[image] [image] Ressource protégée - Archive ouverte HAL Accéder directement au contenu

*[image] [image]*

* Documentation

• FR
Français (FR)

• Anglais (EN)
Se connecter

• [HAL science ouverte](https://www.hal.science/)

- Recherche
Loading...

Recherche avancée

Information de documents

Titres

○ [Titres](https://www.hal.ac.at/HAL/)

○ Sous-titre

○ Titre de l'ouvrage

○ Titre du volume (Série)

○ Champ de recherche par défaut (multicritères) + texte
intégral des PDF

○ Résumé

○ Texte intégral indexé des documents PDF

○ Mots-clés

○ Type de document

○ Sous-type de document

○ Tous les identifiants du document

○ Identifiant HAL du dépôt

○ Langue du document (texte)

○ Pays (Texte)

○ Ville

○ À paraître (true ou false)

Ajouter

Auteur
# Analysis of Disentangled Representation Learning for High-Resolution Dynamic MRI Synthesis

Claire Scavinner-Dorval, Rodolphe Bailly, Bhushan Borotikar, Sylvain

Brochard, Douraied Ben Salem, François Rousseau

<figure><img src="image_1.png" /></figure>

## To cite this version:

Claire Scavinner-Dorval, Rodolphe Bailly, Bhushan Borotikar, Sylvain Brochard, Douraied Ben Salem, et al.. Analysis of Disentangled Representation Learning for High-Resolution Dynamic MRI Synthesis. SPIE Medical Imaging 2024, Feb 2024, San Deigo, CA, United States. 10.1117/12.3006829. hal-04598556

HAL Id: hal-04598556

https://hal.science/hal-04598556v1

Submitted on 3 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
## Auteur (multicritères)

○ Auteur (multicritères)

○ Auteur : Nom complet

○ Auteur : Nom de famille

○ Auteur : Prénom

○ Auteur : Complément de nom, deuxième prénom

○ Auteur : Organisme payeur

○ Auteur : IdHal (chaîne de caractères)

○ Auteur : Fonction

○ Auteur : personID (entier)

○ Auteur : Identifiant de l'organisme payeur

○ Auteur : Identifiant de la structure

○ Directeur de thèse

○ Éditeur

○ Éditeur scientifique

○ Éditeur de la série

## Ajouter

### Structure

### Structure (multicritères)

○ Structure (multicritères)

○ Structure : Acronyme

○ Structure : Nom

○ Structure : Code

○ Structure : Pays

○ Structure : Type

○ Structure : État dans le référentiel

○ Structure : Identifiant HAL de la structure

○ Structure/équipe de recherche : Pays

○ Structure/regroupement d'équipes : Pays

○ Structure/laboratoire : Pays

○ Structure/regroupement de laboratoires : Pays

○ Structure/institution : Pays

○ Structure/regroupement d'institutions : Pays

## Ajouter

## Autres
# Analysis of Disentangled Representation Learning for High-Resolution Dynamic MRI Synthesis

Claire Scavinner-Dorval<sup>a</sup>, Rodolphe Bailly<sup>b</sup>, Bhushan Borotikar<sup>c</sup>, Sylvain Brochard<sup>d</sup>, Douraied Ben Salem<sup>d</sup>, and François Rousseau<sup>a</sup>

<sup>a</sup>IMT Atlantique, LaTIM U1101 INSERM, Brest, France

<sup>b</sup>Fondation Ildys, LaTIM U1101 INSERM, Brest, France

<sup>c</sup>Symbiosis Centre for Medical Image Analysis, Pune, India

<sup>d</sup>CHRU, UBO, LaTIM U1101 INSERM, Brest, France

## ABSTRACT

Unpaired image synthesis is a particularly active area of research, especially in medical imaging, where paired datasets are rare. Disentangled representations are an important part of the techniques used, following those based on GAN. However, by relying on the factorization of an image into independent variation latent codes, these methods can offer greater control over the synthesis result than GANs. This work investigates the use of disentangled representation learning for high-resolution dynamic MRI synthesis.

