Key words
computed tomography - image reconstruction - deep neural network - deep learning -
artificial intelligence - image denoising
Background
In the clinical routine, the crucial aim of CT imaging is to provide clinically relevant
information or more specifically, information as to whether a specific feature is
found or not, with reasonable certainty. However, the certainty of that radiological
decision depends heavily on the image quality, e. g. contrast, resolution, noise,
and artifacts [1].
Alongside image quality, radiation dose is an important element in CT protocol optimization.
Furthermore, acquisition and reconstruction time should be acceptable, especially
in a high-level workflow setting such as emergency radiology. Both image quality and
radiation dose are affected by acquisition parameters, patient constitution, and positioning
[2]
[3]. Another important element of the CT imaging process, influencing quality and reconstruction
time, is the mathematical transformation of the raw data into a three-dimensional
volume viewable as an anatomical slice image. To solve this transformation, several
reconstruction algorithms have been developed [4]. Algorithms routinely used since the introduction of computer tomography are filtered back projection (FBP), which was used into the early 2010 s, and iterative reconstruction (IR), which was subsequently used [5]. To understand the ongoing developments in CT image reconstruction, the principles
of FBP and IR are briefly reviewed, followed by an introduction to deep learning (DL).
Filtered back projection
FBP is the most commonly used analytical reconstruction method owing to its computational
efficiency and stability creating images rapidly. The mathematical approach to FBP
is primarily the idea that a projection, consisting of measurements in multiple angles,
can be back projected into a model of the scanned object by an inverse radon transformation
with a high-pass filter ([Fig. 1]). Without a filter, smearing the projection values into the radiation path would
result in a blurry object morphology. This filter or kernel can additionally be modified to facilitate evaluation of specific anatomic components,
e. g. bones [4]. Later, FBP benefited from advanced methods compensating geometric problems [6]. The disadvantage of FBP is the strong relationship between radiation dose and noise,
that is especially problematic in obese patients [7]. In the ongoing advancement in CT imaging from measured data to algorithmic optimized
data, FBP is closest to measured data. As computational power for industrial purposes
and graphical processing was advancing, FBP was gradually replaced by iterative reconstruction
[8].
Fig. 1 Schematic illustration of image reconstruction by filtered back projection. After
the acquisition the raw data consisting of the measurement of attenuation profiles
in multiple angles is transformed into an image domain with a filter or kernel to
compensate for the blur emerging with reconstruction.
Abb. 1 Schematische Darstellung einer Bildrekonstruktion durch gefilterte Rückprojektion.
Nach Akquisition werden die Rohdaten, welche den Schwächungsprofilen in multiplen
Winkel entsprechen, mittels Rückprojektion als anatomisches Schnittbild rekonstruiert.
Ein Filter bzw. Kernel kompensiert dabei die Kantenverschmierung.
Iterative reconstruction
The IR method starts with the creation of an image estimate that is basically equal
to FBP. Subsequently, the image estimate is projected forward into an artificial sinogram
and iteratively corrected by comparison with the original raw data sinogram. When
a predefined endpoint condition in the algorithm is fulfilled, the iterative cycle
is stopped, and the images are readily reconstructed ([Fig. 2]). However, the iterative cycle can get implemented in different steps, e. g. the
raw data and the image domain. Additionally, further statistic adjustments and modelling
can be used. Roughly iterative algorithms can be divided into methods without statistics
(e. g. ART, the first iterative algorithm), methods with modeling of photon counting
statistics (e. g. hybrid IR), and model-based methods beyond photon counting (MBIR) [9]
[10]. With enhanced image quality, a reduction in patient dose was feasible. In a systematic
review from 2015, the mean effective dose for contrast-enhanced chest CT with IR decreased
by 50 % compared to FBP [11]. IR is now considered criterion standard for CT image reconstruction.
Fig. 2 Simplified exemplary illustration of iterative image reconstruction. From the raw
data a reconstructed image estimate is generated, which is iteratively compared with
the original sinogram in the forward projection and corrected until a predefined endpoint
is reached.
Abb. 2 Vereinfachte, beispielhafte Darstellung einer iterativen Bildrekonstruktion. Aus
den Rohdaten wird eine Rückprojektion erstellt, welche in der Vorwärtsprojektion iterativ
mit dem originalen Sinogramm verglichen und verbessert wird, bis ein definierter Endpunkt
erreicht wird.
