基于频域自适应隐式神经表征的医学影像压缩方法

doi:10.12122/j.issn.1673-4254.2026.07.23

摘要/Abstract

摘要：

目的为解决在医学影像数据量呈指数级增长的背景下数据存储与传输受限问题，对隐式神经网络（INR）进行优化，提出频域自适应隐式神经网络（FAINC）的医学影像压缩方法。方法基于KiTS19和AVT数据集的356例患者CT数据进行回顾性分析，构建由多子网络协作的FAINC压缩方法。该方法通过光谱稀疏指数（SSI）评估数据块频域复杂度，接着采用频域门控机制将其动态分配至不同容量的子网络，最终结合参数量化与熵编码实现压缩输出。为验证性能，将所提方法与主流商业压缩标准（H.265/HEVC、JPEG2000）、隐式神经表示方法NeRV以及深度学习压缩方法DVC进行系统对比。结果在高压缩率时FAINC在KiTS19和AVT数据集均表现出最优重建性能。当比特率分别为BPV=0.32和BPV=0.34时，FAINC分别实现最高的PSNR（47.03和50.76）、最高的SSIM指标（0.9853和0.9930）以及最低的RMSE指标（0.0045和0.0029）。主观图像质量评分亦显著优于其他方法（P<0.05）。消融实验表明，频域门控与参数动态分配机制分别贡献约2.05dB和1.87dB的PSNR提升。结论实验结果证明，该方法在高压缩比下能够显著提高医学影像的重建质量，并在结构保真度和压缩效率方面优于现有主流方法。为医学影像的高效存储及低带宽远程传输提供技术支撑。

关键词: 医学影像压缩, 隐式神经表征, 频域自适应建模, 高保真重建

Abstract:

Objective To address the limitations of data storage and transfer caused by exponential growth of medical imaging data size, we propose a frequency-adaptive implicit neural compression (FAINC) method for medical images based on optimized implicit neural networks (INRs). Methods A retrospective analysis was conducted on abdominal CT data from 356 patients in the KiTS19 and AVT datasets. We developed the FAINC method, a multi-subnetwork collaborative compression framework, which first evaluates the frequency-domain complexity of image blocks using the Spectral Sparsity Index (SSI), and then dynamically allocates them to subnetworks of different capacities through a frequency-domain gating mechanism. Compression output is achieved by combining parameter quantization with entropy coding. To assess its performance, the proposed method was compared with mainstream commercial compression standards (H.265/HEVC and JPEG2000), the implicit neural representation method NeRV, and the deep-learning-based compression method DVC. Results The FAINC method achieved the best reconstruction performance on both KiTS19 and AVT datasets at high compression ratios. At bitrates of BPV=0.32 and BPV=0.34, the FAINC method obtained the highest PSNR (47.03 and 50.76), the highest SSIM (0.9853 and 0.9930), and the lowest RMSE (0.0045 and 0.0029), achieving also a significantly higher subjective image quality score than other methods. Ablation studies demonstrated that the frequency-domain gating mechanism and dynamic parameter allocation contributed approximately 2.05 dB and 1.87 dB PSNR improvements, respectively. Conclusion The proposed method substantially enhances image reconstruction quality at high compression ratios and outperforms the existing mainstream approaches in terms of structural fidelity and compression efficiency. The FAINC method provides a promising technical solution for efficient storage and low-bandwidth remote transfer of medical image data.

Key words: medical image compression, implicit neural representation, frequency-adaptive modeling, high-fidelity reconstruction

宋含笑, 石华仙, 李颖, 边兆英, 曾栋. 基于频域自适应隐式神经表征的医学影像压缩方法[J]. 南方医科大学学报, 2026, 46(7): 1703-1713.

Hanxiao SONG, Huaxian SHI, Ying LI, Zhaoying BIAN, Dong ZENG. A frequency-adaptive implicit neural representation method for medical image compression[J]. Journal of Southern Medical University, 2026, 46(7): 1703-1713.

图/表 12

图1 本文提出的FAINC总体架构

Fig.1 Overall architecture of the proposed frequency-adaptive implicit neural compression (FAINC) method.

图2 在KiTS19数据集上, 不同压缩方法的率失真曲线

Fig.2 Rate distortion curves of different compression methods on the KiTS19 dataset. A: BPV-PSNR curves. B: BPV-SSIM curves.

