Journal Articles

• Haseljić H, Chatterjee S, Frysch R, Kulvait V, Semshchikov V, Hensen B, Wacker F, Brüsch I, Werncke T, Speck O, Nürnberger A, and Rose G (tba): Liver segmentation using Turbolift Learning for multi-modal perfusion imaging. Manuscript submitted to Computers in Biology and Medicine (under review): arXiv:2207.10167 (1-16). DOI: 10.48550/ARXIV.2207.10167. URL: https://arxiv.org/abs/2207.10167

• Ernst P, Chatterjee S, Rose G, Speck O, and Nürnberger A (tba): Sinogram upsampling using primal-dual UNet for undersampled CT and radial MRI reconstruction. Manuscript submitted to Computer Methods and Programs in Biomedicine (under review): arXiv:2112.13443 (1-20). DOI: 10.48550/ARXIV.2112.13443. URL: https://arxiv.org/abs/2112.13443

• Chatterjee S, Sarasaen C, Rose G, Nürnberger A, and Speck O (tba): DDoS-UNet: Incorporating temporal information using dynamic dual-channel UNet for enhancing super-resolution of dynamic MRI. Manuscript submitted to Medical Image Analysis (under review): arXiv:2202.05355 (1-12). DOI: 10.48550/ARXIV.2202.05355. URL: https://arxiv.org/abs/2202.05355

• Chatterjee S, Saad F, Sarasaen C, Ghosh S, Khatun R, Radeva P, Rose G, Stober S, Speck O, and Nürnberger A (tba): Exploration of interpretability techniques for deep COVID-19 classification using chest X-ray images. Manuscript submitted to Scientific Reports (under review): arXiv:2006.02570 (1-16). DOI: 10.21203/rs.3.rs-1396136/v1; 10.48550/ARXIV.2006.02570. URL: https://www.researchsquare.com/article/rs-1396136/v1

• Chatterjee S, Prabhu K, Pattadkal M, Bortsova G, Sarasaen C, Dubost F, Mattern H, De Bruijne M, Speck O, and Nürnberger A (2022): DS6, deformation-aware semi-supervised learning: Application to small vessel segmentation with noisy training data. Journal of Imaging, 8(10): 259 (1-22). DOI: 10.3390/jimaging8100259. URL: https://www.mdpi.com/2313-433X/8/10/259

• Chatterjee S, Sciarra A, Dünnwald M, Tummala P, Agrawal S K, Jauhari A, Kalra A, Oeltze-Jafra S, Speck O, and Nürnberger A (2022): StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder. Computers in Biology and Medicine, 149: 106093 (1-15). DOI: 10.1016/j.compbiomed.2022.106093. URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482522008010

• Chatterjee S, Nizamani F A, Nürnberger A, and Speck O (2022): Classification of brain tumours in MR images using deep spatiospatial models. Scientific Reports, 12: 1505 (1-11). DOI: 10.1038/s41598-022-05572-6. URL: https://www.nature.com/articles/s41598-022-05572-6

• Chatterjee S, Das A, Mandal C, Mukhopadhyay B, Vipinraj M, Shukla A, Rao R N, Sarasaen C, Speck O, and Nürnberger A (2022): TorchEsegeta: Framework for interpretability and explainability of image-based Deep Learning models. Applied Sciences, 12(4): 1834 (1-20). DOI: 10.3390/app12041834. URL: https://www.mdpi.com/2076-3417/12/4/1834
  

• Chatterjee S, Breitkopf M, Sarasaen C, Yassin H, Rose G, Nürnberger A, and Speck O (2022): ReconResNet: Regularised residual learning for MR image reconstruction of undersampled cartesian and radial data. Computers in Biology and Medicine, 143: 105321 (1-17). DOI: 10.1016/j.compbiomed.2022.105321. URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482522001135

• Sciarra A, Mattern H, Yakupov R, Chatterjee S, Stucht D, Oeltze-Jafra S, Godenschweger F, and Speck O (2021): Quantitative evaluation of prospective motion correction in healthy subjects at 7T MRI. Magnetic Resonance in Medicine, 87(2): 646-57. DOI: 10.1002/mrm.28998. URL: https://onlinelibrary.wiley.com/doi/10.1002/mrm.28998

• Sarasaen C, Chatterjee S, Breitkopf M, Rose G, Nürnberger A, and Speck O (2021): Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge. Artificial Intelligence in Medicine, 121: 102196 (1-11). DOI: 10.1016/j.artmed.2021.102196. URL: https://www.sciencedirect.com/science/article/pii/S0933365721001895

• Emre Kavur A, Sinem Gezer N, Barıș M, Aslan S, Conze P-H, Groza V, Duy Pham D, Chatterjee S, Ernst P, Özkan S, Baydar B, Lachinov D, Han S, Pauli J, Isensee F, Perkonigg M, Sathish R, Rajan R, Sheet D, Dovletov G, Speck O, Nürnberger A, Maier-Hein K H, Bozdağı Akar G, Ünal G, Dicle O, and Selver M A (2020): CHAOS Challenge -- Combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis, 69: 101950 (1-23). DOI: 10.1016/j.media.2020.101950. URL: https://arxiv.org/abs/2001.06535

Thesis

• Chatterjee S (2022): Reducing artefacts in MRI using Deep Learning: Enhancing automatic image processing pipelines. Doctoral dissertation (monography), no. of pages: 385, Faculty of Computer Science (FIN), Otto von Guericke University Magdeburg, published. Date of thesis defense: Sep 5, 2022. DOI: tba. URL: https://lhmdb.gbv.de/DB=1/XMLPRS=N/PPN?PPN=1816945994