Artificial neural networks and deep learning
Artificial neural networks and deep learning
The recent rise in popularity of artificial intelligence is primarily due to the advances
in the field of artificial neural networks (ANNs). ANNs are a subfield of machine learning (ML). In machine learning a model learns by training data and is subsequently able
to perform specific tasks. This process is termed supervised if the training data
contains both input and desired output. The system learns how to do tasks only by
showing what the results should be and not how to perform those tasks [12]. Aside from the supervised approach, the learning process can be unsupervised, or
reward-based as in reinforcement learning [13]. ANNs as a method of machine learning are inspired by the operation principle of
neurons, although they do not simulate them in detail. ANNs consist of interconnected
nodes, comparable to the cell body of a neuron with synapses for signal transduction
Those networks are usually built with an input layer, one or more hidden layers, and
an output layer. On information flow values are passed from one node to the next connected
nodes. However, the connections themselves are weighted and will modify the incoming
values from the previous nodes to a subsequent node. In the node itself, the sum of
all weighted incoming values is passed through an activation function that determines
the output of a single node. Depending on the sum and the activation function, similar to the principle of a neuronal excitation threshold, there might be no activation
at all. Leaving the output layer, the final calculated output is compared to the desired
output by a loss or error function. Subsequently the previously mentioned weights are adjusted, and the network is recalculated.
Using this process of iterative readjustment and reevaluation, the network is trained,
in order to finally produce an output with an acceptable level of inaccuracy. The
training data, consisting of multiple inputs with corresponding desired outputs (also
known as ground truth), is usually divided into a training, a validation, and a test set. Different from
the training set, the validation set will not be used directly for the training regarding
the iterative adjustment of weights. However, the validation set is utilized to monitor
network performance and to fine-tune the non-learnable hyperparameters that need to
be manually adjusted beforehand. Aside from performance evaluation and hyperparameter
tuning, the dataset split enables the recognition of overfitting. That means a network
is too closely adapted to the training set, by identifying more features than the
data provides. This inevitably leads to an inability to recognize unseen data correctly.
When training has been finished, there is still a lot of testing ahead, usually with
another unseen testing dataset. When the network has been trained and tested to satisfaction,
it will eventually be applied to unknown data outside of the training environment.
This is called inferencing
[14]. With ANNs, two concepts are commonly mentioned: deep learning (DL) and convolutional neural networks (CNNs). Deep refers to the multiple layers of an ANN between the input and the output
layer, increasing the complexity usually with millions of parameters being calculated.
Those networks are sometimes labeled as deep neural networks (DNNs). Convolutional neural networks are a class of DNN. They are especially interesting
for tasks in computer vision, as they operate on grid pattern data, for example images.
Usually CNNs show a typical architecture with convolutional and pooling layers that
can be passed iteratively and a fully connected layer. In the convolutional and pooling
layers multiple filter kernels consisting of a grid of weights are applied to the
image extracting different features. While the first layers usually represent low-level
features like edges and corners, mid-level features might be parts of objects or organs
and high-level features are whole structures or organs. The fully connected layers
finally flatten the previous layer and create a one-dimensional vector so the output
can be classified. In the training process additional to the adjustment of weights,
the filter kernels are optimized, again, by loss functions comparing the output to
the known ground truth [15]. Generative adversarial networks (GANs) are another type of ANN that show potential
for tasks in medical imaging, e. g. denoising. They consist of two neural networks:
a generator, creating artificial samples from input data, and a discriminator, learning
to distinguish real from generated data [16]. The final principle of transforming an image with characteristics of another image
is called image-to-image translation [17].
There are multiple possible objectives for ANNs in radiology. However, the most common
can be assigned to the following applications: classification, detection, segmentation,
and image optimization, e. g. denoising [18]. Classification tasks in radiology include concluding whether lesions, e. g. pulmonary
nodules, are benign or malignant. While lesions are characterized during classification,
the existence of lesions and their location are the focus of detection tasks in the
CNN. A promising example is the study by Lakhani et al. who trained two DNNs on the
detection of tuberculosis. The network ensemble showed a sensitivity of 97.3 % and
a specificity of 94.7 %. This approach could facilitate mass screenings and have a
positive impact on healthcare in regions with TBC epidemics and low radiological infrastructure
or expertise [19]. Segmentation defines borders between anatomical compartments or structures, for
example of the liver. Segmentation of organs will, for example, accelerate volumetric
tasks and radiation therapy planning [14]. Image denoising as an application is especially beneficial for low-dose CT. Since
2017, multiple networks and methods have been proposed. In 2017 Wolterink et al. combined
a generator CNN with an adversarial CNN and showed the improved ability to generate
images similar to routine dose from low-dose CT [20].