**Keywords:** Unpaired image synthesis, disentangled representation learning, dynamic MRI

## 1. INTRODUCTION

Image synthesis is defined as the process of generating an image according to specific characteristics. It covers a wide range of applications in computer vision (style transfer<sup>1,2</sup> super-resolution<sup>3,4</sup> semantic image synthesis<sup>5,6</sup> etc.) and medical imaging (reconstruction,<sup>7</sup> denoising,<sup>8</sup> registration,<sup>9</sup> super-resolution,<sup>10</sup> etc.). However, this task remains complex due to the often ill-posed nature of the problem and the lack of paired datasets. The term "paired" is used to describe datasets where each sample has a corresponding ground truth. The construction of such datasets requires manual annotation or access to the corresponding ground truths, making their number relatively limited. These difficulties are exacerbated in medical imaging, where building such a dataset can be tedious (e.g., manual segmentation by an expert), expensive (multiple acquisitions with one or more imaging systems), or imprecise (rough registration between images from different imaging modalities).

In this context, image synthesis using unpaired datasets mobilizes a growing part of the image processing community. Since their emergence in 2014, Generative Adversarial Networks<sup>11</sup> (GANs) have provided the basis for a significant number of such methods.<sup>12–14</sup> However, this type of approach offers limited control over the synthesis result and can lead to realistic images with anatomical inconsistency. Instead, disentangled representations are formulated on the assumption that it is possible to factorize an input image in a set of meaningful, independent features, each representing a mode of variation within the image, and encode them into distinct dimensions.<sup>15,16</sup> Despite similar application field as GAN, encouraging a representation to be disentangled benefits from a more interpretable generation process, a more controllable synthesis, increase the transferability between tasks and is particularly suitable for privacy aware application such as federated learning applications.<sup>17,18</sup>

In this work, we focus on pediatric dynamic MRI data<sup>19</sup> acquired to study equinus which is the most common musculo-skeletal deformity in children with cerebral palsy. In this context, one high-resolution static image and several low-resolution dynamic images have been acquired to better understand the biomechanics of the pediatric ankle joint. The estimation of high-resolution dynamic sequences using a set of static images is an unpaired image synthesis problem. In this article, we investigate the use of a disentangled representation learning framework to this end.
Champ de recherche par défaut (multicritères)

○ <u>Champ de recherche par défaut (multicritères)</u>

○

○ <u>Revues (multicritères)</u>

○ <u>Revue : Éditeur</u>

○ <u>Revue : Titre abrégé</u>

○ <u>Revue : Titre</u>

○ <u>Revue : Identifiant interne</u>

○ <u>Revue : Date de début de publication</u>

○ <u>Revue : ISSN électronique</u>

○ <u>Revue : ISSN</u>

○ <u>Revue : Éditeur</u>

○ <u>Revue : Couleur dans SHERPA/RoMEO</u>

○ <u>Revue : État dans le référentiel</u>

○

○ <u>Colloque (multicritères)</u>

○ <u>Colloque : Titre</u>

○ <u>Colloque : Organisateur</u>

○ <u>Colloque : date de début (Année)</u>

○ <u>Colloque : date de fin (Année)</u>

○ <u>Conférence invitée (oui/non)</u>

○

○ <u>Projets ANR (multicritères)</u>

○ <u>Projet ANR : Acronyme</u>

○ <u>Projet ANR : Acronyme du programme</u>

○ <u>Projet ANR : Code décision (référence)</u>

○ <u>Projet ANR : Nom</u>

○ <u>Projet ANR : Identifiant interne</u>

○ <u>Projet ANR : État dans le référentiel</u>

○

○ <u>Projets européens (multicritères)</u>

○ <u>Projet européen : Acronyme</u>

○ <u>Projet européen : Identifiant de l'appel à projet</u>

○ <u>Projet européen : Référence</u>

○ <u>Projet européen : Nom</u>

○ <u>Projet européen : Date de fin</u>

○ <u>Projet européen : Financement</u>

○ <u>Projet européen : Date de début</u>

○ <u>Projet européen : État dans le référentiel</u>
## 2. METHODS

### 2.1 Unpaired Image Synthesis Framework

Let $X$ be a set of low-resolution dynamic MRI data and $Y$ be a set of high-resolution static MRI data. Our goal is to estimate the transformation from $X$ to $Y$ to synthesize high-resolution dynamic data from a low-resolution sequence, while preserving its anatomical properties. To this end, we make use of the DRIT++ framework, proposed by Lee et al.²⁰ Unpaired images are decomposed into two latent codes: $z^m$ characterizing the modality and $z^a$ the anatomy. The modality latent code refers to the modality specific attributes, such as the parameters of the MRI scan. The anatomy latent code refers to the modality invariant, spatial information in the image. Each latent code is extracted from an input image by fully convolutional encoders, specific to each latent code and domain (see equation 1). The anatomy encoders are denoted $E_X^a$ and $E_Y^a$, and the modality encoders are denoted $E_X^m$ and $E_Y^m$.

