Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
10.1109/ACCESS.2023.0322000

This work was supported in part by Innosuisse under Grant 56768.1 IP-LS.

Corresponding author: Frank-Peter Schilling (e-mail: scik@zhaw.ch).

# Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with
Deep Learning - A Review

MOHAMMADREZA AMIRIAN1  Daniel Barco1  Ivo Herzig2  and Frank-Peter Schilling1
Centre for AI (CAI), Zurich University of Applied Sciences (ZHAW), Winterthur,
Switzerland (e-mail: mohammadreza.amirian<EMAIL_ADDRESS>Institute of Applied Mathematics and Physics (IAMP), Zurich University of
Applied Sciences (ZHAW), Winterthur, Switzerland (e-mail<EMAIL_ADDRESS>
###### Abstract

Deep learning based approaches have been used to improve image quality in
cone-beam computed tomography (CBCT), a medical imaging technique often used
in applications such as image-guided radiation therapy, implant dentistry or
orthopaedics. In particular, while deep learning methods have been applied to
reduce various types of CBCT image artifacts arising from motion, metal
objects, or low-dose acquisition, a comprehensive review summarizing the
successes and shortcomings of these approaches, with a primary focus on the
type of artifacts rather than the architecture of neural networks, is lacking
in the literature. In this review, the data generation and simulation
pipelines, and artifact reduction techniques are specifically investigated for
each type of artifact. We provide an overview of deep learning techniques that
have successfully been shown to reduce artifacts in 3D, as well as in time-
resolved (4D) CBCT through the use of projection- and/or volume-domain
optimizations, or by introducing neural networks directly within the CBCT
reconstruction algorithms. Research gaps are identified to suggest avenues for
future exploration. One of the key findings of this work is an observed trend
towards the use of generative models including GANs and score-based or
diffusion models, accompanied with the need for more diverse and open training
datasets and simulations.

###### Index Terms:

Cone-beam Computed Tomography (CBCT), Deep Learning, Artifacts.

=-21pt

Figure 1: Visual Abstract: An illustration of the CBCT acquisition process in
IGRT for lung CBCT and the application of deep learning for artifact
correction. The diagram depicts the acquisition of 2D projections (initial
corrections such as scatter corrections have already been applied), including
(optionally) time- and motion-related information (e.g. breathing amplitude
signal), standard CBCT reconstruction (typically 2D$\rightarrow$3D), and DL-
based components for image enhancement. Incorporating acquired temporal and
motion information provides the opportunity to apply a projection binning
which can be used to reconstruct 4D CBCT images (3D images at various states
of motion). During the course of CBCT reconstruction, several types of
artifacts (e.g. arising from cone-beam geometry, low dose, sparse view or
limited angle scans, scatter, metal or beam hardening) can be mitigated
through DL-based optimization in the projection and/or volume domain, or by
improving (parts of) the reconstruction algorithm itself using neural
networks. The illustration of a commerical radiotherapy system is adapted from
[1].

##  I Introduction

Cone-beam computed tomography (CBCT) is an imaging technique to acquire
volumetric scans in medical domains such as implant dentistry, orthopaedics,
or image-guided radiation therapy (IGRT). In particular, in the case of IGRT,
on-board imaging mounted directly on radiotherapy machines is used to assess a
patient’s current anatomy before radiation treatment sessions. Changes in
anatomy during the treatment period and since the acquisition of the planning
CT (pCT) can lead to inefficiencies in the treatment process. Recent research
has demonstrated that utilizing 3D or 4D (volumetric data with additional time
dimension to track motion) CBCT scans in IGRT [2] improves patient positioning
and dose calculation for radiotherapy sessions.

The quality of CBCT scans suffers from similar types of artifacts as for
spiral/helical CT scans, including those arising from beam hardening and
scatter effects, metal implants, and patient motion. In addition, new
artifacts arise due to the cone-beam geometry. Further, minimizing the
radiation dose in radiotherapy is important for the safety of the patients.
However, reducing the imaging dose per scan, acquiring fewer X-ray
projections, or acquiring projection data from a limited angle can result in
streak artifacts.

This paper provides an overview of the current body of research on artifact
reduction in 3D and 4D CBCT with applications including, but not limited to,
IGRT, aiming to improve scan quality while also minimizing the imaging
radiation dose. The significant variation in the methods and techniques used
to mitigate different types of artifacts suggests to organize the literature
based on the type of artifact. For instance, sparse-view artifacts can be
addressed in the projection domain by interpolating new projections, but
refining the original projections is not beneficial; however, motion artifact
mitigation is possible through projection refinement. Further, the survey aims
to present a clear picture of all necessary steps in the artifact mitigation
process for all relevant types of artifacts individually.

In particular, we review the current state-of-the-art research which uses deep
learning (DL) [3] to reduce various artifacts in CBCT scans, and we categorize
the research based on the types of artifacts they address. While Ref. [4]
focuses broadly on the use of DL methods in IGRT, the closest literature
reviews to our work are presented in references [5, 6, 7]. The first survey
[5] is focused on synthetic CT generation from various types of input scans,
including CBCT, with the aim to enhance the scan quality. Its content
partially overlaps with what we present in Section III. However, it does not
cover all the other artifacts which can degrade CBCT image quality as
discussed after Section III. Ref. [6] discusses supervised, self-supervised,
and unsupervised techniques for artifact reduction in CT scans, and it covers
unrolling the reconstruction, as well as optimization methods in both the
projection (raw 2D X-ray images) and volume (reconstructed 3D images) domains.
However, it is essential to note that Ref. [6] primarily focuses on CT scans,
which differs from the main focus of this work, namely CBCT scans. The third
survey [7] provides an in-depth literature analysis, considering criteria such
as anatomy, loss functions, model architectures, and training methods for
supervised learning specifically applied to CBCT scans. In our work, instead
of dividing the literature based on the deep learning methods, we group the
research based on the type of artifacts, discussing results employing
projection- and/or volume-domain optimization, dividing the methods based on
the type of supervision, and also including research addressing time-resolved
4D CBCT reconstruction.