Deep learning image reconstruction
Deep learning image reconstruction
The intention of developing DL image reconstruction algorithms is mainly the improvement
of image quality in comparison to the performance of current IR algorithms, while
reduction of radiation dose might be a secondary benefit. Although there are differences
with respect to the structure and the development of the DLR algorithms, the basic
concept is comparable ([Fig. 3]). The first step is designing the network architecture. This includes setting the
hyperparameters, i. e., parameters not learned during the training process, for instance
the size and topology of the network, the type of activation function, as well as
the learning rate. The definition of the hyperparameters is crucial to the performance
of a network. The next step is the training process. As previously explained, this
requires a dataset consisting of multiple low-quality input images and corresponding
high-quality ground truth images. The dataset originates from phantom scans as well
as patient examinations in the clinical setting. Starting the supervised training
process, the DLR algorithm will create an output image out of the low-quality input
data that will be immediately compared to the corresponding ground truth image, calculating
the error function to the output. In a so-called backpropagation process, the impact
of each weight to the error will be calculated and subsequently weights in the network
will be adjusted accordingly. This process is repeated iteratively, and the network
will successively learn to eliminate most of the noise, while keeping anatomical detail
[21]
[22]. With the complexity of the image data, the training process will likely involve
millions of calculations on each iteration and require an extreme amount of computational
power, even when facilitated by use of GPU. As previously mentioned, the complete
dataset will usually be split into a training set, a validation set, and a test set
to ensure no overfitting is present. Apart from stability, a thorough evaluation of
anatomical details is necessary. There is no guarantee that important information
will not be lost during reconstruction. Conversely, a reconstruction algorithm might
also be able to create new artificial structures, known as hallucinations, that may
simulate a pathology [23].
Fig. 3 Schematic steps of deep learning image reconstruction. In the training phase low-
and high-quality images are fed into a neural network. After completed training and
validation, the algorithm can generate high-quality images out of unseen low-quality
inputs.
Abb. 3 Schematische Darstellung des Trainings und der Bildrekonstruktion durch ein Deep-Learning-Netzwerk.
Während der Trainingsphase werden dem Netzwerk Bilder mit hoher und niedriger Qualität
zugeführt. Nach Abschluss dieser Phasen und der Validierung ist das Netzwerk in der
Lage, hochqualitative Bilder aus bisher ungesehenen Daten mit geringer Qualität zu
generieren.
Advancements from deep learning image reconstructions
Advancements from deep learning image reconstructions
There are several advancements from DL image reconstruction that are intricately linked
to each other. The most essential advantage is improved noise reduction. Noise is
the variation of attenuation coefficients in homogeneously dense material. In reconstruction
this leads to a grainy image appearance [24]. While DL algorithms are trained on low-dose data, they are capable of noise reduction
while containing a true signal. This aspect can be quantified by the signal-to-noise
ratio (SNR) [25]. In 2017, Jin et al. proposed a CNN based on the U-net, which showed improved SNR
in experimental datasets compared to IR [26]. The loss of anatomical information can be visualized by subtraction of CT slices
reconstructed in DL and iterative methods ([Fig. 4]). Denoising has two immediate clinically relevant aspects. Firstly, lesions of any
kind might be better detectable. This is expected to facilitate the daily work of
a radiologist, e. g. in oncological imaging ([Fig. 5]). Secondly, noise is directly associated with radiation dose. A lower tube current
will reduce the dose, but noise will increase concurrently, thereby deteriorating
image quality and assessability. With DL image reconstruction, the increase in noise
can be compensated. This aspect will contribute to imaging in low-dose scenarios,
e. g. CT of pediatric patients ([Fig. 6]). Another feasible opportunity of DL image reconstruction is the virtual improvement
of spatial resolution by creating thin-slice images out of thick slices. This was
demonstrated by Umehara et al. in chest CT imaging as well by Park et al. who further
successfully showed deblurring of bone edges due to partial volume effect [27]
[28]. An additional advancement due to DL image reconstruction is improved artifact reduction.
