Anisotropic volumetric acquisitions are common in clinical MRI and volume electron microscopy (vEM), where sparse through-plane sampling creates thick slices or sections that degrade orthogonal reformats and downstream analysis. We present CRIS, a cross-plane self-supervised framework for isotropic restoration without paired isotropic ground truth. CRIS casts 3D restoration as 2D stripe completion on orthogonal reformats of an isotropic grid: high-resolution in-plane slices are synthetically degraded and periodically masked for training, while at inference blank slices define the isotropic grid, two orthogonal reformats are restored, and predictions are fused by multi-view averaging. We evaluate CRIS on two MRI cohorts and two microscopy benchmarks up to 8x anisotropy. On brain MRI, CRIS achieves 32.921 +/- 0.436 dB PSNR and 0.9631 +/- 0.0027 SSIM, outperforming interpolation, SMORE4, SIMPLE, SA-INR, and ATME, and gives the best segmentation consistency (Dice 0.940 +/- 0.004, ASSD 0.245 +/- 0.014 mm, HD99 1.275 +/- 0.061 mm). On reference-free abdominal MRI, CRIS reduces FID/KID to 48.714/0.023. On vEM, CRIS outperforms interpolation, NIIV, and vEMINR, reaching 29.133 dB/0.834 3D PSNR/SSIM at 4x, 27.123 dB/0.734 on EPFL at 8x, and 21.915 dB/0.699 on noisy hemibrain data. In a robustness experiment, one variable-gap CRIS model evaluated across gap factors 3--7 and coronal, axial, and sagittal degradations maintained higher PSNR/SSIM than interpolation (36.36--31.14 dB and 0.977--0.932 vs. 33.07--27.85 dB and 0.951--0.853). These results support CRIS as a modality-flexible route to isotropic restoration without paired isotropic targets or configuration-specific retraining. Code is available at https://github.com/adi-hatav/CRIS.

翻译：各向异性体积采集在临床MRI和体积电子显微镜（vEM）中十分常见，稀疏的跨平面采样会产生厚切片或厚截面，从而降低正交重格式图像的质量并干扰下游分析。我们提出CRIS，一种无需配对各向同性真实标签的跨平面自监督各向同性恢复框架。CRIS将三维恢复问题转化为各向同性网格上正交重格式的二维条纹补全任务：训练时，高分辨率面内切片被合成退化并周期性掩蔽；推理时，空白切片定义各向同性网格，对两个正交重格式进行恢复，并通过多视图平均融合预测结果。我们在两个MRI队列和两个显微镜基准数据集上对CRIS进行评估，各向异性倍数高达8倍。在脑部MRI中，CRIS达到32.921±0.436 dB的PSNR和0.9631±0.0027的SSIM，优于插值法、SMORE4、SIMPLE、SA-INR和ATME，并取得最佳分割一致性（Dice 0.940±0.004，ASSD 0.245±0.014 mm，HD99 1.275±0.061 mm）。在无参考腹部MRI中，CRIS将FID/KID降至48.714/0.023。在vEM中，CRIS优于插值法、NIIV和vEMINR，在4倍各向异性下达到29.133 dB/0.834的三维PSNR/SSIM，在EPFL数据集8倍各向异性下达到27.123 dB/0.734，在含噪的半脑数据上达到21.915 dB/0.699。在鲁棒性实验中，单个可变间距CRIS模型在3至7倍间隙因子及冠状面、轴面和矢状面退化条件下评估，其PSNR/SSIM均高于插值法（36.36–31.14 dB和0.977–0.932对比33.07–27.85 dB和0.951–0.853）。这些结果证明CRIS是一种无需配对各向同性目标或特定配置重训练的模态灵活的各向同性恢复方案。代码已开源：https://github.com/adi-hatav/CRIS。