Artifacts in CBCT images can principally be reduced by optimizations in the
projection, volume, or dual-domain (both projections and volumes), as well as
by DL-enabled reconstruction. This survey presents an overview of deep
learning techniques able to reduce artifacts in 3D as well as time-resolved 4D
CBCT using optimizations in the above domains, and through novel CBCT
reconstruction methods. Furthermore, it addresses the challenges and
limitations associated with these approaches and provides recommendations for
future research directions.

This survey organizes the literature according to the type of artifacts which
is addressed, and presents and contrasts the methodologies used within each
specific artifact group (see Figure 2). The remainder of this paper is
organized as follows: Section II briefly summarizes the basic aspects of CBCT
acquisition and the assessment of scan quality. Thereafter, the literature is
discussed based on different types of artifacts (as outlined in [8, 9]) as
follows: Section III presents methods attempting to improve CBCT image quality
by reducing artifacts generated because of the cone-beam geometry and by
bringing the CBCT quality closer to the one of CT scans. The subsequent
sections focus on various methods to address artifacts resulting from reduced
acquisition dose. Firstly, Section IV discusses techniques that lower the dose
per X-ray projection to achieve dose reduction. This is followed by Section V,
which explains methods for artifact reduction when acquiring fewer projections
by uniformly dropping some of them (sparse-view reconstruction). Section VI
explores artifact reduction methods specifically for CBCT scans acquired from
a limited angular range. The paper then proceeds to discuss methods targeting
scatter and beam hardening artifacts in Section VII. Section VIII is dedicated
to research on reducing metal artifacts. Section IX focuses on motion
compensation techniques for 3D and 4D CBCT. Further, the main trends in the
recent literature on using deep learning-based architectures for CBCT artifact
mitigation are presented in Section X, complemented with a discussion
concerning the connections amongst the methods used for various types of
artifacts and recommendations for future work. Finally, the paper concludes
with Section XI.

Figure 2: Visualisation of the content of this survey and the literature
covered.

##  II Preliminaries

This section briefly reviews the basics of CBCT reconstruction and evaluation
methods employed in artifact reduction and scan quality assessment.

###  II-A Cone-Beam Geometry Reconstruction and Deep Learning

CBCT scans are acquired by means of an imaging system consisting of an X-ray
source and a flat-panel (2D) detector mounted on a gantry system which rotates
around the body region of interest. Several hundred 2D X-ray images are
acquired at various angles. These projections can be acquired from a limited
angular range (so-called short scan) or a full $360^{\circ}$ trajectory (full
scan). Following the acquisition, a volumetric 3D image is reconstructed from
the 2D projection images. Several methods exist to solve this ill-posed
inverse problem. The most popular one is based on an analytic method developed
by Feldkamp, Davis, and Kress (FDK [10]) which provides a fast and reliable
approximation of the inverse Radon transform. Alternatively, iterative
algebraic reconstruction techniques (ART [11]) have become popular as well.
Moreover, by tracking the patients’ motion, e.g. by capturing an external or
internal breathing signal, and dividing the projections based on the motion
state, it is possible to reconstruct 4D (motion-resolved) volumetric images.
4D scans include both the 3D volumetric information as well as their temporal
dynamics.

In a nutshell, deep learning based approaches can be deployed at various
stages of the CBCT reconstruction process. Firstly, deep neural networks can
be trained to correct the acquired 2D projections (projection domain
correction); secondly, they can be used to correct the reconstructed CBCT
volumetric images (volume domain correction); and thirdly, the two approaches
can be combined into a dual-domain correction. Another approach is to augment
or replace (parts of) the 2D-3D CBCT reconstruction itself with deep learning
based components. The components of the FDK algorithm were mapped into a deep
neural network by means of a novel deep learning enabled cone beam back-
projection layer [12, 13]. The backward pass of the layer is computed as a
forward projection operation. This approach thus permits joint optimization of
correction steps in both volume and projection domain. An open source
implementation of differentiable reconstruction functions is available [14].
The networks are often trained in a supervised fashion by comparing
reconstructed CBCT images with an artifact-free ground truth. Unsupervised
[15, 16] and self-supervised [17, 18] learning approaches have been employed
as well.

While datasets of 3D or 4D CBCT scans obtained from phantoms, animals or human
subjects are available for training, they generally lack ground truth
information required for deep learning based artifact mitigation employing
supervised learning. To overcome this, artificial or simulated CBCT data is
often used, obtained e.g. by means of forward projecting existing CT scans in
a CBCT setup and manual incorporation of artifacts. For example, motion
artifacts can be included by sampling CBCT projections at scan angles and time
steps matching interpolated phases of a given 4D CT scan.

The general acquisition and reconstruction process of CBCT scans, including
deep learning based corrections, is summarized in the visual abstract in
Figure 1.

###  II-B Evaluation Metrics

Several metrics have been utilized in the literature to evaluate the quality
of CBCT scans enhanced by deep learning-based techniques. The main qualitative
evaluation metrics, computed between a reconstructed volume (with artifacts)
and the ground truth reference, can be divided into two main groups as
follows, according to [7]:

  * •

Image Similarity Metrics: These metrics compute the similarity between scans
and include (mean) absolute error (ME and MAE), (root) mean squared error (MSE
and RMSE), (peak) signal-to-noise ratio (SNR and PSNR), structural similarity
(SSIM) [19], and Dice coefficient [20].

  * •

Dosimetric Similarity Metrics: These metrics measure the consistency in
dosimetry using a pair of scans, such as dose difference pass rate (DPR);
dose–volume histogram (DVH), and gamma pass rate (GPR).

In addition to the metrics mentioned above, metal artifact index (MAI [21]),
and streak index (SI [22]) have been used in the literature to measure the
level of specific artifacts in CT and CBCT scans. For motion, visual
information fidelity (VIF) [23] or autofocus (sharpness) metrics have been
employed, among others.

###  II-C Clinical Evaluation

The numerical evaluation metrics mentioned above compute the similarity of the
improved CBCT compared with a reference, or report the level of the presence
of artifacts, scan sharpness, or other quality criteria. Ideally, these
metrics should reflect the scan quality; hence, they should correspond to the
preference of the experts in using the scans in clinical routine. However, it
is essential to note likely inconsistencies between simulated (where ground
truth references exist) and real-world clinical data, so clinical evaluations
are necessary to ensure the applicability of the presented methods for
practical applications. A clinical evaluation can be conducted by completing
surveys with experts such as medical doctors or radiation physicists to
directly assess the level of artifacts and the performance of the artifact
reduction techniques, and the applicability of the improved images in various
clinical tasks such as dose calculation, soft-tissue segmentation, and patient
positioning [24].