Beam hardening artifacts remain a highly relevant problem especially in head and neck
imaging due to dental fillings as well in the case of imaging after osteosynthesis
implants, impeding detection of implant loosening or periprosthetic fractures. In
2018, Zhang et al. developed a CNN trained on metal-free, metal-inserted, and precorrected
images capable of superior metal artifact suppression while providing anatomical detail
[29]. In addition, DL image reconstruction will be able to reduce beam hardening artifacts
from bony structures. Especially in head CT, such artifacts can mask subtle intracranial
hemorrhage ([Fig. 7]).
Fig. 4 Comparison of a head CT of an 87-year-old woman, reconstructed by A FBP, B ASiR-V-50 % and C DLIR-H, show reduced image noise for DL image reconstruction. Subtraction images
of D FBP-DLIR-H and E ASiR-V-50 %-DLIR-H demonstrate no anatomical structure besides the calvaria, indicating
that anatomical detail is preserved. Slice thickness: 0.625 mm. W: 100, C: 40.
Abb. 4 Der Vergleich einer CCT einer 87-jährigen Frau, rekonstruiert durch A FBP, B ASiR-V-50 % und C DLIR-H, ergibt einen deutlich entrauschten Bildeindruck. In den Differenzbildern
D FBP-DLIR-H und E ASiR-V-50 %-DLIR-H zeigt sich neben dem dichten Schädelknochen keine anatomische
Struktur, als Indikator für die Erhaltung anatomischer Details. Schichtdicke: 0,625 mm.
W: 100, C: 40.
Fig. 5 Comparison of an abdominal CT of a 66-year-old woman with hepatic metastases generated
by A ASiR-V-50 % and B DLIR-H shows a more homogeneous liver texture due to denoising with DLIR-H. Slice
thickness: 0.625 mm. W: 400, C: 40.
Abb. 5 Abdomen-CT einer 66-jährigen Patientin mit Rekonstruktionen durch A ASiR-V-50 % und B DLIR-H. Letztere zeigt durch das verminderte Bildrauschen eine deutlich homogenere
Lebertextur. Schichtdicke: 0,625 mm. W: 400, C: 40.
Fig. 6 Comparison of a low-dose chest CT of a 7-year old boy generated by A ASiR-V-50 % and B DLIR-H (without lung kernel) shows a sharper image impression and reduced noise with
DLIR-H. Slice thickness: 1.25 mm. W: 1600, C: –500.
Abb. 6 Der Vergleich eines Low-Dose-Thorax-CT eines 7-Jährigen, rekonstruiert durch A ASiR-V-50 % and B DLIR-H (ohne Lungenkernel), zeigt einen deutlich schärferen Bildeindruck und ein
deutlich vermindertes Bildrauschen mit DLIR-H. Schichtdicke: 1,25 mm. W: 1600, C:
–500.
Fig. 7 Comparison of a head CT detail of an 85-year-old woman reconstructed by A FBP, B ASiR-V-50 % and C DLIR-H demonstrates improved reduction of the beam hardening artifact caused by a
dental filling in the left upper quadrant. Slice thickness: 2.5 mm. W: 100, C: 40.
Abb. 7 Der Vergleich eines CCT-Ausschnitts einer 85-Jährigen, rekonstruiert durch A FBP, B ASiR-V-50 % und C DLIR-H, zeigt die verbesserte Reduktion von Hartstrahlartefakten, die hier durch
eine Zahnfüllung im linken oberen Quadranten verursacht sind. Schichtdicke: 2,5 mm.
W: 100, C: 40.
Deep learning reconstruction algorithms in the clinical routine
Deep learning reconstruction algorithms in the clinical routine
To our knowledge, there are currently two commercially available CT image reconstruction
algorithms using DL methods cleared by the FDA, TrueFidelity by GE Healthcare and
AiCE by Canon Medical Systems. For this review we did a literature search, performed
via PubMed with the search terms: “deep learning CT reconstruction” “DLR”, “DLIR”,
“AiCE” and “TrueFidelity”. Studies performed with TrueFidelity or AiCE as reconstruction
algorithms were of interest. Altogether seven studies were relevant. The studies,
their characteristics, and important results are depicted in [Table 1]. Both TrueFidelity and AiCE have been investigated in phantom and patient studies.
