When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation

Valentin Boussot; Cedric Hemon; Caroline Lafond; Jean-Claude Nunes; Jean-Louis Dillenseger

cs.CVSep 24, 2026

When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation

Authors: Valentin Boussot, Cedric Hemon, Caroline Lafond, Jean-Claude Nunes, Jean-Louis Dillenseger

Organizations: Univ. Rennes, CLCC Eugene Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France

Abstract

Supervised synthetic CT (sCT) generation is commonly trained and evaluated as voxel-wise regression against registered reference CT images. In practice, MRI-CT and CBCT-CT pairs are aligned through registration procedures that leave residual misalignments. These residuals are not independent intensity noise but spatially coherent geometric discrepancies that act as structured label noise. We investigate how this registration-induced bias affects supervised MRI-to-CT and CBCT-to-CT synthesis on 1,784 paired patients covering five anatomical regions. Voxel-wise scores strongly depend on the consistency between the registration used to build the training targets and the one used for evaluation: models score best when both conventions match, showing that networks partly learn the geometric convention of the registration pipeline and that standard metrics reward it. Training on more anatomically consistent registrations reduces prediction variability and improves out-of-distribution robustness, and CT-only controls show that registration alone produces metric errors in the range of top challenge submissions. To mitigate the limits of voxel-wise supervision, we introduce a perceptual loss computed in the feature space of a pretrained Segment Anything encoder. Compared with MAE-only and VGG-based objectives, it improves downstream segmentation and yields sharper, more structurally coherent sCT. Perceptual and voxel-wise metrics disagree under imperfect alignment and agree when the evaluation geometry is reliable. These results identify registration-induced bias as a central confounder in supervised sCT generation and argue for complementing voxel-wise agreement with anatomy-oriented evaluation criteria.

Figures & tables

Fig. 1: Overview of the synthesis and evaluation protocol used to study registration-induced bias. Paired MR/CBCT and planning CT data are first converted into supervised training pairs through ELX or IMPACT registration conventions, yielding registered CT targets used as supervision labels for a 2.5D U-Net++ trained with MAE, VGG, or SAM-based objectives. Rectangular blocks denote data, targets, or outputs, whereas rounded blocks denote processing modules, training components, metrics, or analysis toolboxes; dashed blocks indicate independent or OOD evaluation geometries. The resulting synthetic CT images are evaluated using reference-based, anatomy-oriented, and dose-based metrics, complemented by independent/OOD test settings and CT-derived controls that isolate robustness and registration-induced metric sensitivity. Small tags indicate where the corresponding protocol components and analyses are defined in the manuscript. The robustness and benchmark branch distinguishes the independent/OOD geometry from ELX- and IMPACT-based evaluation registrations.

Region	Rigid	ELX	IMPACT
Pelvis	0.64	0.70	0.73
Abdomen	0.63	0.71	0.70
Thorax	0.61	0.66	0.71
Head & Neck	0.72	0.75	0.81
Brain	0.71	0.80	0.84
Mean	0.662	0.724	0.758

TABLE I: Mean Dice coefficient per anatomical region for the evaluated registration approaches. Higher values indicate better anatomical alignment between the registered source-modality structures and the reference CT structures.

	Task 1			Task 2
Train/Eval	MAE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	MAE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$
IMPACT/IMPACT	63.55	29.97	0.927	58.76	31.16	0.937
IMPACT/ELX	72.47	28.49	0.918	69.17	29.13	0.920
ELX/IMPACT	68.31	29.37	0.920	65.70	30.08	0.921
ELX/ELX	66.98	29.24	0.924	61.28	30.22	0.930

TABLE II: Aggregate in-distribution performance for all combinations of registration strategies used during training and evaluation. Results are pooled across the AB, HN, and TH regions. Complete regional results are reported in Appendix C-A .