##  III Cone-Beam Image Quality

Cone-beam geometry and the size of the flat-panel detector result in the
coverage of larger body areas but at lower resolution and degradation in scan
quality compared to fan-beam CT scan acquisition. Consequently, significant
attention and extensive research has been directed at improving the quality of
CBCT scans, often referred to as removing cone-beam or geometry artifacts in
the literature. One of the initial approaches to enhance CBCT quality involves
employing supervised learning and training a 39-layer deep convolutional
neural network (CNN) to map input CBCT scans to the corresponding planning CT
as ground truth (reference) volumes [25]. This mapping of CBCT images to match
correpsonding CT images is often called synthetic CT (sCT) from CBCT.

#### Applications based on CNNs

Researchers have explored several CNN-based architectures with various
supervised training objectives to enhance CBCT quality. For instance,
denoising has been targeted through solving the multi-agent consensus
equilibrium (MACE) problem and multi-slice information fusion techniques [26].
CNN models have demonstrated the ability to reduce ring artifacts from flat-
panel CBCT scans using pre-corrected and artifact-free scans as ground truth
[27]. Geometric artifacts caused by misalignment of the CBCT system were
reduced using a modified fully convolutional neural network (M-FCNN), without
using any pooling layers [28]. A further approach used a 3D block-based
residual encoder-decoder convolutional neural network (RED-CNN) architecture
coupled with a bilateral 3D filter and a 2D-based Landweber iteration to
successfully remove Poisson noise while preserving the image structure at
tissue edges [29]. Training 3D models using a multi-task learning objective
improved the quality of CBCTs by producing high-quality synthetic CT (sCT)
scans from noisy and artifact-ridden scans for segmenting organs-at-risk
(OARs) [30]. Lately, using InceptionV3 [31] as a backbone has proven
beneficial in reducing the artifacts observed in CBCT short scans due to the
misalignment of the detection plane around the z-axis [32].

#### GANs and conditional GANs

Researchers have used self-supervised and unsupervised techniques to eliminate
the need for paired CBCT and CT scans in supervised learning and to consider
anatomical changes between the acquisition of planning CT (pCT) and CBCT.
These techniques mainly involve training auto-encoders, (conditional)
generative adversarial networks (GANs [33]), and cycle-consistent generative
adversarial networks (Cycle-GANs [34]). Combining auto-encoders and GANs as a
complementary approach to reweighting in analytical and iterative
reconstruction methods has improved the quality of CBCT scans [35]. Training
conditional GANs has shown promising results in enhancing the quality of CBCT
through style transfer, effectively removing artifacts and discrepancies
between CBCT and pCT for average tumor localization [36] and adaptive therapy
[37]. Moreover, a more advanced GAN variant called temporal coherent
generative adversarial network (TecoGAN) also improves the quality of
simulated 4D CBCT scans by considering the time dependencies and motion for
quality enhancement [38, 39].

#### Cycle-GANs

Using Cycle-GANs for unpaired translation from CBCT to pCT has received
significant attention among researchers. Notably, Cycle-GANs have successfully
generated high-quality synthetic CT scans from CBCT for various organs,
including prostate [40], lung [41], and abdominal scans [42]. A novel
architecture inspired by contrastive unpaired translation (CUT [43]), trained
in an unsupervised manner, improves the quality of CBCT scans by addressing
fringe artifacts and noise degradation for dose calculation in adaptive
radiotherapy [15]. The combination of binary cross-entropy, gradient
difference, and identity losses with Cycle-GANs has further improved the
quality of head and neck CBCT scans [44]. Introducing the residual block
concept in the implementation of Res-Cycle-GAN has demonstrated advancements
in the quality of sCT scans [45]. Moreover, researchers have explored the
combination of a Cycle-GAN with classical image processing techniques [46] and
U-Net [47] architectures [16] in two-step approaches. These approaches aim to
initially reduce artifacts and subsequently generate sCT scans to improve the
quality. Ultimately, researchers demonstrated that trained Cycle-GANs enhance
the quality of CBCT scans and achieve high accuracy in volumetric-modulated
arc photon therapy (VMAT) [48].

#### Alternative methods

In addition to adopting mainstream trends and computer vision architectures
for artifact reduction in CBCT scans, researchers have explored creative
methods specifically tailored to CBCT reconstruction using deep learning and
neural networks. For instance, U-Nets have been optimized for spectral
blending of independently reconstructed sagittal and coronal views to enhance
the CBCT quality [49]. Neural networks have also been integrated into the core
of the reconstruction algorithms in the Feldkamp, Davis and Kress (FDK)
technique to introduce the NN-FDK technique for CBCT quality improvement [50].
Another novel architecture, known as the iterative reconstruction network
(AirNet), incorporates several variants in selecting projections based on
random-phase (RP), prior-guided (PG), and all-phases (AP) for reconstruction
[51]. Geometry-guided deep learning (GDL [52]), and its multi-beamlet-based
approach (GMDL [53]) are additional examples of leveraging deep learning to
enhance the reconstruction geometry effectively. Finally, CNNs have been
employed to predict the quality of the scans and accordingly dynamically adapt
the C-arm source trajectory in the imaging acquisition process to avoid
generating artifacts in the final scans [54].

