MRI-to-CT Translation of Spinal Images via Anatomy-Guided Pseudo-Pairing and Adversarial Diffusion

Eduardo Nieto; Ibai Genua; Davide Scorza; Karen López-Linares

cs.CVOct 27, 2026

MRI-to-CT Translation of Spinal Images via Anatomy-Guided Pseudo-Pairing and Adversarial Diffusion

Authors: Eduardo Nieto, Ibai Genua, Davide Scorza, Karen López-Linares

Organizations: Fundación Vicomtech, Basque Research and Technology Alliance (BRTA), Donostia - San Sebastián, Spain · R&D Department, Cyber Surgery, Donostia - San Sebastián, Spain · eHealth Group, Bioengineering Area, Biogipuzkoa Health Research Institute, Donostia - San Sebastián, Spain

Abstract

Magnetic Resonance Imaging (MRI) is routinely used to diagnose spinal disorders, but surgical planning often requires Computed Tomography (CT) for better visualization of vertebral bone anatomy. Synthesizing CT from MRI could avoid additional radiation exposure and streamline preoperative assessment. We propose a three-stage framework for spine MRI-to-CT synthesis. First, an anatomy-guided vertebral matching system identifies similar sagittal MRI and CT slices using spine segmentations and aligns them through rigid registration, creating pseudo-paired data from unpaired scans. Second, these pairs train an adversarial diffusion model for MRI-to-CT translation. Third, synthesized slices are assembled into 3D volumes and refined by a network that improves inter-slice continuity and structural coherence. The framework was trained on over 10,000 unpaired sagittal MRI and CT slices and evaluated on 1,911 slices from 83 MRI volumes spanning all spinal regions. Assessment combined distribution-based measures with vertebral segmentation metrics. Compared with diffusion alone, the proposed approach increased binary 3D vertebral Dice from 0.666 to 0.739. Volumetric post-processing reduced visible discontinuities between stacked slices and improved vertebral coherence in axial and coronal views. Combining anatomy-guided pseudo-pairing, adversarial diffusion and 3D refinement improves spine MRI-to-CT synthesis. Vertebral matching primarily enhanced anatomical alignment and fidelity, while volumetric refinement improved 3D consistency. These findings support further development of MRI-derived CT-like visualization for preoperative planning, particularly to preserve fine intensity details while maintaining volumetric coherence.

Figures & tables

Figure 1: Data flow diagram illustrating the full pipeline from raw data to the final dataset construction. Starting from 2844 CT scans and 560 MR scans, vertebral segmentations are obtained via TotalSegmentator, followed by manual filtering and region-based subvolume extraction. Subvolumes are then automatically filtered based on the quality of their segmentation masks, discarding those with anomalous or incomplete vertebral segmentations. Since the downstream translation model operates on 2D, sagittal slices are extracted from the subvolumes. Finally, the resulting slices are split into training, validation and test sets.

Split	Region	MR	CT	MR Slices	CT Slices
Train	Cervical	198	340	4532	10303
	Cervical & Thoracic	13	334	297	9984
	Thoracic	12	3028	270	82984
	Thoracic & Lumbar	1	293	24	8280
	Lumbar	261	2412	6196	85416
Validation	Cervical	60	107	1371	3228

Table 1: Dataset split summary after sagittal slice extraction. MR and CT columns indicate the number of subvolumes per modality, while MR Slices and CT Slices reflect the total number of 2D sagittal slices extracted from the subvolumes. The statistics are further stratified by spinal region. A vertebral region is considered present if it contains at least 50% of the possible vertebrae from that region (e.g 4 out of 7 cervical vertebrae).

Figure 2: Example of MR (left) and CT (right) patches extraction after preprocessing. From each scan, multiple overlapping patches covering different spinal regions are extracted, as shown by the individual patch examples on the right of each scan. Unlike CT, where redundant patches from the same scan are allowed, MR patches are filtered to avoid patches with similar spine information, resulting in a significantly smaller number of extracted MR patches overall.

Dataset	MR-CT pseudo-pairs	Mean DSC	Differing vertebrae
			0	1	$>1$
Train	11319	0.6374	6459	4855	5
Validation	3555	0.6340	1825	1728	2
Test	1911	0.6180	975	928	8

Table 2: Vertebral matching system results for the different datasets. The mean DSC value represents the average overlap between the MR and CT spines in the generated pseudo-pairs. The differing vertebrae indicates the number of vertebral mismatches between the sagittal slices of every generated pseudo-pair, with 0 meaning both slices have the same vertebrae, 1 meaning there is at least one vertebra that is not present in both slices and ¿1 meaning there is more than 1 vertebrae that is not present in both slices.

Figure 3: Vertebral matching system overview. Figure illustrates how every MR sagittal slice is paired with a slice of the CT corresponding set, trying to maximize the spine similarity and overlap between each pair through a rigid registration process. Once paired, both slices are cropped to avoid empty pairings resulting from excessive rotations or shifts from rigid registration.

Figure 4: Vertebral matching system empirical evaluation on the test dataset. The top row shows the obtained weighted DSC, execution time and matching efficiency (DSC increase per second of execution) as a function of the number of evaluated candidates. The bottom plot applies the elbow method to find the optimal trade-off between patch spinal similarity (DSC) and computational cost, identifying 50 candidates as the optimal selection.

Figure 5: Overview of the post-processing net architecture. The ResUNet generator takes a synthetic CT as input and produces a corrected CT output, trying to increase its volumetric consistency and coherence. Residual skip connections concatenate encoder and decoder feature maps, enabling multi-scale feature fusion and improved anatomical detail preservation. The PatchGAN discriminator classifies local regions of the output as real or fake to provide adversarial supervision.

Figure 6: Example of the simulated discontinuity artifacts generated through the vertebral matching system a on real CT volume.

Figure 7: Progressive qualitative impact of each pipeline stage. Rows show representative and challenging examples across different spinal regions.

	Global realism		Vertebral fidelity
Method	rMMD ↓	Full FID ↓	TS 3D DSC (bin.)↑	TS 3D DSC ↑	Vertebral FID ↓	EMD ↓	JSD ↓
SynDiff baseline	0.135	77.84	0.666±0.07	0.435±0.17	41.57	111.2±61.1	0.308±0.13
+ pseudo-matching	0.053	80.88	0.739±0.04	0.564±0.15	33.64	85.7±44	0.267±0.13
+ 3D post-processing	0.074	87.41	0.732±0.04	0.575±0.133	50.37	76.8±63.2	0.213±0.14

Table 3: Incremental contribution of each pipeline stage. Each row adds a component on top of the previous one. Global realism metrics evaluate the overall image quality, while vertebral fidelity metrics asses the anatomical consistency of the generated CT. TS 3D DSC reports the multiclass Dice per vertebra, while TS 3D DSC (bin.) treats all vertebrae as a single foreground class. Values are reported in a mean

\pm

and standard deviation (when available) format computed over the 83 volumes of the test set.

\uparrow

indicates higher is better,

\downarrow

lower is better.

Figure 8: Vertebra-wise intensity fidelity before and after 3D refinement.

Figure 9: Volumetric vertebral coherence before and after 3D post-processing, seen through the vertebral segmentations in the different anatomical planes. The left column shows the MR ground truth segmentation, the middle column the synthetic CT segmentation obtained with the 2D translation model and the vertebral matching system approach and the right column shows the result after applying the 3D post-processing network.

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