Task	Region	IMPACT			ELX
		MAE	PSNR	SSIM	MAE	PSNR	SSIM
T1	Ext-T2	88.54 [0.4ex] $\pm$ 12.51	26.82 [0.4ex] $\pm$ 0.91	0.899 [0.4ex] $\pm$ 0.025	97.43 ∗∗∗ [0.4ex] $\pm$ 14.83	26.19 ∗∗∗ [0.4ex] $\pm$ 0.87	0.889 ∗∗∗ [0.4ex] $\pm$ 0.031
T2	$AB_{\mathrm{sim}}$	60.41 [0.4ex] $\pm$ 14.83	30.32 [0.4ex] $\pm$ 1.87	0.927 [0.4ex] $\pm$ 0.016	71.09 ∗∗∗ [0.4ex] $\pm$ 13.12	28.68 ∗∗∗ [0.4ex] $\pm$ 1.30	0.915 ∗∗∗ [0.4ex] $\pm$ 0.014
	$HN_{\mathrm{sim}}$	136.18 [0.4ex] $\pm$ 30.01	24.10 [0.4ex] $\pm$ 2.05	0.901 [0.4ex] $\pm$ 0.029	149.66 ∗∗∗ [0.4ex] $\pm$ 28.91	23.45 ∗∗∗ [0.4ex] $\pm$ 1.65	0.892 ∗∗∗ [0.4ex] $\pm$ 0.031
	$TH_{\mathrm{sim}}$	95.99 [0.4ex] $\pm$ 44.86	27.38 [0.4ex] $\pm$ 3.26	0.885 [0.4ex] $\pm$ 0.062	109.15 ∗∗∗ [0.4ex] $\pm$ 49.65	26.34 ∗∗∗ [0.4ex] $\pm$ 2.93	0.870 ∗∗∗ [0.4ex] $\pm$ 0.067

TABLE III: Quantitative results on out-of-distribution (OOD) regions for Task 1 and Task 2 in supervised cross-validation experiments. Mean

\pm

standard deviation of commonly used image similarity metrics (MAE, PSNR, SSIM) are reported. Column groups indicate the models used (IMPACT or ELX). Ext-T2 is the external T2-weighted MRI set;

AB_{\mathrm{sim}}

HN_{\mathrm{sim}}

, and

TH_{\mathrm{sim}}

are the registration-free simulated CBCT sets. Statistical comparisons against IMPACT follow the patient-level protocol described in Section III-F .

Fig. 2: Qualitative comparison between MR input and synthesized CT generated using ELX-based and IMPACT-based training.

Region	Task 1	Task 2
AB	+41.2%	+27.6%
HN	+20.7%	+1.2%
TH	+20.7%	+12.1%
AB/HN/TH	+29.7%	+12.5%
Brain	+2.7%	–
Pelvis	+116.1%	–

TABLE IV: Relative percentage increase of uncertainty for ELX compared to IMPACT. Uncertainty is estimated as the variance across the 15 predictions per patient obtained from the five cross-validation models and three test-time augmentations. Ext-T2 denotes the external T2-weighted MRI set and Sim-CBCT the registration-free simulated CBCT set.

Task	Evaluation set	MAE	VGG	SAM
Task 1	AB/HN/TH	0.711	0.712	0.738
Task 1	Brain/Pelvis	0.749	0.772	0.806
Task 1	Ext-T2	0.665	0.672	0.725
Task 2	AB/HN/TH	0.695	0.683	0.700
Task 2	$AB_{\mathrm{sim}}$	0.709	0.723	0.764
Task 2	$HN_{\mathrm{sim}}$	0.734	0.750	0.755

TABLE V: Downstream Dice scores obtained after training with MAE, VGG-based perceptual, or SAM-based perceptual supervision. All models use IMPACT-based training pairs. For Task 2,

AB_{\mathrm{sim}}

HN_{\mathrm{sim}}

, and

TH_{\mathrm{sim}}

denote registration-free simulated CBCT evaluations. Complete region-wise Dice and SSIM results are reported in Appendix C-B .

Fig. 3: Qualitative comparison of synthesized CT images obtained using different training losses (rows) across multiple anatomical cases (columns). From top to bottom: input MR images, sCT generated using MSE, MAE, MAE+VGG-based perceptual loss, and MAE+SAM-based loss, followed by the reference CT images. For each synthesized image, the corresponding MAE with respect to the reference CT is reported. While all methods produce visually plausible outputs, differences in image sharpness and structural fidelity can be observed across losses, particularly at anatomical boundaries.

			MAE supervision			SAM supervision
Task	Set	Prediction	MAE $\downarrow$	$d_{\mathrm{SAM}}\downarrow$	LPIPS $\downarrow$	MAE $\downarrow$	$d_{\mathrm{SAM}}\downarrow$	LPIPS $\downarrow$
Task 1	AB/HN/TH	Mean CV	70.75	24.27	8.34	78.26	18.99	7.06
	AB/HN/TH	CV ensemble	66.98	24.31	8.22	73.21	19.85	7.03
	Ext-T2	Mean CV	104.39	37.43	13.82	96.36	33.71	12.00
	Ext-T2	CV ensemble	97.43	37.30	13.25	90.14	35.93	11.79
Task 2	AB/HN/TH	Mean CV	64.95	17.54	5.48	69.52	13.99	4.69

TABLE VI: Aggregate performance of models trained with MAE or SAM-based supervision and evaluated with ELX references. Mean CV denotes the patient-level performance averaged across the five individual cross-validation models, whereas CV ensemble denotes performance after voxel-wise averaging of their predictions.

d_{\mathrm{SAM}}

and LPIPS values are multiplied by 100 for readability. Complete region-wise results and statistical comparisons are provided in Appendix C-C .