The evaluated criteria usually consisted of quantitative and qualitative measurements.
Utilizing DLIR in coronary computed tomography angiography, Benz et al. showed a significant
reduction in noise and higher image quality, while DL image reconstruction was equal
to IR with regard to diagnostic accuracy, sensitivity, and specificity in the detection
of significant coronary artery stenosis with invasive coronary angiography as criterion
standard [30]. The phantom study by Greffier et al. published in 2020 showed a potential for dose
reduction of up to 56 % for TrueFidelity, by achieving comparable detectability with
iterative reconstruction and DL while lowering the dose [31]. This was especially successful for small and subtle features. Detectability was
calculated utilizing a non-prewhitening matched filter with eye filter (NPWE) model
as a surrogate for human perception that includes noise and resolution [32]. A potential for dose reduction was furthermore shown in the clinical context in
a study from our institution in 2020. The DL image reconstruction allowed improved
SNR and CNR at the same dose levels compared to IR [33]. AiCE was examined by Akagi et al. in 2019 by measuring noise, CNR, and image quality
in abdominal CT scans, as stated by two radiologists. They were able to show improved
CNR and image quality compared to images generated by hybrid iterative reconstruction
and MBIR [34]. Another study in 2019 by Nakamura et al. evaluated the detectability of hypovascular
hepatic metastasis in images reconstructed with AiCE in addition to measuring noise
and CNR. They demonstrated less image noise and superior conspicuity for DLR compared
to the iterative algorithm [35]. A phantom study by Higaki et al. from 2020 additionally examined spatial resolution
with a task-based modulation transfer function at 10 % [36]. MBIR outperformed the other algorithms, while the DL algorithm was still superior
to FBP and hybrid IR. Furthermore, this study calculated the detectability index for
the aforementioned reconstruction methods at different doses. Detectability as the
quality criterion is dependent on noise as well as spatial resolution. Consequently,
MBIR showed the highest detectability in most dose settings. However, for low dosage
the DL method outperformed the other algorithms. This underlines the potential for
clinical scenarios with the aim of low dosage, e. g. pediatric CT. The imaging quality
of the common bile duct in maximum intensity projection was improved when reconstructed
with DL compared to iterative methods in a study by Narita et al. in 2020 [37]. This could facilitate scenarios with bile duct pathologies when MR is not available
or possible and preoperative planning is needed.
Table 1
Summary of published studies investigating DL image reconstruction algorithms by GE
Healthcare and Canon Medical Systems.
Tab. 1 Übersicht über bisherige Studien, welche die DL-Rekonstruktion von GE HealthCare
und Canon Medical Systems untersucht haben.
|
author of study and year of publication
|
study subject
|
number of patients included
|
reconstruction algorithms and vendor
|
evaluated criteria
|
important results
|
|
Benz DC et al. (2020) [30]
|
patients
|
43
|
-
ASiR-V-70 % SD
-
ASiR-V-70 % HD
-
DLIR-M
-
DLIR-H
by GE Healthcare
|
|
-
less noise in DLIR compared to ASiR
-
higher quality in DLIR compared to ASiR
-
no differences in sensitivity, specificity, and diagnostic accuracy between ASiR and
DLIR
|
|
Greffier J et al. (2019) [31]
|
phantoms
|
n/a
|
-
FBP
-
ASiR-V-50 %
-
ASiR-V-100 %
-
DLIR-L
-
DLIR-M
-
DLIR-H
by GE Healthcare
|
|
-
less noise in DLIR
-
higher spatial resolution in DLIR
-
higher detectability for small low-contrast lesions in DLIR,
-
comparable detectability for other lesions
|
|
Heinrich A et al. (2020), submitted manuscript [33]
|
patients
|
100
|
|
-
SNR and CNR in abdominal aorta, liver, spleen, kidney, pelvic bone, and abdominal
fat
|
|
|
Higaki T et al. (2020) [36]
|
phantoms
|
N/A
|
-
FBP
-
AIDR 3 D
-
MBIR (FIRST)
-
DLR (AiCE)
by Canon Medical Systems
|
-
noise (NPS)
-
spatial resolution (MTF)
-
detectability
|
|
|
Akagi M et al. (2019) [34]
|
patients
|
276
|
-
AIDR 3 D
-
MBIR (FIRST)
-
DLR (AiCE)
by Canon Medical Systems
|
-
noise (SD of attenuation of paraspinal muscle)
-
CNR of aorta, portal vein, and liver
-
image quality on 5-point scale rated by two radiologists
|
|
|
Narita K et al. (2020) [37]
|
patients
|
30
|
by Canon Medical Systems
|
|
|
|
Nakamura Y et al. (2019) [35]
|
patients
|
58
|
by Canon Medical Systems
|
-
noise (SD of attenuation of paraspinal muscle)
-
CNR from liver and hepatic metastasis
-
conspicuity of smallest metastasis by two radiologists on 5-point scale
|
|
AiCE = Advanced intelligent Clear-IQ Engine; ASiR = Adaptive statistical iterative
reconstruction; AIDR = Adaptive iterative dose reduction; CNR = Contrast-to-noise-ratio;
DLIR = Deep learning image reconstruction; DLR = Deep learning reconstruction; ICA = Invasive
coronary angiography; MBIR = Model-based iterative reconstruction; MTF = Modulation
transfer function; NPS = Noise power spectrum; SD = Standard deviation; SNR = Signal-to-noise-ratio;
TTF = Task-based transfer function.