##  IV Low dose

The reduction of the acquisition dose in CBCT scans, which leads to the
increased presence of artifacts, has been addressed through various approaches
such as adjusting the radiation dose per X-ray projection [55], increasing the
acquisition speed or collecting fewer projections [56]. Early research focused
on low-dose artifact reduction primarily by removing artifacts in the volume
domain using deep CNNs with U-Net architectures. The studies demonstrated the
potential of decreasing the overall radiation dose through both dose reduction
methods mentioned above [55, 56]. Moreover, a combination of 2D and 3D
concatenating convolutional encoder-decoder (CCE-3D) with a structural
sensitive loss (SSL) was employed to denoise low-dose CBCT scans and remove
artifacts in both projection and volume domains. This approach showed
promising results in improving the quality of CBCT scans based on several
metrics, such as PSNR and SSIM, and with greater improvements reported in the
projection domain compared with the volume domain [57]. In addition, a CNN-
based iterative reconstruction framework was integrated with a plug-and-play
proximal gradient descent framework to leverage DL-based denoising algorithms
and enhance CBCT reconstruction [56]. Training models inspired by self-
supervised learning approaches for inpainting and denoising Poisson and
Gaussian noise have shown promising results in removing low-dose artifacts
[58]. Similarly, models optimized for removing Gaussian noise and addressing
view aliasing artifacts through 2D iterations with 3D kernels have been
developed [59]. Furthermore, researchers combined a non-subsampled contourlet
transform (NSCT) and a Sobel filter with U-Net architectures, referred to as
NCS-Unet, to improve the quality of low-dose CBCT scans by enhancing both low-
and high-frequency components [60].

##  V Sparse-View

This section summarizes research aiming at reducing artifacts in CBCT
reconstruction occurring from using uniformly downsampled full-scan
($360^{\circ}$) projections, primarily with the goal of dose reduction.
Sparse-view artifact reduction is closely related to mitigation of artifacts
caused by limited angle acquisition and breathing-phase-correlated 4D
reconstruction, which will be reviewed in the upcoming sections VI and IX,
respectively. While the underlying motivations for sparse-view (acquisition
dose reduction), limited angle (geometric constraints), and 4D (time resolved
imaging) acquisition are different, in all cases artifacts are created due to
the lack of projections from various angles. Decreasing the number of
projections and the resulting data insufficiency for the reconstruction
algorithm results in artifacts appearing in the shape of symmetric and uniform
streaks, as depicted in Figure 3.

#### Projection and volume domain optimization

The body of literature on sparse-view artifact reduction using deep learning
has been consistently growing since 2019, when initial research demonstrated
the opportunity to reproduce the original image quality with using as few as
one-seventh of the projections with symmetric CNN’s as post-processing
operation in the volume domain [61]. Similarly, using a multi-scale residual
dense network (MS-RDN) successfully improved the quality of CBCTs
reconstructed from one-third of the projections [62]. In addition to training
in the volume domain, the intensities of under-sampled projections can be
corrected using deformation vector fields (DVFs) to match the original data,
resulting in negligible streak artifacts after reconstruction [63]. Similarly,
symmetric residual CNN’s (SR-CNN) can enhance the sharpness of the edges in
anatomical structures reconstructed from sparse-view projections with total
variation (TV) regularization in half-fan scans [61]. Furthermore, a counter-
based total variational CBCT reconstruction using a U-Net architecture
enhances the smoothed edges in lung CT reconstructed scans from half-fan
projections [64]. In Ref. [65], a Reconstruction-Friendly Interpolation
Network (RFI-Net) is developed, which uses a 3D-2D attention network to learn
inter-projection relations for synthesizing missing projections, and then
introduces a novel Ramp-Filter loss to constrain a frequency consistency
between the synthesized and real projections. The authors of [66] developed a
dual-domain attention-guided network framework (Dual-AGNet) which works in
both projection and reconstruction domains, featuring spatial attention
modules and a joint loss function.

#### Dual-domain optimization

Though interpolating missing data in the projections and removing artifacts in
the volume domain are straightforward approaches to sparse-view artifact
reduction, combining both and backpropagating the error through the
reconstruction algorithm is not trivial. Despite the complexity involved,
researchers attempted to unroll the proximal gradient descent algorithm for
reconstruction and backpropagate the gradient through a U-Net architecture to
reduce streak artifacts in [67]. Since optimization in the volume domain and
projection interpolation are regression problems with different or the same
data channels as input and output, autoencoder-decoder architectures have also
gained popularity for artifact reduction [68]. To avoid complications
regarding backpropagation through the reconstruction (back-projection)
algorithm, DEER is introduced as an efficient end-to-end model for directly
reconstructing CBCT scans from few-view projections [69]. Furthermore,
DeepOrganNet could fine-tune the lung mesh by skipping the reconstruction step
and avoiding sparse-view artifacts appearing on organ mesh [70]. Furthermore,
the recent deep intensity field network (DIF-Net) model uses the latent
representation (feature maps) of the 2D projections coupled with a view-
specific query for extracting information from the projections. This
information is then fed through cross-view fusion and intensity regression
models to reconstruct a volume without artifacts. [71].

#### Information fusion, prior-based and physical modeling

Recent research trends seek to minimize sparse-view artifacts by incorporating
multi-slice [72] and scale [73] information fusion techniques, as well as
combining information from different scan views (coronal, axial, and sagittal)
[74]. As the computational resources have become more powerful, deep learning
for sparse-view artifact reduction has extended from 2D models for single
slice processing to 3D models and processing of 4D CBCT scans [72]. The use of
prior (planing) CT and CBCT volumes to enhance the trained models, such as
regularized iterative optimization reconstruction (PRIOR-Net [75]) and merge-
encoder CNN (MeCNN [73]) have recently become popular for sparse-view artifact
reduction. Researchers have also investigated using perception-aware [76] and
physics-based [75] methods. The learning paradigm has expanded beyond purely
supervised learning to different tasks, such as denoising (DRUNet [77]),
artifact reduction [78], self-supervised by dropping projections [18] and
unsupervised learning through training conditional and generative adversarial
networks (GANs) [79].

##  VI Limited angle

Besides lowering the imaging dose through uniformly downsampled projections,
another approach to reducing the number of acquired projections and scanning
dose is scanning the body from a limited angle. Such scan settings are
especially common when using a full-fan acquisition technique in a short-scan,
where reconstruction is performed using projections from an angular range
covering less than $360$ degrees. Although Parker weights [80] can be utilized
to compensate for the loss of mass in the resulting CBCT scans, artifacts
still appear due to the smaller number of acquired projections when scans are
acquired from limited angles. One of the initial attempts used learnable
Parker weights in the projection domain to address the mass loss in the
angular range from $180^{\circ}+\theta$ to $360^{\circ}$ ($\theta$ being the
fan angle) [12]. A subsequent study optimized a deep artifact correction model
(DAC) using a 3D-ResUnet architecture to create high-quality scans and improve
artifacts in limited-angle circular tomosynthesis (cTS), confirming the
potential for quality enhancement in the volume domain [81]. Further research
demonstrated that combining FDK-based reconstruction with a neural network can
achieve outstanding performance in 3D CBCT reconstruction from projections
acquired from only $145^{\circ}$ [82].