Region	IMPACT vs ELX
AB	124.25% ∗∗∗
HN	136.65% ∗∗∗
TH	3.59% ∗∗
AB/HN/TH	81.82% ∗∗∗
Sim-CBCT	9.65% ∗∗∗

TABLE VII: Registration-based deformation measure between the input CBCT and the synthesized image for Task 2, using the IMPACT-Reg metric M297. Values report the relative percentage difference between ELX and IMPACT. Positive values indicate larger deformation for ELX compared to IMPACT. Results are reported across anatomical regions for models trained with the SAM criterion. Statistical significance is assessed using paired Wilcoxon signed-rank tests on matched patients.

Metric	Task 1		Task 2
Metric	ELX	IMPACT	ELX	IMPACT
MAE	68.20	75.82	52.87	56.05
PSNR	29.81	28.70	32.36	31.65
SSIM	0.92	0.91	0.96	0.95
Dice	0.72	0.70	0.83	0.82
HD95	8.42	8.89	5.40	5.41

TABLE VIII: ELX- versus IMPACT-trained models on the public SynthRAD2025 validation set, as scored by the challenge server. Higher ELX scores reflect agreement with the ELX-consistent evaluation geometry, not established source-anatomy preservation.

Task	Region	Type	MAE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	$d_{\mathrm{SAM}}$ $\downarrow$	LPIPS $\downarrow$	Dice $\uparrow$
Task 1	AB	Reg.	$66.45\pm 13.58$	$28.34\pm 1.38$	$0.908\pm 0.027$	$28.448\pm 6.007$	$10.8\pm 3.5$	$0.839\pm 0.040$
	AB	Sup.	$64.89$	$29.10$	$0.91$	$26.70$	$10.7$	$0.777$
	HN	Reg.	$62.96\pm 13.87$	$29.43\pm 1.71$	$0.953\pm 0.020$	$11.342\pm 2.975$	$2.5\pm 0.8$	$0.818\pm 0.072$
	HN	Sup.	$65.15$	$30.20$	$0.94$	$11.96$	$3.1$	$0.731$
	TH	Reg.	$53.00\pm 13.30$	$30.71\pm 2.13$	$0.950\pm 0.014$	$21.936\pm 5.210$	$7.1\pm 2.4$	$0.803\pm 0.046$
	TH	Sup.	$60.07$	$30.76$	$0.94$	$20.50$	$7.2$	$0.706$

TABLE IX: Registration-induced metric sensitivity (Reg., CT-derived controls) versus representative supervised sCT performance (Sup.). Red: supervised result better than the control. Orange: worse by more than 10%. Uncolored: within 10%.

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Fig. 4: Qualitative comparison of registration results between IMPACT and ELX across anatomical regions (Abdomen (AB), Head-and-Neck (HN), Thorax (TH)). For each case, the fixed image (MR), moving image (CT), overlay visualization, and checkerboard fusion are shown. In the displayed cases, IMPACT-based registration shows sharper and more spatially consistent correspondences in several boundary regions, particularly at soft-tissue interfaces, whereas ELX shows more visible local discrepancies. Checkerboard views further highlight structural inconsistencies.

#	Team	MAE $\downarrow$	PSNR $\uparrow$	MS-SSIM $\uparrow$	Dice $\uparrow$	HD95 $\downarrow$
1	FelixSun (KoalAI)	64.81	29.997	0.936	0.779	6.01
2	JavierSequeiro	65.51	29.611	0.933	0.766	6.32
3	Valentin (BreizhCT)	67.24	29.957	0.935	0.737	7.51
4	siyuanmei (MixCT)	67.90	29.628	0.931	0.785	5.77
5	hanbingocean (QWER)	75.68	28.756	0.922	0.715	7.69
14	MaartenTerpstra (baseline)	309.28	18.630	0.466	0.006	136.34

Appendix

TABLE X: Official ranking for the MR

\rightarrow

CT synthesis task based on the challenge evaluation metrics. Performance is reported using commonly used image similarity metrics (MAE, PSNR, MS-SSIM) and segmentation-based metrics (Dice, HD95). The top 5 performing teams and the challenge baseline are shown. Our method (BreizhCT) ranks 3rd overall.