To conclude, for both DL image reconstruction methods, a decrease of noise and improved
CNR were consistently shown, while one study showed diagnostic comparability regarding
the presence of significant coronary artery stenosis. Furthermore, both TrueFidelity
and AiCE were rated superior for subjective image quality by radiologists. However,
this must be interpreted with caution. As already stated by Hoeschen, a good image
impression is not necessarily associated with a higher diagnostic value [38].
Limitations
Although DLR algorithms seem to be highly effective for improving image quality, there
are several limitations or issues to be discussed.
Firstly, further external validation of DL image reconstruction is necessary. Although
comparison of non-diagnostic quantitative parameters e. g. CNR and noise, is important,
advancements should be driven by clinical superiority, for instance showing improved
detection of specific lesions or even a higher rate of more certain report statements
regarding ambiguous lesions. When comparing different DL image reconstruction methods,
it is important not to generalize results. Due to unique training and testing data,
every algorithm might show different findings for particular imaging scenarios.
Secondly, although dose reduction potential has been shown in phantoms and patients,
actual dose reduction in the clinical routine as a result of altering acquisition
parameters while performing diagnostic investigations has yet to be confirmed.
Thirdly, the decision-making process of trained algorithms is a black box to human
perception. The complexity of a neural network especially in this field of image reconstruction
is immense and conceptually different from human decision-making based on reasoning
and memory. While we are already using DL image reconstruction in the clinical routine,
computer scientists are examining methods and techniques to construct algorithms more
reasonable to human understanding. Even though a DL image reconstruction algorithm
might produce a correct image, it might be based on wrong reasoning. A specific lesion
might be removed or blurred because it was underrepresented in the training data and
thereby evaluated as noise. Conversely a non-existing lesion might be hallucinated
into the reconstructed images by the reconstruction algorithm. This problem of unreliability
will be difficult to solve. However, there are approaches to illuminate complex algorithms
in the concept of explainable AI
[39]
.
Conclusion and future outlook
Conclusion and future outlook
In our view machine learning will inevitably affect many aspects of radiology positively.
DL image reconstruction demonstrates improved image quality in terms of denoising
and artifact reduction and shows potential for dose optimization. However, the implementation
is not straightforward and currently in early clinical stages while the explainability
and reliability of machine learning are a focus of research. While early research
findings are promising, DL image reconstruction should be further introduced into
clinical practice and extensively investigated to provide evidence of clinical superiority.
Everything considered, after FBP and IR, reconstruction algorithms of CT images in
the clinical routine are expected to move towards an ML-based approach using CNNs
and possibly other upcoming methods, for example GAN. Aside from algorithm optimization,
image reconstruction will additionally benefit from advancements in imaging hardware,
for example photon counting CT (PCCT) [40]. As the performance of DL image reconstruction algorithms depends on the quality
of the training data, there might also be an additional advancement in the future
when the ground truth training data is provided by PCCT. Vice versa DL algorithms
might facilitate reconstruction of those PCCT images, as currently used algorithms
are presumably underachieving in terms of more complex data [40].
