图像分析

Self-distilled Masked Attention guided masked image modeling with noise  Regularized Teacher (SMART) for medical image analysis

We present a novel conditional Generative Adversarial Network (cGAN)
architecture that is capable of generating 3D Computed Tomography scans in
voxels from noisy and/or pixelated approximations and with the potential to
generate full synthetic 3D scan volumes. We believe conditional cGAN to be a
tractable approach to generate 3D CT volumes, even though the problem of
generating full resolution deep fakes is presently impractical due to GPU
memory limitations. We present results for autoencoder, denoising, and
depixelating tasks which are trained and tested on two novel COVID19 CT
datasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from
12.53 - 46.46 dB, and the Structural Similarity index ( SSIM) range from 0.89
to 1.

利用条件生成对抗网络可以从噪声和 / 或像素化的近似中生成三维计算机体层扫描，该方法在 COVID19 CT 方面表现出色，可用于图像降噪和复原任务。