#	Team	Dose MAE ${}_{\gamma}\downarrow$	Dose MAE ${}_{p}\downarrow$	DVH ${}_{\gamma}\downarrow$	DVH ${}_{p}\downarrow$	GPR ${}_{\gamma}\uparrow$	GPR ${}_{p}\uparrow$
1	FelixSun (KoalAI)	0.006	0.024	0.011	0.064	98.33	84.04
2	JavierSequeiro	0.006	0.024	0.011	0.060	98.50	84.56
3	Valentin (BreizhCT)	0.006	0.027	0.013	0.067	98.88	82.19
4	siyuanmei (MixCT)	0.007	0.023	0.016	0.075	98.22	81.92
5	hanbingocean (QWER)	0.007	0.027	0.014	0.073	98.29	81.91
14	MaartenTerpstra (baseline)	0.056	0.152	0.094	0.451	78.08	59.59

Appendix

TABLE XI: Official ranking for the MR

\rightarrow

CT synthesis task based on dosimetric evaluation metrics. Performance is reported using dose-based metrics (Dose MAE, DVH) and gamma pass rate (GPR), for both

\gamma

and

p

criteria. The top 5 performing teams and the challenge baseline are shown. Our method (BreizhCT) ranks 3rd overall.

#	Team	MAE $\downarrow$	PSNR $\uparrow$	MS-SSIM $\uparrow$	Dice $\uparrow$	HD95 $\downarrow$
1	GlassCity (MixCT)	48.27	32.62	0.968	0.857	4.53
2	JavierSequeiro	52.49	31.91	0.964	0.846	4.87
3	Valentin (BreizhCT)	53.09	32.49	0.966	0.843	5.08
4	ayuan (et)	53.61	31.89	0.963	0.842	4.99
5	RicardoBrioso	62.75	31.01	0.952	0.801	6.65
14	MaartenTerpstra (baseline)	308.99	18.77	0.505	0.004	133.48

Appendix

TABLE XII: Official ranking for the CBCT

\rightarrow

CT synthesis task based on challenge evaluation metrics. Performance is reported using commonly used image similarity metrics (MAE, PSNR, MS-SSIM) and segmentation-based metrics (Dice, HD95). The top 5 performing teams and the challenge baseline are shown. Our method (BreizhCT) ranks 3rd overall.

#	Team	Dose MAE ${}_{\gamma}\downarrow$	Dose MAE ${}_{p}\downarrow$	DVH ${}_{\gamma}\downarrow$	DVH ${}_{p}\downarrow$	GPR ${}_{\gamma}\uparrow$	GPR ${}_{p}\uparrow$
1	GlassCity (MixCT)	0.004	0.017	0.013	0.034	99.300	88.640
2	JavierSequeiro	0.005	0.018	0.015	0.036	99.312	87.765
3	Valentin (BreizhCT)	0.005	0.020	0.015	0.036	99.308	86.407
4	ayuan (et)	0.005	0.018	0.015	0.039	99.227	87.405
5	RicardoBrioso	0.006	0.021	0.017	0.046	98.948	84.989
14	MaartenTerpstra (baseline)	0.036	0.094	0.184	0.482	76.393	59.136

Appendix

TABLE XIII: Official ranking for the CBCT

\rightarrow

CT synthesis task based on dosimetric evaluation metrics. Performance is reported using dose-based metrics (Dose MAE, DVH) and gamma pass rate (GPR), for both

\gamma

and

p

criteria. The top 5 performing teams and the challenge baseline are shown. Our method (BreizhCT) ranks 3rd overall.

IMPACT

ELX

Region

IMPACT

ELX

IMPACT

ELX

MAE

PSNR

SSIM

MAE

PSNR

SSIM

MAE

PSNR

SSIM

MAE

PSNR

SSIM

58.89 [0.4ex]

\pm

9.16

29.94 [0.4ex]

\pm

1.43

0.909 [0.4ex]

\pm

0.022

72.14 ∗∗∗ [0.4ex]

\pm

13.78

27.68 ∗∗∗ [0.4ex]

\pm

1.57

0.899 ∗∗ [0.4ex]

\pm

0.032

64.04 ∗∗∗ [0.4ex]

\pm

8.78

29.29 ∗∗∗ [0.4ex]

\pm

1.23

0.900 ∗∗∗ [0.4ex]

\pm

0.025

67.73 ∗∗ [0.4ex]