Supervised learning, frequently implemented through training U-Net
architectures, for shading corrections in CBCT volumes with a narrow field of
view (FOV) notably improved the quality of reconstructed CBCT scans, using CT
scans as ground truth [83]. Another approach involves using a prior based on a
fully sampled CT or CBCT and training a 2D3D-RegNet, which demonstrates the
effectiveness of using a patient-specific prior for limited-angle sparseness
artifact reduction [84]. A conventional method for 4D CBCT reconstruction is
dividing the projections based on the breathing phases and then reconstructing
the body volume in those phases. As a result of using only a subset of the
projections for each motion state, sparseness artifacts are prevalent for this
special case of limited angle acquisition. These artifacts have been addressed
in the projection domain by interpolating the projections from different
breathing phases [85]. In the volume domain, transfer learning, layer
freezing, and fine-tuning have been employed to adapt the trained DL models to
individual patients and mitigate sparseness artifacts [86].

|  |
---|---|---
Simulated 4D CBCT scan at three distinct motion phases, without significant
motion artifacts
|  |
Sparse-view artifacts at various sub-sampling rates (from left to right: 1/6,
1/18 and 1/48)
|  |
Limited angle artifacts [12] | Scatter artifacts [87] | Metal artifacts [88]
|  |
Motion artifacts in simulated (left) and real (middle and right) CBCT scans
[24]

Figure 3: Examples of different kinds of artifacts appearing in CBCT scans.
Shown are several artifact-free motion states obtained with a simulated 4D
CBCT scan (1st row), sparse-view artifacts at various sub-sampling rates (2nd
row), limited-angle, scatter and metal artifacts (3rd row), as well as motion
artifacts (4th row).

##  VII Scatter and Beam Hardening

Large cone angles within the CBCT geometry setup have been observed to
contribute to scatter artifacts, which have been addressed in the projection
domain by leveraging Monte Carlo photon transport simulations to compute
ground truth projections for supervised learning [89]. A CNN-based deep
scatter estimation (DSE [89]) architecture, as well as a scatter correction
network (ScatterNet [87]) are the results of research endeavors using
supervised learning for artifact correction in the projection domain. The DSE
model has demonstrated the potential to accurately emulate scatter artifacts
and reduce the computational burden of using Monte-Carlo simulations while
being orders of magnitude faster [90]. ScatterNet is considerably faster than
the classical methods and might allow for on-the-fly shading correction [87].
ScatterNet, in combination with shading correction, also showed satisfactory
results for dose calculation using volumetric modulated arc radiation therapy
(VMAT), but yielded unsatisfactory outcomes for intensity-modulated proton
therapy (IMPT). Despite the abundant research work on scatter artifact
corrections, studies tackling beam hardening are scarce. One such study
involved training a U-Net-based architecture to predict monoenergetic X-ray
projections from polyenergetic X-ray projections using supervised learning on
Monte Carlo simulation-based ground truth in the projection domain [91].

Compared with the classical fast adaptive scatter kernel superposition (fASKS)
scatter reduction technique [92], a U-Net-based architecture outperformed in
scatter artifact reduction for both full-fan and half-fan scans based on
several metrics [93]. Additionally, a U-Net-based model trained on simulated
CBCT projections has shown comparable performance to a validated empirical
scatter correction technique in dose calculation for correcting the scatter
artifacts in head and neck scans, computing the corrected volumes in less than
5 seconds [94]. Besides classical approaches of scatter artifact reduction, CT
scans have been used as ground truth volumes for training a modified U-Net
architecture with a multi-objective loss function specifically targeting
scatter artifact reduction in esophagus scans [95].

Apart from supervised learning methods, researchers have also trained Cycle-
GAN models to improve the quality of CBCT scans, remove scatter artifacts, and
generate sCT. In particular, Cycle-GAN has demonstrated superior performance
compared to similar techniques using deep convolutional generative adversarial
networks (DCGAN [96]) and progressive growing GANs (PGGAN [97]) [98].

##  VIII Metal

Metal objects and implants in the patient’s body result in scattered radiation
reaching the detector, leading to streak artifacts. In the early research
addressing metal artifacts, a CNN-based regression model has been trained to
predict the detectability rank of metal implants to recommend out-of-plane
angulation for C-arm source trajectories [99]. Further research in this area
has proposed predicting the X-ray spectral shift after the localization of
metal objects to define the optimal C-arm source-detector orbit [100]. The
metal artifact avoidance (MAA) technique uses low-dose scout projections to
roughly localize metal objects for the identification of a circular or non-
circular orbit of C-arm source-detector to minimize variations in spectral
shift and avoid metal artifacts [101].

Researchers have also employed supervised learning for reducing metal
artifacts and estimating the deviation of the voxel values after inserting
neuroelectrodes [102]. Self-supervised learning approaches, focused on
training models for inpainting the regions affected by metal artifacts, have
demonstrated improvements in simultaneously tackling metal artifact reduction
while preserving the essential anatomical structures near the inserted
implants [88]. In addition to supervised and self-supervised techniques,
various types of GANs have been employed in the literature for unsupervised
metal artifact reduction. Optimized conventional GANs can reduce metal
artifacts in high-resolution and physically realistic CT scans, with good
generalization to clinical CBCT imaging technologies for inner-ear scans
[103]. Conditional GANs, inspired by the pix2pix-GAN [104], have successfully
reduced metal artifacts in spine CBCT scans, enabling precise recovery of
fiducial markers located outside the C-arm’s field-of-view (FOV) [105]. A
Cycle-GAN has also been employed to efficiently reduce metal artifacts by
generating synthetic CT (sCT) from Megavolt CBCT (MVCBCT) and improving the
quality of CBCT scans [106].