$$ \begin{cases} x = (z_x^a, z_x^m) = (E_X^a(x), E_X^m(x)) \\ y = (z_y^a, z_y^m) = (E_Y^a(y), E_Y^m(y)) \end{cases} \qquad (1) $$

The latent codes are recombined pairwise within the generators to form the new image (see equation 2). There are two generators, one for each modality, denoted $G_X$ and $G_Y$. The synthesis of an image from the source modality to the target modality is achieved by recombining the anatomy latent code of the source image with the modality latent code of an image from the target modality. We denote the images synthesized in the other modality as $x_Y$ and $y_X$.

$$ \begin{cases} x_Y = G_Y(z_x^a, z_y^m) \\ y_X = G_X(z_y^a, z_x^m) \end{cases} \qquad (2) $$

The generated images have no corresponding ground truth in the target modality. Therefore, we apply the inverse transformation to recover the original images (see equation 3).

$$ \begin{cases} \tilde{x} = G_X(z_{x_Y}^a, z_{y_X}^m) \\ \tilde{y} = G_Y(z_{y_X}^a, z_{x_Y}^m) \end{cases} \qquad (3) $$

The general synthesis scheme is shown in Figure 1. In addition, DRIT++ uses three discriminators (not shown in the figure for sake of clarity). The first discriminator $D$ attempts to differentiate the anatomical latent codes extracted from the images according to their original modality. It encourages the anatomical latent code to be modality invariant. The other two discriminators, $D_X$ and $D_Y$, are modality-specific and aim to distinguish real images from synthesized images.

The global cost function is written:

$$ \mathcal{L} = \lambda_{adv}^a \mathcal{L}_{adv}^a + \lambda_{adv}^m \mathcal{L}_{adv}^m + \lambda_{latent} \mathcal{L}_{latent} + \lambda_{self} \mathcal{L}_{self} + \lambda_{cycle} \mathcal{L}_{cycle} \qquad (4) $$

where $\mathcal{L}_{adv}^a$ and $\mathcal{L}_{adv}^m$ are adversarial loss, $\mathcal{L}_{latent}$ a function enforcing an irreversible mapping between image and modality latent space, $\mathcal{L}_{self}$ a self-reconstruction loss and $\mathcal{L}_{cycle}$ a cycle-consistency loss applied on images from both modalities. $\mathcal{L}_{cycle}$, $\mathcal{L}_{adv}^a$ and $\mathcal{L}_{adv}^m$ are illustrated on Figure 1.

### 2.2 Latent space constraints

The use of disentangled representations in an unpaired framework requires the introduction of constraints on latent spaces without any ground truth for the latent codes being extracted. In the original DRIT++ framework,²⁰ a constraint on the content (or anatomy) space is introduced by the $D$ discriminator. The discriminator encourages the content latent codes to be projected into the same space so that they are indistinguishable with respect to the discriminator. On the other hand, the two discriminators $D_X$ and $D_Y$ both operate in image space. The only constraint on the style space is the function $\mathcal{L}_{latent}$, which promotes an invertible transformation between image space and style space.
○ Projet européen : Identifiant interne

○

○ Date de production : année

○ Date de mise en ligne : année

○ Date de publication : année

○ Date d'écriture : année

○ Date de modification du dépôt : année

○ Date de dépôt : année

○ Date de publication électronique : année

○

○ Collection HAL (multicritères)

○ Collection HAL : catégorie

○ Collection HAL : Code

○ Collection HAL : Nom

○ Collection HAL : Identifiant interne

○

○ Identifiant interne du contributeur/déposant

○ Nom complet du contributeur/déposant

○

○ Domaines

○ Domaine primaire

○ Domaine racine

○ Sous-domaine niveau 1

○ Sous-domaine niveau 2

○ Sous-domaine niveau 3

○

○ Statut du document

○ Version du document

○ Type de dépôt

○ Type de document

○ ISBN

○ Numéro - référence

○ Identifiant DOI

○

○ Classification

○ Audience

○ Vulgarisation

○ Comité de lecture - texte (oui ou non)

