A segmented backprojection tensor degradation feature encoding model for motion artifacts correction in dental cone beam computed tomography

doi:10.12122/j.issn.1673-4254.2025.02.23

Abstract

Abstract:

Objective We propose a segmented backprojection tensor degradation feature encoding (SBP-MAC) model for motion artifact correction in dental cone beam computed tomography (CBCT) to improve the quality of the reconstructed images. Methods The proposed motion artifact correction model consists of a generator and a degradation encoder. The segmented limited-angle reconstructed sub-images are stacked into the tensors and used as the model input. A degradation encoder is used to extract spatially varying motion information in the tensor, and the generator's skip connection features are adaptively modulated to guide the model for correcting artifacts caused by different motion waveforms. The artifact consistency loss function was designed to simplify the learning task of the generator. Results The proposed model could effectively remove motion artifacts and improve the quality of the reconstructed images. For simulated data, the proposed model increased the peak signal-to-noise ratio by 8.28%, increased the structural similarity index measurement by 2.29%, and decreased the root mean square error by 23.84%. For real clinical data, the proposed model achieved the highest expert score of 4.4221 (against a 5-point scale), which was significantly higher than those of all the other comparison methods. Conclusion The SBP-MAC model can effectively extract spatially varying motion information in the tensors and achieve adaptive artifact correction from the tensor domain to the image domain to improve the quality of reconstructed dental CBCT images.

Key words: motion artifact correction, dental cone beam computed tomography, segmented backprojection tensor

Zhixiong ZENG, Yongbo WANG, Zongyue LIN, Zhaoying BIAN, Jianhua MA. A segmented backprojection tensor degradation feature encoding model for motion artifacts correction in dental cone beam computed tomography[J]. Journal of Southern Medical University, 2025, 45(2): 422-436.

Figures/Tables 18

Fig.1 Schematic diagram of the segmented backprojection tensor. A: Origin of the segmented backprojection tensor. B: Training idea of DNN in the traditional image domain. C: Training idea of the proposed model.

Fig.2 Flow chart of the SBP-MAC model. A: Preparation of the input data of the model. B: Composition of the proposed model. C: Output results of the model.

Fig.3 Detailed archetecture of the key modules. A: Degradation Encoder Module (E). B: Spatial Adaptive Modulation Module (SAM). C: Generator Module (G). The number of stacks for each smaller module is indicated at the bottom right corner.

Fig.4 Schematic diagram of CBCT scanning and motion waveforms. A: Schematic diagram of CBCT scanning. B: Translational motion waveform used in the simulation. C: Rotational motion waveform used in the simulation.

Fig.5 Transverse-section results of different motion artifact correction methods on simulation data.

Fig.6 Coronal-section results of different motion artifact correction methods on simulation data.

Fig.7 Sagittal-section results of different motion artifact correction methods on simulation data.

Tab.1 Quantitative indicators of different motion artifact correction algorithms on simulation data (Mean±SD)

Methods	PSNR	RMSE	SSIM
Uncorrected	29.5479±1.5036	8.6211±1.4713	0.9276±0.0191
UNet	30.2005±1.5400	8.0025±1.3922	0.9339±0.0191
REDCNN	30.0276±1.5185	8.1599±1.3991	0.9318±0.0185
HINet	30.2865±1.5343	7.9228±1.3729	0.9346±0.0183
Restormer	30.3057±1.5646	7.9111±1.4128	0.9342±0.0191
SBP-MAC	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144

Fig.10 Coronal-section results of different motion artifact correction methods on real data.

Fig.8 Transverse-section results of different motion artifact correction methods on real data.

Tab.2 Image quality expert score statistics on real clinical data

Methods	Scores (Mean±SD)	P
UNet	3.7263±0.5915	0.0018
REDCNN	3.8000±0.5416	0.0042
HINet	3.7895±0.4026	0.0012
Restormer	3.9211±0.3084	0.0078
SBP-MAC	4.2421±0.3548	-

Fig.10 　Sagittal-section results of different motion artifact correction methods on real data.

Fig.11 Displacement of motion artifact image generation results.

Tab.3 Ablation experiments of different hyperparameters λ of the loss function on simulation data (Mean±SD)

$L o s s = λ L M A C y', y$ $+ (1 - λ) L C o n s i s t e n c y x', x$	PSNR	RMSE	SSIM
$λ = 0$	29.4475±1.5051	8.7216±1.4902	0.9272±0.0190
$λ = 0.2$	30.9235±2.0620	7.4532±1.7300	0.9410±0.0210
$λ = 0.4$	31.6159±1.7890	6.8346±1.3598	0.9497±0.0156
$λ = 0.5$	31.6953±1.8010	6.7756±1.3774	0.9519±0.0150
$λ = 0.6$	31.7653±1.9522	6.7456±1.4842	0.9513±0.0169
$λ = 0.8$	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
$λ = 1$	31.5523±2.0146	6.9245±1.5739	0.9504±0.0182
Uncorrected	29.5479±1.5036	8.6211±1.4713	0.9276±0.0191

Tab.3 Ablation experiments of different hyperparameters λ of the loss function on simulation data (Mean±SD)