表1 KiTS19 数据集上, 各方法在20例三维CT影像上的重建指标均值及方差（BPV约为0.32-0.34）

Tab.1 Mean and variance of reconstruction metrics for different methods on 20 3D CT volumes from the KiTS19 dataset with BPV of 0.32-0.34

Methods	PSNR	PSNR (Rel. ↑ %)	SSIM	SSIM (Rel. ↑ %)	RMSE	RMSE (Rel. ↓ %)
FAINC	47.0346±0.7289	-	0.9853±0.0015	-	0.0045±0.0004	-
JPEG2000	45.7261±0.4892	2.86	0.9800±0.0016	0.54	0.0052±0.0003	13.46
H.265	43.8230±0.2628	7.34	0.9629±0.0039	2.32	0.0064±0.0002	29.69
H.264	42.0655±0.3790	11.81	0.9539±0.0038	3.30	0.0079±0.0004	43.04
JPEG	42.0379±0.3820	11.87	0.9539±0.0039	3.30	0.0079±0.0004	43.04
NeRV	43.7035±1.2676	7.62	0.9716±0.0116	1.41	0.0124±0.0016	63.71

图3 在相近压缩比(BPV=0.34-0.36)下不同方法于KiTS19数据集上的影像重建结果视觉对比

Fig.3 Visual comparison of reconstructed images produced by different methods on the KiTS19 dataset under similar compression ratios (BPV=0.34-0.36). A1-A3: Ground Truth; B1-B3: FAINC; C1-C3: H.265; D1-D3: H.264; E1-E3: NeRV; F1-F3: JPEG. The display window is [-160, 240]HU.

表2 FAINC压缩KiTS19数据集后重建图像的整体质量评分统计结果

Tab.2 Overall image quality scores for reconstructed images after FAINC compression of the KiTS19 dataset

Method	Mean±SD	P
FAINC	4.72±0.45
JPEG2000	4.62±0.58	0.019
H.265	4.23±0.70	<0.001
H.264	3.97±0.64	<0.001
JPEG	3.45±0.75	<0.001
NeRV	2.35±0.61	<0.001

图4 在AVT数据集上, 不同压缩方法的率失真曲线

Fig.4 Rate distortion curves of different compression methods on the AVT dataset. A: BPV-PSNR curves. B: BPV-SSIM curves.

表3 AVT数据集上, 各方法在18例三维CT影像上的重建指标均值及方差（BPV约为 0.33-0.35）

Tab.3 Mean and variance of reconstruction metrics for different methods on 18 3D CT volumes from the AVT dataset (BPV=0.33-0.35)

Methods	PSNR	PSNR (Rel. ↑ %)	SSIM	SSIM (Rel. ↑ %)	RMSE	RMSE (Rel. ↓ %)
FAINC	50.7630±0.6820	-	0.9930±0.0010	-	0.0029±0.0004	-
JPEG2000	50.9773±1.2549	-0.42	0.9918±0.0019	0.12	0.0031±0.0004	6.45
H.265	49.1827±0.9431	3.21	0.9872±0.0085	0.59	0.0035±0.0004	17.14
H.264	44.8626±1.0237	13.15	0.9564±0.0117	3.83	0.0058±0.0008	50.00
JPEG	44.8287±1.0157	13.24	0.9563±0.0117	3.84	0.0058±0.0008	50.00
NeRV	43.1059±4.5098	17.77	0.9701±0.0220	2.36	0.0047±0.0014	38.30

图5 在相近压缩比(BPV=0.34~0.36)下不同方法于AVT数据集上的影像重建结果视觉对比

Fig.5 Visual comparison of reconstructed images produced by different methods on the AVT dataset under similar compression ratios (BPV=0.34-0.36). A1-A3: Ground Truth; B1-B3: FAINC; C1-C3: H.265; D1-D3: H.264; E1-E3: NeRV; F1-F3: JPEG. The display window is [-160, 240]HU.

表4 FAINC压缩AVT数据集后重建图像的整体质量评分统计结果

Tab.4 Overall image quality scores for reconstructed images after FAINC compression of the AVT dataset

Method	Mean ± SD	P
FAINC	4.77±0.43
JPEG2000	4.55±0.50	0.005
H.265	4.20±0.58	0.002
H.264	3.92±0.46	<0.001
JPEG	3.55±0.65	<0.001
NeRV	2.72±0.69	<0.001

图6 FAINC与两种消融实验模型的医学影像重建视觉效果对比

Fig.6 Visual comparison of reconstructed medical images by FAINC and two ablation models. A1-A2: Ground Truth; B1-B2: FAINC; C1-C2: W/o Frequency-Guided Partition; D1-D2: W/o Adaptive Allocation. The display window is [-160, 240]HU.

图7 FAINC与两种消融实验模型的率-失真曲线比较

Fig.7 Comparison of rate-distortion curves for FAINC and two ablation models.

图8 FAINC与两种消融模型在不同子网络上的PSNR与参数规模对比

Fig.8 PSNR and parameter scale comparison across multiple subnetworks for FAINC and two ablation models. Top: Reconstruction quality (PSNR); bottom: Sub-network memory usage (MB).

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