○ Actes de colloque

○ Référence interne
<figure><img src="image_3.png" /><figcaption>Figure 1. Overview of the synthesis process. Each image $x \in X$ and $y \in Y$ is factorized into anatomy and modality latent codes by $E^a$ and $E^m$. Generators $G$ recombine the latent codes and generate a new image according to the input latent codes. Once the cross-modality synthesis is done, the inverse operation is performed to recover the original images and ensure the cycle consistency.</figcaption></figure>

In this work, we investigate the use of two other constraints on the latent vectors. The first is a contrastive cost function applied in the modality space. The principle of such contrastive loss is to favor the proximity of an anchor (the current sample) and the similar samples (i.e., from the same class, referred to as "positive" examples) in a latent space, while also promoting distance between the anchor and the samples from different classes (called "negative" samples). Since the modalities of each image are known, it is possible to use a supervised function (equation 5), proposed by Khosla et al.²¹

$$ \mathcal{L}_{\text{contrast}} = \sum_{n \in [1,N]} \frac{-1}{|P_n|} \sum_{p \in P_n} \log \frac{\exp(z_n^m \cdot z_p^m / \tau)}{\sum_{a \in A_n} \exp(z_n^m \cdot z_a^m / \tau)} \quad (5) $$

For a set of $N$ samples, let $y_n$ be the class (dynamic or static) associated with a given modality latent code $z_n^m$, $A_n = \{a \in [1, N] | a \neq n\}$ the set of indices distinct from $n$, $P_n = \{p \in A_n | y_p = y_n\}$ the set of positive examples associated with the anchor $z_n^m$, and $\tau \in \mathbb{R}^+$ a scalar temperature parameter. The . symbol stands for the inner dot product.

The second is a cyclic cost function applied in the latent space of anatomy and modality. This cost encourages a one-to-one transformation between the different modalities, similar to a cyclic cost in image space.

$$ \mathcal{L}_{\text{cycle}}^{a} = \|z_{x}^{a} - z_{x_{Y}}^{a}\|_{1} + \|z_{y}^{a} - z_{y_{X}}^{a}\|_{1} \quad (6) $$

$$ \mathcal{L}_{\text{cycle}}^{m} = \|z_{x}^{m} - z_{y_{X}}^{m}\|_{1} + \|z_{y}^{m} - z_{x_{Y}}^{m}\|_{1} \quad (7) $$

## 3. RESULTS AND DISCUSSION

The study, which was approved by the regional ethics committee, involved eleven typically developing children and nine children with cerebral palsy, who ranged in age from 6 to 14 years old. 3D MRI data was collected during a single visit after informed consent forms were signed by the parents, and have been acquired using a 3T MR scanner (Achieva dStream, Philips Medical Systems, Best, Netherlands). The scanning protocol included one high-resolution static 3D scan of the ankle joint with a resolution of 0.26 x 0.26 x 0.8 mm (T1-weighted gradient-echo, flip angle 10°, matrix 576 x 576, FOV 150 mm x 150 mm, TR/TE 7.81/2.75 ms, mean acquisition
○ Financement

○ Collaborations

Ajouter

Lancer la recherche

Recherche experte (SolR)

Recherche experte (SolR)

Lancer la recherche
Vers la recherche avancée

• Déposer

X

X

X

Loading...

X

Le document que vous demandez est en access restreint
jusqu'au 2024-10-02 !

Revenir a la notice

Retourner à l'accueil

[image]

[image]

[image]

[image]

Contact

• Support

Ressources

• Documentation

• FAQ

• API
duration: 424.32 s), and three low-resolution dynamic sequences per child: two passive sequences for repeatability measures and one active sequence, all acquired with knee angle kept at full extension (approximately between 0° and 10°). Each sequence is composed of 15 time frames with a voxel size of 0.57 x 0.57 x 8 mm (flip angle 15°, matrix 352 x 352, FOV 200 mm x 200 mm, TR/TE 18.98 s).

Each method is trained on an unpaired dataset of static and dynamic MRI images of the joint. The disentangled representation learning framework is compared with CycleGAN¹² whose network architecture includes around 22 million parameters. For a fair comparison, and in particular to avoid a bias in the results related to the number of network parameters, we have re-implemented the DRIT++ framework to obtain an architecture with a similar number of parameters to CycleGAN (about 18 million parameters instead of 87 million for the original DRIT++ code). To boost the performance, the Adaptive Instance Normalization (AdaIN) approach²² is used in the generator architecture in this modified implementation of DRIT++ framework, called mDRIT++ hereafter.