##  IX Motion

Many of the state-of-the-art volumetric reconstruction techniques for CBCT
rely heavily on the initial assumption that the projections are acquired from
a stationary object. However, this assumption is often violated because of
periodic respiratory and cardiac motions or non-voluntary and non-periodic
movement of air bubbles in the abdominal area. When reconstructing CBCT
volumes using projections acquired from various body states under motion,
motion streak artifacts appear in the reconstructed volume, as shown in Figure
3. The severity of the resulting artifacts is positively correlated with the
intensity of motion. The most common approach to tackle motion artifacts in
CBCT scans is dividing the projections based on the motion state (motion-
resolved [107, 108, 109, 110, 111, 112]), periodic motion state (phase-
resolved [111, 113, 114]) or acquisition time (time-resolved [115, 116]), and
then reconstruct multiple volumes based on each batch of projections to
generate a 4D CBCT.

#### Motion-resolved methods

A novel approach using CNNs to predict the missing projections in motion-
resolved 4D-CBCT combined with a bin-sharing technique to accelerate the
acquisition process, substantially removed streak artifacts compared with
standard conjugate gradient reconstruction [107]. Training a residual U-Net
also reduces the streak artifacts appearing in 4D-CBCT by addressing the
sparseness of the projections acquired in each breathing phase [108]. Residual
dense networks (RDNs [110]) have successfully improved sparseness artifacts
using an in-house lung and liver dataset, as well as a public dataset of the
SPARE challenge [117, 118]. Similar research demonstrates that combining the
information of the different breathing phases to train a prior-guided CNN can
effectively reduce artifacts in motion-resolved 4D-CBCT scans [109]. In
addition to training single models, researchers attempted to optimize a
cascade of spatial and temporal CNN models to combine spatial and temporal
information for maximum artifact removal and to avoid errors in the
tomographic information [112]. A dual-encoder CNN (DeCNN) architecture
simultaneously processes and combines the information of 4D motion-resolved
volumes and the averaged volume, thereby improving the sharpness of the edges
in moving and fixed tissues in 4D-CBCT [119].

#### Phase- and time-resolved methods

Phase-resolved CBCT is a specific case of motion-resolved CBCT, where
projections are selected based on the different phases of body volume under
periodic, respiratory, or cardiac motion. Motion Compensation Learning-induced
sparse tensor constraint reconstruction (MCL-STCR) was shown to improve
4D-CBCT scans for all motion phases [120]. 3D-CNNs have shown to effectively
mitigate sparse-view artifacts in motion-compensated 4D-CBCT scans
reconstructed using FDK, thereby enhancing the overall quality [114]. N-Net
uses the prior volume reconstructed using all projections to remove streak
artifacts. CycN-Net combines the temporal correlation among the phase-resolved
scans to reduce streak artifacts that are caused by sparse-view sampled
motion-resolved projections [111]. Furthermore, training a patient-specific
GAN-based model on phase-resolved 4D-CBCT to reproduce CT quality using CBCT
scans demonstrates improvements when applied to test set projections acquired
from the same patient [113]. In addition to motion- and phase-resolved
methods, training a U-Net can remove sparseness artifacts from time-resolved
4D-CBCT without requiring any prior information [115]. GANs have also
demonstrated the capacity of estimating sCT scans from time-resolved 4D-CBCT
and the average 3D-CBCT volume, resulting in a comparable improvement in dose
calculation using both strategies [116].

#### Biomechanical and physical modeling

In addition to phase-, motion-, and time-resolved techniques, researchers have
also explored targeting motion artifacts by physically modeling the motion
using a deformation-vector-field (DVF) and by optimizing an autofocus metric
(i.e., maximizing some measure of sharpness). The Simultaneous Motion
Estimation and Image Reconstruction (SMEIR) model, as well as its
biomechanical modeling-guided version (SMEIR-Bio), are examples of models
developed for motion effect prediction in lung 4D CBCT scans [121]. These
models have also been enhanced using a U-Net-based DVF optimization technique,
leveraging a population-based deep learning scheme to improve the accuracy of
intra-lung DVF prediction (SMEIR-Unet) in the same research work. By
incorporating the reference phase in 4D CBCT as an extra channel to their
model, training a 4D U-Net for motion estimation, with fine-tuning the
estimated DVFs, the performance of SMEIR models increases for motion artifact
reduction [122]. CNN-based architectures have been optimized to estimate
deformable motion and predict the motion intensity on $8\times 8$ grids
covering the axial slice, followed by a preconditioning technique to favor
more likely motion intensities [123]. CNNs have also been trained for motion
compensation in CBCT scans to solve the high-dimensional and no-convex problem
of optimizing the autofocus metric [124].

#### Alternative methods

The autofocus metric has also been replaced with the Context-Aware Deep
Learning-based Visual Information Fidelity (CADL-VIF) image similarity metric
to optimize multi-resolution CNNs [125]. This approach aims to improve motion
degradation and compute sharp scans while preserving the tissue structures by
optimizing visual information fidelity (VIF) without requiring motion-free
ground truth. An alternative to the autofocus metric is using contrastive loss
to train GAN architectures to enhance the quality of 4D-CBCT scans and to
reduce streak and motion artifacts [15]. To address the slow speed of
reconstruction and to compensate for the errors of 4D-CBCT due to the severe
intraphase undersampling, a feature-compensated deformable convolutional
network (FeaCo-DCN [126]) model has been proposed. It achieves nearly real-
time reconstruction and accurate CBCT, outperforming the previous method
applied to the SPARE Challenge [117, 118]. Besides the numerous research
studies addressing motion in 4D CBCT, which requires recording the patient’s
breathing curve, researchers have also simulated motion in CBCT scans based on
the estimation of DVFs according to 4D CT ground truth scans [127]. They
subsequently trained a dual-domain model to mitigate 3D CBCT motion artifacts
in the projection and volume domains. The clinical validation on real-world
CBCT images yielded positive feedback from clinical experts, demonstrating the
effectiveness of their approach for motion compensation [24]. In addition to
all methods to reduce motion artifacts, researchers have successfully used an
artifact-driven slice sampling technique to avoid artifacts caused by moving
air bubbles in the segmentation of the female pelvis [128].

