低剂量ct图像去噪pycharm代码

### 实现低剂量CT图像去噪的Python代码

针对低剂量CT(LDCT)图像中存在的噪声问题，可以采用多种深度学习模型来提升图像质量。下面提供了一个基于U-Net架构并融合ConvNeXt模块的简化版实现方案[^1]。

#### 导入必要的库

import torch
from torch import nn, optim
import torchvision.transforms as transforms
from PIL import Image

#### 定义改进型UNet结构
此部分定义了带有修改过的ConvNeXt单元作为基础构建块的神经网络框架。

class ModifiedConvNeXtBlock(nn.Module):
    def __init__(self, channels, group_size=8):
        super(ModifiedConvNeXtBlock, self).__init__()
        
        # 使用不同尺寸内核替代单一7x7卷积层
        self.conv_layers = nn.Sequential(
            nn.Conv2d(channels, channels * 2, kernel_size=(3, 3), padding='same', groups=group_size),
            nn.ReLU(),
            nn.Conv2d(channels * 2, channels, kernel_size=(5, 5), padding='same'),
            nn.ReLU()
        )

    def forward(self, x):
        return self.conv_layers(x)

class UNeXtModel(nn.Module):
    def __init__(self, input_channels=1, output_channels=1):
        super(UNeXtModel, self).__init__()

        base_channel_num = 64
        
        # 编码器路径
        self.encoder_blocks = nn.ModuleList([
            ModifiedConvNeXtBlock(base_channel_num*(2**i)) for i in range(4)
        ])
    
        # 解码器路径
        self.decoder_blocks = nn.ModuleList([
            ModifiedConvNeXtBlock(base_channel_num*(2**(3-i))) for i in range(4)
        ])

        # 输出转换
        self.final_conv = nn.Conv2d(base_channel_num, output_channels, kernel_size=1)

    def forward(self, inputs):
        skips = []
        current_input = inputs

        # 下采样阶段
        for enc_block in self.encoder_blocks:
            out = enc_block(current_input)
            skips.append(out)
            current_input = nn.MaxPool2d(kernel_size=2)(out)

        # 上采样恢复空间维度
        for dec_idx, dec_block in enumerate(self.decoder_blocks):
            upsampled = nn.Upsample(scale_factor=2)(current_input)
            concat_features = torch.cat([upsampled, skips[-dec_idx-1]], dim=1)
            current_input = dec_block(concat_features)

        final_output = self.final_conv(current_input)
        return final_output

#### 数据预处理与加载
为了使模型能够接收正确的输入格式，需对原始CT扫描数据执行标准化变换。

transform_pipeline = transforms.Compose([
    transforms.ToTensor(), 
    transforms.Normalize(mean=[0.5], std=[0.5])
])

def load_image(path):
    img = Image.open(path).convert('L')  # 转换成灰度模式
    tensor_img = transform_pipeline(img)
    return tensor_img.unsqueeze(dim=0)   # 添加批次维度

#### 训练过程概览
这里仅给出训练循环的大致框架；实际应用时应根据具体情况进行调整优化。

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = UNeXtModel().to(device=device)

criterion = nn.MSELoss()  # 或者其他适合的任务特定损失函数
optimizer = optim.Adam(model.parameters())

for epoch in range(num_epochs):
    running_loss = 0.
    for batch_data in dataloader:
        ldct_images, ndct_images = map(lambda t: t.to(device), batch_data)

        optimizer.zero_grad()

        predictions = model(ldct_images)
        loss_value = criterion(predictions, ndct_images)

        loss_value.backward()
        optimizer.step()

        running_loss += loss_value.item()

上述代码片段展示了一个基本的工作流程，适用于初步实验验证目的。然而，在投入生产环境之前还需要进一步完善细节配置以及超参数调优等工作。