Quantitative results are shown in Table 1. We use two state-of-the-art metrics for unpaired image synthesis: Kernel Inception Distance (KID) and Fréchet Inception Distance (FID). FID focuses on the distance between distributions, while KID emphasizes visual similarities. According to these two quantitative metrics, CycleGAN provides the best results, followed by the mDRIT++ with the cyclic constraint on the anatomy latent code. The two other constraints (contrastive loss and cyclic constraint on the modality latent code) do not provide any improvements. Visual evaluation is provided by Figure 2 showing the result of the cross-modality synthesis for CycleGAN and mDRIT++. Overall, as shown also in,²³ we can see that mDRIT++ is not able to reproduce a realistic image (i.e. with a texture similar to the high-resolution data) compared to CycleGAN. On the other hand, the image produced by CycleGAN may not be anatomically correct (modification of the bone structure).

<table><caption>Table 1. Quantitative evaluation using KID and FID. Best performance is indicated in bold, and second best performance in italic.</caption><thead><tr><td>Method</td><td>KID ↓</td><td>FID ↓</td></tr></thead><tbody><tr><td>CycleGAN</td><td><b>0.1035</b></td><td><b>154.81</b></td></tr><tr><td>mDRIT++</td><td>0.1818</td><td>209.64</td></tr><tr><td>mDRIT++ and L<sup>m</sup><sub>cycle</sub></td><td>0.2317</td><td>234.48</td></tr><tr><td>mDRIT++ and L<sup>a</sup><sub>cycle</sub></td><td><b>0.1667</b></td><td><b>198.35</b></td></tr><tr><td>mDRIT++ and L<sup>a</sup><sub>cycle</sub> + L<sup>m</sup><sub>cycle</sub></td><td>0.2042</td><td>219.00</td></tr><tr><td>mDRIT++ and L<sup>a</sup><sub>cycle</sub> + L<sup>m</sup><sub>cycle</sub> + L<sup>contrast</sup></td><td>0.4248</td><td>309.46</td></tr></tbody></table>

# 4. CONCLUSION AND PERSPECTIVES

In this article, we have presented an analysis of a disentangled representation learning framework for high-resolution dynamic MRI synthesis. Experimental results show that adding cyclic constraints on the anatomy latent space is a way to improve synthesis performance of the original DRIT++ framework. The proposed approach provides a correct anatomical reconstruction of the ankle joint, even if it is less rich in texture than a CycleGAN-type approach. The improvement of the reconstruction quality to synthesize a realistic image with the proposed approach is crucial for the future work to be able to compete with an approach like CycleGAN.

# ACKNOWLEDGMENTS

The research leading to these results has received funding from ANR (AI4CHILD ANR-19-CHIA-0015-01), Région Bretagne (DynMRI project), Philips, Fondation de l'Avenir, Paris, France, and Fondation Motrice, Paris, France. This work was granted access to the HPC resources of IDRIS under the allocation 2023-AD010314332 made by GENCI.
• OAI-PMH

• AuréHAL

## Informations

• À propos

• Données personnelles

• Mentions légales

• Accessibilité

• Conformité RGAA

## Questions juridiques

• Je publie, quels sont mes droits ?

• Loi pour une République numérique

• Stratégie de non-cession des droits

• SHERPA/RoMEO

## Portails

• Portails institutionnels

• HAL

• HAL SHS

• HAL Thèses

• MédiHAL

## CCSD

• CCSD

• Episciences

• Sciencesconf
<figure><img src="image_4.png" /><figcaption>Figure 2. High-resolution dynamic sequence synthesis results using CycleGAN and mDRIT++. For each sub-figure, the second row shows a zoom of the highlighted part of the image above. (a) Dynamic image (b) Static image (c) Estimation with CycleGAN (d) Estimation with mDRIT++</figcaption></figure>

DECLARATION OF CONFLICT OF INTEREST

No potential conflict of interest was reported by the authors. The results in this article is not being and have
not been submitted for publication or presentation elsewhere.

REFERENCES

[1] Jiang, L., Zhang, C., Huang, M., Liu, C., Shi, J., and Loy, C. C., "TSIT: A Simple and Versatile Framework for Image-to-Image Translation," in [Computer Vision - ECCV 2020], Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.-M., eds., Lecture Notes in Computer Science, 206-222, Springer International Publishing, Cham (2020).