\pm

14.25

28.47 ∗∗∗ [0.4ex]

\pm

1.73

0.904 [0.4ex]

\pm

0.032

76.21 [0.4ex]

\pm

11.31

28.56 [0.4ex]

\pm

1.29

0.931 [0.4ex]

\pm

0.027

88.51 ∗ [0.4ex]

\pm

28.29

27.43 ∗ [0.4ex]

\pm

2.74

0.915 [0.4ex]

\pm

0.043

82.10 ∗∗∗ [0.4ex]

\pm

12.83

28.00 ∗∗∗ [0.4ex]

\pm

1.35

0.924 ∗∗∗ [0.4ex]

\pm

0.030

78.98 [0.4ex]

\pm

23.41

28.38 [0.4ex]

\pm

2.41

0.925 [0.4ex]

\pm

0.038

56.44 [0.4ex]

\pm

10.81

31.29 [0.4ex]

\pm

1.86

0.941 [0.4ex]

\pm

0.018

58.14 [0.4ex]

\pm

11.12

30.23 [0.4ex]

\pm

1.74

0.940 [0.4ex]

\pm

0.027

59.81 ∗∗∗ [0.4ex]

\pm

9.85

30.70 ∗∗∗ [0.4ex]

\pm

1.56

0.936 ∗∗∗ [0.4ex]

\pm

0.022

55.30 [0.4ex]

\pm

10.14

30.74 [0.4ex]

\pm

1.66

0.943 [0.4ex]

\pm

0.028

AB/HN/TH

63.55 [0.4ex]

\pm

13.61

29.97 [0.4ex]

\pm

1.91

0.927 [0.4ex]

\pm

0.026

72.47 ∗∗∗ [0.4ex]

\pm

22.68

28.49 ∗∗∗ [0.4ex]

\pm

2.42

0.918 ∗∗ [0.4ex]

\pm

0.038

68.31 ∗∗∗ [0.4ex]

\pm

14.27

29.37 ∗∗∗ [0.4ex]

\pm

1.78

0.920 ∗∗∗ [0.4ex]

\pm

0.030

66.98 [0.4ex]

\pm

19.27

29.24 ∗∗ [0.4ex]

\pm

2.24

0.924 [0.4ex]

\pm

0.036

Appendix

TABLE XIV: Quantitative results for Task 1 (supervised cross-validation) across anatomical regions. Mean

\pm

standard deviation of commonly used image similarity metrics (MAE, PSNR, SSIM) are reported. Column groups indicate the registration method used during training (IMPACT or ELX), while subgroups correspond to the registration method used for evaluation. Regions AB, HN, and TH correspond to in-distribution data, whereas brain and pelvis represent out-of-distribution regions not seen during training. Statistical comparisons against IMPACT/IMPACT configuration follow the patient-level protocol described in Section III-F .

IMPACT

ELX

Region

IMPACT

ELX

IMPACT

ELX

MAE

PSNR

SSIM

MAE

PSNR

SSIM

MAE

PSNR

SSIM

MAE

PSNR

SSIM

55.30 [0.4ex]

\pm

14.14

31.17 [0.4ex]

\pm

2.40

0.928 [0.4ex]

\pm

0.023

67.10 ∗∗∗ [0.4ex]

\pm

13.71

28.96 ∗∗∗ [0.4ex]

\pm

1.65

0.909 ∗∗∗ [0.4ex]

\pm

0.030

62.96 ∗∗∗ [0.4ex]

\pm

13.70

29.88 ∗∗∗ [0.4ex]

\pm

1.79

0.910 ∗∗∗ [0.4ex]

\pm

0.031

59.08 [0.4ex]

\pm

10.64

30.12 ∗ [0.4ex]

\pm

1.57

0.920 ∗∗ [0.4ex]

\pm

0.025

64.84 [0.4ex]

\pm

14.73

30.20 [0.4ex]

\pm

2.07

0.950 [0.4ex]

\pm

0.019

77.18 ∗∗∗ [0.4ex]

\pm

17.73

28.33 ∗∗∗ [0.4ex]

\pm

1.94

0.935 ∗∗∗ [0.4ex]

\pm

0.024

71.74 ∗∗∗ [0.4ex]

\pm

16.89

29.40 ∗∗∗ [0.4ex]

\pm

2.09

0.938 ∗∗∗ [0.4ex]

\pm

0.025

68.55 ∗∗ [0.4ex]

\pm

13.92

29.29 ∗∗ [0.4ex]

\pm

1.89

0.943 ∗∗∗ [0.4ex]

\pm

0.020

55.40 [0.4ex]