(a) Distribution based on model architecture.

(b) Distribution based on artifact type.

Figure 4: A visual summary of the distribution of the covered research
literature in CBCT artifact mitigation using deep learning, separately for two
time periods, (a) based on three generic deep learning architecture categories
given a broad categorization by artifact type, and (b) based on the
distribution according to the type of artifact.

##  X Discussion and Recommendations

Artifact type |  Year |  Title |  Anatomic site |  Model | Patients |  GPU Hardware |  Published code?
---|---|---|---|---|---|---|---
image quality |  2019 |  Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography [45] |  brain, pelvis |  cycle GAN | 44 |  NVIDIA TITAN XP |  -
2019 |  CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation [48] |  pelvis |  cycle GAN | 33 |  NVIDIA Tesla P100 |  -
low-dose |  2019 |  Computationally efficient deep neural network for computed tomography image reconstruction [67] |  abdomen |  U-Net | 10 |  NVIDIA GTX 1080 Ti |  -
2020 |  Neural networks-based regularization for large-scale medical image reconstruction [55] |  cardiac |  U-Net | 19 |  - |  -
sparse-view |  2023 |  Sub-volume-based Denoising Diffusion Probabilistic Model for Cone-beam CT Reconstruction from Incomplete Data [129] |  breast |  diffusion model | - |  128x NVIDIA Tesla V100 |  -
2023 |  Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction [71] |  knee |  learned reconstruction | - |  NVIDIA RTX 3090 |  yes
2020 |  Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction [110] |  liver, lung |  residual dense network | 20 |  NVIDIA GeForce RTX 2080 Ti |  yes
2020 |  Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction [69] |  breast |  GAN | 42 |  NVIDIA Titan RTX |  yes
limited-angle |  2020 |  C-arm orbits for metal artifact avoidance (MAA) in cone-beam CT [101] |  chest phantom |  U-Net | 0 |  NVIDIA TITAN X |  -
scatter |  2019 |  Real-time scatter estimation for medical CT using the deep scatter estimation: Method and robustness analysis with respect to different anatomies, dose levels, tube voltages, and data truncation [90] |  head, thorax, pelvis |  U-Net | 21 |  NVIDIA Quadro P6000 |  -
metal |  2021 |  Inner-ear augmented metal artifact reduction with simulation-based 3D generative adversarial networks [130] |  temporal bone images |  GAN | 597 |  11 GB GPU |  -
motion |  2022 |  Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN) [119] |  lung |  CNNs | 26 |  NVIDIA Titan RTX |  -
2022 |  Deep learning‐based motion compensation for four‐dimensional cone‐beam computed tomography (4D‐CBCT) reconstruction [114] |  thorax |  CNNs | 18 |  NVIDIA Tesla V100S |  yes
TABLE I: Summary of a subset of studies selected guided by recency and number
of citations. The table provides details about artifact category, publication
year, study title, anatomic site, model type, number of patients, GPU
hardware, and whether the code was published.

The previous sections have outlined the methodology and the complete workflow
employed for deep learning based mitigation of artifacts in CBCT scans,
addressing each specific type of artifact separately. This section presents a
summary, emphasizing the central role of various deep learning approaches. The
objective is to offer a comprehensive review of the architectures employed for
different artifact types, highlighting both the promising aspects and the
limitations in the current literature.

In general, a trend is observed in shifting from conventional supervised
learning with CNNs and U-Net-type architectures to exploring more modern
learning paradigms such as GANs, and investigating self-supervised and
unsupervised methods, leveraging e.g. Cycle-GANs, as depicted in Figure 4a. In
particular, Cycle-GAN-based architectures offer the appealing feature of
enabling model training without needing paired labeled data [131]. However,
they come with high data requirements, rising attention toward methods and
projects for data collection, synthetical data generation, dataset merging
from diverse sources, and data homogenization. This trend suggests the rise of
research works attempting at the adaptation of generative models including
GANs, Cycle-GANs, as well as scored-based models [132, 133], in upcoming
research endeavors. A recent example [129], which employs denoising diffusion
probabilistic models [134, 135] for sparse-view CBCT reconstruction,
demonstrates a lot of potential for future research, however at the expense of
tremendous compute resources (up to 128 GPUs, see also Table I). On the other
hand, less computationally intense, U-Net-based, architectures have
demonstrated their merit in successfully addressing artifacts across all
categories, making them a highly recommended and robust baseline approach for
artifact mitigation.

In the context of this survey, the primary DL-based architectures used in the
literature can be divided into four key categories: CNNs, U-Nets, GANs, and
cycle-GANs. Here, we categorize architectures with multi-scale information
fusion, i.e. including connections from the network’s input (encoding) layers
to output (decoding) layers (such as [67]) under the category U-Net, while
those without such direct connections (such as autoencoders [136]) are
categorized as CNNs. DL-based models generally require medium to large
datasets for training, validation and testing through clinical evaluation.
While medium-sized datasets, including multiple patients, can serve as
starting points for training CNNs and U-Nets [83], GANs perform better using
datasets containing at least dozens of patient scans [42]. This trend
generalizes to 3D and 4D reconstruction, where larger input sizes and a higher
number of scans become essential, in particular for 4D [122]. A review of the
studies presented in Table I reveals that the majority of research was
conducted with fewer than 50 patients. This relatively small number of
patients can pose challenges for validating the approach across a diverse
population. Consequently, the robustness of these models warrants further
scrutiny to ensure their ability to generalize well across various human
anatomies.

CNN architectures, known for their stable convergence and versatility,
demonstrate a wide range of applications for artifact reduction through
adapting different vision backbones [32] and incorporating diverse
architectural components such as attention blocks[24]. However, in terms of
multi-scale information fusion, they are inferior to U-Nets and their variants
(e.g., U-Net++ [137]), which demonstrate a fast convergence in supervised
learning due to the internal architectural connections between different
layers enhancing the multi-resolution information fusion [7]. Since CNNs and
U-Nets are predominantly being trained in a supervised manner, their learning
technique necessitates explicitly labeled data to define the task. On the
other hand, generative models (GANs), incorporating an adversarial loss, also
offer potential applications in generating high-quality synthetic scans to
meet the data needs of the deep learning-based architectures [36]. Moverover,
Cycle-GANs compute the inverse path of artifact reduction automatically, using
a cycle-consistent loss, thus being able to learn artifact reduction without
the need for paired artifact-free ground truth [48].