$L o s s = λ L M A C y', y$ $+ (1 - λ) L C o n s i s t e n c y x', x$	PSNR	RMSE	SSIM
$λ = 0$	29.4475±1.5051	8.7216±1.4902	0.9272±0.0190
$λ = 0.2$	30.9235±2.0620	7.4532±1.7300	0.9410±0.0210
$λ = 0.4$	31.6159±1.7890	6.8346±1.3598	0.9497±0.0156
$λ = 0.5$	31.6953±1.8010	6.7756±1.3774	0.9519±0.0150
$λ = 0.6$	31.7653±1.9522	6.7456±1.4842	0.9513±0.0169
$λ = 0.8$	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
$λ = 1$	31.5523±2.0146	6.9245±1.5739	0.9504±0.0182
Uncorrected	29.5479±1.5036	8.6211±1.4713	0.9276±0.0191

Tab.4 Ablation experiments of different key factors on simulation data (Mean±SD)

Tensor as input	Degradation encoder guidance	Artifact consistency loss	PSNR	RMSE	SSIM
--	--	--	30.3743±1.6067	7.8556±1.4400	0.9368±0.0180
√	--	--	30.7192±1.8001	7.5822±1.5518	0.9414±0.0181
√	√	--	31.5523±2.0146	6.9245±1.5739	0.9504±0.0182
√	√	√	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
Uncorrected			29.5479±1.5036	8.6211±1.4713	0.9276±0.0191

Tab.5 Exploratory experiment on the impact of α1 and α2 on model performance (Mean±SD)

Fixed hyperparameter	Variable hyperparameter	PSNR	RMSE	SSIM
$α 1 = 0.1$	$α 2 = 0.001$	30.5183±1.9243	7.7814±1.6805	0.9386±0.0212
	$α 2 = 0.01$	30.6998±1.9764	7.6334±1.7315	0.9400±0.0219
	$α 2 = 0.1$	30.7374±2.0913	7.6229±1.8165	0.9409±0.0226
	$α 2 = 1$	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
	$α 2 = 10$	31.2309±1.7166	7.1332±1.3707	0.9445±0.0169
$α 2 = 1$	$α 1 = 0.001$	30.9549±1.9805	7.4097±1.6358	0.9389±0.0219
	$α 1 = 0.01$	31.8414±1.5808	6.6301±1.1826	0.9508±0.0122
	$α 1 = 0.1$	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
	$α 1 = 1$	30.4684±1.6176	7.7740±1.4537	0.9349±0.0190
	$α 1 = 10$	29.5610±1.4616	8.6027±1.4620	0.9293±0.0210
Uncorrected		29.5479±1.5036	8.6211±1.4713	0.9276±0.0191

Tab.5 Exploratory experiment on the impact of α1 and α2 on model performance (Mean±SD)

Fixed hyperparameter	Variable hyperparameter	PSNR	RMSE	SSIM
$α 1 = 0.1$	$α 2 = 0.001$	30.5183±1.9243	7.7814±1.6805	0.9386±0.0212
	$α 2 = 0.01$	30.6998±1.9764	7.6334±1.7315	0.9400±0.0219
	$α 2 = 0.1$	30.7374±2.0913	7.6229±1.8165	0.9409±0.0226
	$α 2 = 1$	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
	$α 2 = 10$	31.2309±1.7166	7.1332±1.3707	0.9445±0.0169
$α 2 = 1$	$α 1 = 0.001$	30.9549±1.9805	7.4097±1.6358	0.9389±0.0219
	$α 1 = 0.01$	31.8414±1.5808	6.6301±1.1826	0.9508±0.0122
	$α 1 = 0.1$	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
	$α 1 = 1$	30.4684±1.6176	7.7740±1.4537	0.9349±0.0190
	$α 1 = 10$	29.5610±1.4616	8.6027±1.4620	0.9293±0.0210
Uncorrected		29.5479±1.5036	8.6211±1.4713	0.9276±0.0191

Tab.6 Exploratory experiment on the impact of Nseg on model performance (Mean±SD)

$N s e g$	PSNR	RMSE	SSIM
1	30.2443±1.8714	8.0235±1.7334	0.9318±0.0192
3	30.9100±2.1304	7.4803±1.8122	0.9425±0.0217
6	31.8551±1.7499	6.6448±1.3210	0.9536±0.0130
9	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
15	31.8019±1.8482	6.7010±1.4063	0.9516±0.0148
Uncorrected	29.5479±1.5036	8.6211±1.4713	0.9276±0.0191

Tab.6 Exploratory experiment on the impact of Nseg on model performance (Mean±SD)

$N s e g$	PSNR	RMSE	SSIM
1	30.2443±1.8714	8.0235±1.7334	0.9318±0.0192
3	30.9100±2.1304	7.4803±1.8122	0.9425±0.0217
6	31.8551±1.7499	6.6448±1.3210	0.9536±0.0130
9	31.9936±1.9277	6.5657±1.4119	0.9547±0.0144
15	31.8019±1.8482	6.7010±1.4063	0.9516±0.0148
Uncorrected	29.5479±1.5036	8.6211±1.4713	0.9276±0.0191

Fig.12 Artifact correction results under different values of the Nseg .

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