\pm

15.43

32.21 [0.4ex]

\pm

2.69

0.930 [0.4ex]

\pm

0.018

62.41 ∗∗ [0.4ex]

\pm

12.66

30.15 ∗∗∗ [0.4ex]

\pm

1.95

0.913 ∗∗∗ [0.4ex]

\pm

0.028

61.72 ∗∗∗ [0.4ex]

\pm

16.16

31.02 ∗∗∗ [0.4ex]

\pm

2.45

0.912 ∗∗∗ [0.4ex]

\pm

0.023

55.43 [0.4ex]

\pm

12.51

31.32 ∗ [0.4ex]

\pm

2.15

0.925 [0.4ex]

\pm

0.024

AB/HN/TH

58.76 [0.4ex]

\pm

15.47

31.16 [0.4ex]

\pm

2.53

0.937 [0.4ex]

\pm

0.022

69.17 ∗∗∗ [0.4ex]

\pm

16.24

29.13 ∗∗∗ [0.4ex]

\pm

2.01

0.920 ∗∗∗ [0.4ex]

\pm

0.030

65.70 ∗∗∗ [0.4ex]

\pm

16.36

30.08 ∗∗∗ [0.4ex]

\pm

2.24

0.921 ∗∗∗ [0.4ex]

\pm

0.030

61.28 ∗∗ [0.4ex]

\pm

13.72

30.22 ∗∗∗ [0.4ex]

\pm

2.07

0.930 ∗∗∗ [0.4ex]

\pm

0.025

Appendix

TABLE XV: Quantitative results for Task 2 (supervised cross-validation) across anatomical regions. Mean

\pm

standard deviation of commonly used image similarity metrics (MAE, PSNR, SSIM) are reported. Column groups indicate the registration method used during training (IMPACT or ELX), while subgroups correspond to the registration method used for evaluation. All regions correspond to in-distribution data used during training. Statistical comparisons against IMPACT/IMPACT configuration follow the patient-level protocol described in Section III-F .

	MAE		VGG		SAM
Region	Dice	SSIM	Dice	SSIM	Dice	SSIM
AB	0.737 ∗∗∗ [0.4ex] $\pm$ 0.044	0.909 ∗∗∗ [0.4ex] $\pm$ 0.022	0.737 ∗∗∗ [0.4ex] $\pm$ 0.046	0.904 [0.4ex] $\pm$ 0.023	0.777 [0.4ex] $\pm$ 0.043	0.905 [0.4ex] $\pm$ 0.022
HN	0.718 [0.4ex] $\pm$ 0.067	0.931 ∗∗∗ [0.4ex] $\pm$ 0.027	0.713 ∗∗ [0.4ex] $\pm$ 0.067	0.918 [0.4ex] $\pm$ 0.033	0.731 [0.4ex] $\pm$ 0.060	0.914 [0.4ex] $\pm$ 0.037
TH	0.679 ∗∗∗ [0.4ex] $\pm$ 0.041	0.941 ∗∗∗ [0.4ex] $\pm$ 0.018	0.687 ∗∗∗ [0.4ex] $\pm$ 0.038	0.934 ∗∗ [0.4ex] $\pm$ 0.020	0.706 [0.4ex] $\pm$ 0.031	0.938 [0.4ex] $\pm$ 0.020
AB/HN/TH	0.711 ∗∗∗ [0.4ex] $\pm$ 0.057	0.927 ∗∗∗ [0.4ex] $\pm$ 0.026	0.712 ∗∗∗ [0.4ex] $\pm$ 0.055	0.919 [0.4ex] $\pm$ 0.029	0.738 [0.4ex] $\pm$ 0.055	0.919 [0.4ex] $\pm$ 0.030
Brain	0.778 ∗∗ [0.4ex] $\pm$ 0.121	0.858 [0.4ex] $\pm$ 0.021	0.813 [0.4ex] $\pm$ 0.100	0.859 [0.4ex] $\pm$ 0.019	0.816 [0.4ex] $\pm$ 0.089	0.857 [0.4ex] $\pm$ 0.022

Appendix

TABLE XVI: Quantitative results for Task 1 using IMPACT-based training, comparing models trained with MAE, VGG-based perceptual, and SAM-based losses. Dice evaluates downstream segmentation performance, while SSIM measures image similarity between synthesized CT and reference CT. Values are reported as mean

\pm

standard deviation across matched patients. Statistical comparisons against SAM follow the patient-level protocol described in Section III-F .