Only four of the papers presented in Table I provide a public code repository
to reproduce their results. This highlights a considerable shortage of open
science practices, such as sharing code, to promote transparency and
reproducibility in research. It is strongly recommended for researchers to
share their code publicly to enhance the credibility and reproducibility of
their work and accelerate scientific progress in this field.

##  XI Conclusions

We presented a survey on the application of deep learning and convolutional
neural networks to reduce various types of artifacts in CBCT scans. We
categorized the existing literature based on the type of artifacts they
address as well as the methodology employed. Figure 4b illustrates the amount
of the recent research works based on the type of artifacts. It is observed
that there has been considerable growth in artifact reduction research
compared with focusing more generically on scan quality after 2021. The
opportunity of reducing the imaging dose with the help of compensating for
artifacts when using low-dose scans, sparse-view, and limited-angle
acquisition techniques have gained substantial attention due to the ease of
simulation and computing the ground truth, especially for sparse-view and
limited-angle approaches.

However, metal and scatter artifacts have received less attention. This may
also be due to the challenges involved in computing the ground truth for metal
artifacts, or the high computational cost of Monte-Carlo simulation for
scatter artifacts. We expect that the research community could profit from
open-source accurate and fast artifact simulations for training models (as
before with XCAT [138]). The development of such simulations could also serve
as a driving force for physics-based artifact modeling or training physics-
informed neural networks (PINN) [139] for artifact reduction. These
simulations would benefit from GPU implementations for data generation to
enable on-the-fly integration into the training pipelines with neural
networks. In addition to simulations, there is a research gap for open-source
data augmentation techniques, such as [140, 141], also based on incorporating
simulated artifacts into real datasets.

In addition to simulation and augmentation tools for modelling, the research
community would benefit from the availability of open-source datasets.
Researchers are still reporting results on phantoms and cadavers, indicating a
need for more diverse and realistic publicly available datasets. Nevertheless,
despite the lack of open-source 4D CBCT datasets with raw projections and
breathing curves, there is an increase of motion artifact reduction research
in recent literature. The collection and sharing of up-to-date benchmark
datasets on a large scale, similar to the SPARSE [117, 118] and SynthRAD [142]
challenges, would enhance the quality of many research works and provide the
opportunity for fair and accurate comparison of different approaches.
Furthermore, many studies suffer from a lack of clinical evaluation. The
availability of open-source standard clinical evaluation platforms would be of
significant help in addressing this issue.

In terms of methodology, there has been a noticeable trend of moving beyond
supervised learning towards self-supervised, unsupervised, and domain
adaptation methods in recent years. Researchers have started incorporating
more physically inspired ideas into the neural networks and utilizing prior
patient knowledge to personalize the models for specific anatomies. One of the
drawbacks often observed in the current literature is the absence of ablation
studies. For example, in the case of approaches employing dual-domain
optimization in both projection and volume domains, the performance gained in
each domain should be estimated separately. Besides artifact reduction after
the CBCT acquisition, adapting the acquisition process itself using neural
networks, such as C-arm trajectory adjustments applied to metal artifact
reduction, present a further exciting avenue for future research.

In summary, substantial progress has been made in recent years transferring
state-of-the-art methods from deep learning based computer vision to the
domain of CBCT imaging and in particular the amelioration of prevalent imaging
artifacts, with a clear potential to improve diagnosis and treatment in
clinical practice.

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|  Mohammadreza Amirian received his M.Sc. degree in electrical communications
technology in 2017 from Ulm University, Germany. He immediately began his
Ph.D. in computer science at the Neural Information Processing Institute of
Ulm University following the completion of his master’s degree. During his
Ph.D., he worked as a researcher at both the Institute of Applied Information
Technology (InIT) and the Center for Artificial Intelligence (CAI) at Zurich
University of Applied Sciences (ZHAW) in Winterthur, Switzerland. His research
interests include biophysiological signal processing for person-centered
medical and affective pattern recognition. Furthermore, his research pursuits
extend to interpretable deep learning algorithms for medical image processing
and quality enhancement in imaging technologies.
---|---
|  Daniel Barco received a M.Sc. degree in Applied Information and Data
Science from the Lucerne University of Applied Sciences and Arts, in 2020.
Currently, he is dedicated to advancing AI as a Ph.D. candidate at the
University of Zurich (UZH) and works as a researcher at the Centre for
Artificial Intelligence at the Zurich University of Applied Sciences (ZHAW),
Winterthur, Switzerland. His research pursuits revolve around pioneering novel
neural architectures for computer vision, while also contributing to the
development of Robust and Trustworthy AI solutions.
---|---
|  Ivo Herzig is an engineer and computer scientist with professional
background in software development, computational geometry, computer graphics
and robotics. He is currently a Researcher at Institute of Applied Mathematics
and Physics (IAMP), Zurich University of Applied Sciences (ZHAW), Switzerland,
where he is focusing on deep learning for medical image analysis in the area
of image-guided radiation therapy (IGRT).
---|---
|  Frank-Peter Schilling received his PhD degree in Physics from the
University of Heidelberg, Germany in 2001. He subsequently spent many years in
fundamental research at physics laboratories including CERN (Geneva,
Switzerland), where he was involved in the discovery of the Higgs particle in
2012. Besides managing international scientific projects and teams, and being
a top-cited author of particle physics research journal publications (h-index
of 150), he developed a strong profile in computer science, big data,
statistical modelling, and machine learning. He joined Zurich University of
Applied Sciences ZHAW (Winterthur, Switzerland) in 2018 and is senior
lecturer, group leader and deputy head of ZHAW’s Centre for AI (CAI). His
research interests include AI and Deep Learning, with a focus on Computer
Vision (in particular for Medical Imaging), as well as on Machine Learning
Operations (MLOps). In addition, he is interested in Trustworthy and
Certifiable AI, as well as in applications of Deep Learning in the Physical
Sciences.
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