[2] Gatys, L. A., Ecker, A. S., and Bethge, M., "Image Style Transfer Using Convolutional Neural Networks," in [2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)], 2414-2423, IEEE, Las Vegas, NV, USA (June 2016).

[3] Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W., "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network," 4681-4690 (2017).

[4] Hu, Y., Gao, X., Li, J., Huang, Y., and Wang, H., "Single image super-resolution with multi-scale information cross-fusion network," *Signal Processing* **179**, 107831 (Feb. 2021).

[5] Park, T., Liu, M.-Y., Wang, T.-C., and Zhu, J.-Y., "Semantic Image Synthesis With Spatially-Adaptive Normalization," 2337-2346 (2019).

[6] Tang, H., Xu, D., Yan, Y., Torr, P. H. S., and Sebe, N., "Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation," 7870-7879 (2020).

[7] de Haan, K., Rivenson, Y., Wu, Y., and Ozcan, A., "Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy," *Proceedings of the IEEE* **108**, 30-50 (Jan. 2020). Conference Name: Proceedings of the IEEE.

[8] Chen, J., Wee, L., Dekker, A., and Bermejo, I., "Improving reproducibility and performance of radiomics in low-dose CT using cycle GANs," *Journal of Applied Clinical Medical Physics* **23**(10), e13739 (2022).

[9] Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., and Yang, X., "Deep learning in medical image registration: a review," *Physics in Medicine &amp;$\mathbb{S}$mathsemicolon$ Biology* **65**, 20TR01 (Oct. 2020). Publisher: IOP Publishing.
[10] Sanchez, I. and Vilaplana, V., "Brain MRI super-resolution using 3D generative adversarial networks," (Dec. 2018). arXiv:1812.11440 [cs, stat].

[11] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y., "Generative adversarial networks," *Communications of the ACM* **63**, 139–144 (Oct. 2020).

[12] Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A., "Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks," 2223–2232 (2017).

[13] Park, T., Efros, A. A., Zhang, R., and Zhu, J.-Y., "Contrastive Learning for Unpaired Image-to-Image Translation," *Lecture Notes in Computer Science*, 319–345, Springer International Publishing, Cham (2020).

[14] Taigman, Y., Polyak, A., and Wolf, L., "Unsupervised Cross-Domain Image Generation," (Nov. 2016). arXiv:1611.02200 [cs].

[15] Liu, X., Sanchez, P., Thermos, S., O'Neil, A. Q., and Tsaftaris, S. A., "Learning disentangled representations in the imaging domain," *Medical Image Analysis* **80**, 102516 (Aug. 2022).

[16] Chen, J., Chen, S., Wee, L., Dekker, A., and Bermejo, I., "Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review," *Physics in Medicine & Biology* **68**, 05TR01 (Feb. 2023).

[17] Fрагemann, J., Ardizzone, L., Egger, J., and Kleesiek, J., "Review of Disentanglement Approaches for Medical Applications – Towards Solving the Gordian Knot of Generative Models in Healthcare," (Mar. 2022). arXiv:2203.11132 [cs].

[18] Bercea, C. I., Wiestler, B., Rueckert, D., and Albarqouni, S., "Federated disentangled representation learning for unsupervised brain anomaly detection," *Nature Machine Intelligence* **4**, 685–695 (Aug. 2022). Number: 8 Publisher: Nature Publishing Group.

[19] Makki, K., Borotikar, B., Garetier, M., Brochard, S., Salem, D. B., and Rousseau, F., "In vivo ankle joint kinematics from dynamic magnetic resonance imaging using a registration-based framework," *Journal of biomechanics* **86**, 193–203 (2019).

[20] Lee, H.-Y., Tseng, H.-Y., Mao, Q., Huang, J.-B., Lu, Y.-D., Singh, M., and Yang, M.-H., "DRIT++: Diverse Image-to-Image Translation via Disentangled Representations," *International Journal of Computer Vision* **128**, 2402–2417 (Nov. 2020).

[21] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D., "Supervised Contrastive Learning," in [*Advances in Neural Information Processing Systems*], **33**, 18661–18673, Curran Associates, Inc. (2020).

[22] Huang, X. and Belongie, S., "Arbitrary Style Transfer in Real-Time With Adaptive Instance Normalization," 1501–1510 (2017).

[23] Sasaki, H., Willcocks, C. G., and Breckon, T. P., "UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models," (Apr. 2021). arXiv:2104.05358 [cs, eess].