	MAE_IMPACT		VGG_IMPACT		SAM_IMPACT
Region	Dice	SSIM	Dice	SSIM	Dice	SSIM
AB	0.642 ∗∗∗ [0.4ex] $\pm$ 0.086	0.928 ∗∗∗ [0.4ex] $\pm$ 0.023	0.640 ∗∗∗ [0.4ex] $\pm$ 0.083	0.925 [0.4ex] $\pm$ 0.024	0.670 [0.4ex] $\pm$ 0.086	0.925 [0.4ex] $\pm$ 0.024
HN	0.720 ∗∗∗ [0.4ex] $\pm$ 0.078	0.950 ∗∗∗ [0.4ex] $\pm$ 0.019	0.697 ∗∗∗ [0.4ex] $\pm$ 0.083	0.943 [0.4ex] $\pm$ 0.022	0.708 [0.4ex] $\pm$ 0.076	0.943 [0.4ex] $\pm$ 0.022
TH	0.718 [0.4ex] $\pm$ 0.076	0.930 ∗∗∗ [0.4ex] $\pm$ 0.018	0.709 ∗∗∗ [0.4ex] $\pm$ 0.076	0.928 [0.4ex] $\pm$ 0.019	0.720 [0.4ex] $\pm$ 0.077	0.927 [0.4ex] $\pm$ 0.020
AB/HN/TH	0.695 [0.4ex] $\pm$ 0.088	0.937 ∗∗∗ [0.4ex] $\pm$ 0.022	0.683 ∗∗∗ [0.4ex] $\pm$ 0.086	0.932 [0.4ex] $\pm$ 0.023	0.700 [0.4ex] $\pm$ 0.082	0.932 [0.4ex] $\pm$ 0.023
$AB_{\mathrm{sim}}$	0.709 ∗∗∗ [0.4ex] $\pm$ 0.085	0.927 ∗∗∗ [0.4ex] $\pm$ 0.016	0.723 ∗∗∗ [0.4ex] $\pm$ 0.077	0.921 ∗∗∗ [0.4ex] $\pm$ 0.016	0.764 [0.4ex] $\pm$ 0.079	0.932 [0.4ex] $\pm$ 0.015

Appendix

TABLE XVII: Quantitative results for Task 2 using IMPACT-based training, comparing models trained with MAE, VGG-based perceptual, and SAM-based losses. Dice evaluates downstream segmentation performance, while SSIM measures image similarity between synthesized CT and reference CT. Values are reported as mean

\pm

standard deviation. Statistical comparisons against SAM follow the patient-level protocol described in Section III-F .

AB_{\mathrm{sim}}

HN_{\mathrm{sim}}

, and

TH_{\mathrm{sim}}

correspond to the registration-free simulated CBCT evaluations.

Region

Mean CV

MAE

SAM

MAE

SAM

MAE

SAM

LPIPS

MAE

SAM

LPIPS

MAE

SAM

LPIPS

MAE

SAM

LPIPS

71.30 [0.4ex]

\pm

14.44

32.54 [0.4ex]

\pm

5.94

12.67 [0.4ex]

\pm

3.42

74.00 [0.4ex]

\pm

13.84

25.64 [0.4ex]

\pm

4.38

10.66 [0.4ex]

\pm

2.79

67.73 [0.4ex]

\pm

14.25

32.46 [0.4ex]

\pm

6.07

12.49 [0.4ex]

\pm

3.35

69.35 [0.4ex]

\pm

14.04

26.70 [0.4ex]

\pm

4.50

10.66 [0.4ex]

\pm

2.81

83.51 [0.4ex]

\pm

24.76

14.54 [0.4ex]

\pm

2.37

3.46 [0.4ex]

\pm

0.63

99.88 [0.4ex]

\pm

33.98

11.63 [0.4ex]

\pm

1.76

3.11 [0.4ex]

\pm

0.54

78.98 [0.4ex]

\pm

23.41

14.76 [0.4ex]

\pm

2.37

3.41 [0.4ex]

\pm

0.64

93.66 [0.4ex]

\pm

33.16

11.96 [0.4ex]

\pm

1.82

3.05 [0.4ex]

\pm

0.55

58.57 [0.4ex]

\pm

10.54

25.25 [0.4ex]

\pm

5.66

8.66 [0.4ex]

\pm

2.88

62.60 [0.4ex]

\pm

10.62

19.35 [0.4ex]

\pm

4.77

7.23 [0.4ex]

\pm

2.33

55.30 [0.4ex]

\pm

10.14

25.24 [0.4ex]

\pm

5.71

8.52 [0.4ex]

\pm

2.86

58.22 [0.4ex]

\pm

10.12

20.50 [0.4ex]

\pm

5.19

7.18 [0.4ex]

\pm

2.41

AB/HN/TH

70.75 [0.4ex]

\pm

20.17

24.27 [0.4ex]

\pm

8.83

8.34 [0.4ex]

\pm

4.56

78.26 [0.4ex]

\pm

26.66

18.99 [0.4ex]

\pm

6.88

7.06 [0.4ex]

\pm

3.72

66.98 [0.4ex]

\pm

19.27

24.31 [0.4ex]

\pm

8.77

8.22 [0.4ex]

\pm

4.49

73.21 [0.4ex]

\pm

25.84

19.85 [0.4ex]

\pm

7.26

7.03 [0.4ex]

\pm

3.77

Appendix

TABLE XVIII: Quantitative results for Task 1 using models trained with ELX and evaluated with ELX, comparing MAE- and SAM-based training losses. “CV” denotes the ensemble prediction obtained by averaging the outputs of the five cross-validation models (CV 0 –CV 4 ), while “Mean CV” corresponds to the average performance of the individual models. Performance is reported using MAE,

d_{\mathrm{SAM}}

, and LPIPS (mean

\pm

standard deviation) computed on matched patients.

d_{\mathrm{SAM}}

and LPIPS values are scaled by a factor of 100 for readability.

Region

Mean CV

MAE

SAM

MAE

SAM

MAE

SAM

LPIPS

MAE

SAM

LPIPS

MAE

SAM

LPIPS

MAE

SAM

LPIPS

62.55 [0.4ex]

\pm

10.57

21.83 [0.4ex]

\pm

5.95

7.82 [0.4ex]

\pm

2.95

66.39 [0.4ex]

\pm

11.88

17.57 [0.4ex]

\pm

4.06

6.69 [0.4ex]

\pm

2.35

59.08 [0.4ex]

\pm

10.64

22.19 [0.4ex]

\pm

6.32

7.73 [0.4ex]

\pm

2.99

62.30 [0.4ex]

\pm

12.00

18.10 [0.4ex]

\pm

4.75

6.60 [0.4ex]

\pm

2.45

72.35 [0.4ex]

\pm

13.72

11.48 [0.4ex]

\pm

2.20

2.48 [0.4ex]

\pm

0.70

78.71 [0.4ex]

\pm

14.05

9.40 [0.4ex]

\pm

1.53

2.22 [0.4ex]

\pm

0.61

68.55 [0.4ex]

\pm

13.92

11.75 [0.4ex]

\pm

2.39

2.42 [0.4ex]

\pm

0.71

74.24 [0.4ex]

\pm

14.18

9.50 [0.4ex]

\pm

1.64

2.09 [0.4ex]

\pm

0.61

59.16 [0.4ex]

\pm

12.89

20.09 [0.4ex]

\pm

4.91

6.53 [0.4ex]

\pm

2.31

62.46 [0.4ex]

\pm

12.90

15.63 [0.4ex]

\pm

3.81

5.51 [0.4ex]

\pm

1.85

55.43 [0.4ex]

\pm

12.51

20.51 [0.4ex]

\pm

4.86

6.44 [0.4ex]

\pm

2.28

58.05 [0.4ex]

\pm

12.66

16.12 [0.4ex]

\pm

3.90

5.42 [0.4ex]

\pm

1.88

AB/HN/TH

64.95 [0.4ex]

\pm

13.77

17.54 [0.4ex]

\pm

6.46

5.48 [0.4ex]

\pm

3.15

69.52 [0.4ex]

\pm

14.82

13.99 [0.4ex]

\pm

4.82

4.69 [0.4ex]

\pm

2.58

61.28 [0.4ex]

\pm

13.72

17.88 [0.4ex]

\pm

6.62

5.40 [0.4ex]

\pm

3.15

65.18 [0.4ex]

\pm

14.79

14.36 [0.4ex]

\pm

5.18

4.59 [0.4ex]

\pm

2.63

Appendix

TABLE XIX: Quantitative results for Task 2 using models trained with ELX and evaluated with ELX, comparing MAE- and SAM-based training losses. “CV” denotes the ensemble prediction obtained by averaging the outputs of the five cross-validation models (CV 0 –CV 4 ), while “Mean CV” corresponds to the average performance of the individual models. Performance is reported using MAE,

d_{\mathrm{SAM}}

, and LPIPS (mean

\pm

standard deviation).

d_{\mathrm{SAM}}

and LPIPS values are scaled by a factor of 100 for readability.

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