Conference Papers

•  …, Chatterjee S et al. (2022): Improvements of highly undersampled MR thermometry for hyperthermia using complex-valued convolutional networks. Paper to be presented at 3rd IEEE International Conference on Human-Machine Systems (ICHMS 2022), Orlando, FL, USA, Nov 17-19, 2022. p. —. under review. DOI: —. URL: https://www.ise.ufl.edu/ichms2022/

• Chatterjee S, Haseljić H, Frysch R, Kulvait V, Semshchikov V, Hensen B, Wacker F, Brüsch I, Werncke T, Speck O, Nürnberger A, and Rose G (2022): Liver segmentation in time-resolved C-arm CT volumes reconstructed from dynamic perfusion scans using time separation technique. Paper to be presented at 5th IEEE International Conference on Image Processing, Applications and Systems (IEEE IPAS 2022), Genova, IT, Dec 5-7, 2022. p. tba. DOI: tba. URL: https://ipas.ieee.tn/
  

• Chatterjee S, Tummala P, Speck O, and Nürnberger A (2022): Complex Network for Complex Problems: A comparative study of CNNs and Complex-valued CNNs. Paper to be presented at 5th IEEE International Conference on Image Processing, Applications and Systems (IEEE IPAS 2022), Genova, IT, Dec 5-7, 2022. p. tba. DOI: tba. URL: https://ipas.ieee.tn/

• Sciarra A, Chatterjee S, Dünnwald M, Placidi G, Nürnberger A, Speck O, and Oeltze-Jafra S (2022): Reference-less SSIM regression for detection and quantification of motion artefacts in brain MRIs. Paper presented at Medical Imaging with Deep Learning (MIDL) 2022, Zürich, Switzerland, Jul 6-8, 2022. p. — (1-3). Oral presentation and poster. DOI: —. URL: https://openreview.net/forum?id=24cqMfboXhH

• Sarasaen C, Chatterjee S, Rose G, Nürnberger A, and Speck O (2022): Motion-robust dynamic abdominal MRI using k-t GRASP and dynamic dual-channel training of super-resolution U-Net (DDoS-UNet). Paper presented at 2022 Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM-ESMRMB 2022), London, UK, May 7-12, 2022. ResearchGATE – Cambridge, MA, USA. p. — (1-4). Oral presentation (online). DOI: —. URL: https://www.researchgate.net/publication/358357562_Motion-robust_dynamic_abdominal_MRI_using_k-t_GRASP_and_dynamic_dual-channel_training_of_super-resolution_U-Net_DDoS-UNet

• Iuso D, Chatterjee S, Heylen R, Cornelissen S, De Beenhouwer J, and Sijbers J (2022): Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing. Paper presented at SPIE Developments in X-Ray Tomography XIV (OP504), San Diego, CA, USA, Aug 21-25, 2022. p. — (ID: 12242-55). Poster. DOI: 10.1117/12.2633318. URL: https://www.spie.org/optics-photonics/presentation/Evaluation-of-deeply-supervised-neural-networks-for-3D-pore-segmentation/12242-55

• Ernst P, Chatterjee S, Rose G, and Nürnberger A (2022): Primal-dual UNet for sparse view cone beam computed tomography volume reconstruction. Paper presented at Medical Imaging with Deep Learning (MIDL) 2022, Zürich, Switzerland, Jul 6-8, 2022. p. arXiv:2205.07866 (1-3). Poster. DOI: 10.48550/ARXIV.2205.07866. URL: https://openreview.net/forum?id=RVKcDeJ2fCi

• Chatterjee S, Sarasaen C, Rose G, Nürnberger A, and Speck O (2022): DDoS-UNet: Incorporating temporal information using dynamic dual-channel UNet for enhancing super-resolution of dynamic MRI. Paper presented at Medical Imaging with Deep Learning (MIDL) 2022, Zürich, Switzerland, Jul 6-8, 2022. p. — (1-3). Oral presentation and poster. DOI: —. URL: https://openreview.net/forum?id=S7S6gPtbKU4

• Nath V, Pizzolato M, Palombo M, Gyori N, Schilling K G, Hansen C, Qi Y, Kanakaraj P, Landman B A, Chatterjee S, Sciarra A, Duennwald M, Oeltze-Jafra S, Nuernberger A, Speck O, Pieciak T, Baranek M, Bartocha K, Ciupek D, Bogusz F, Hamidinekoo A, Afzali M, Lin H, Alexander D C, Lan H, Sepehrband F, Liang Z, Wu T-Y, Su C-W, Wu Q-H, Liu Z-Y, Chao Y-P, Albay E, Unal G, Pylypenko D, Ye X, Zhang F, and Hutter J (2021): Resolving to super resolution multi-dimensional diffusion imaging (Super-MUDI). Paper presented at The 29th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2021), — (online), May 15-20, 2021. p. —. Oral presentation (online). DOI: —. URL: https://www.researchgate.net/publication/349589086_Resolving_to_super_resolution_multi-dimensional_diffusion_imaging_Super-MUDI

• Mitta D, Chatterjee S, Speck O, and Nürnberger A (2021): Upgraded W-net with attention gates and its application in unsupervised 3D liver segmentation. Paper presented at 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2021), -- (online), Feb 4-6, 2021. p. 488-94. Oral presentation (online) and digital poster. DOI: 10.5220/0010221504880494. URL: https://www.scitepress.org/Papers/2021/102215/102215.pdf

• Chatterjee S, Sciarra A, Dünnwald M, Mushunuri R V, Podishetti R, Rao R N, Gopinath G D, Oeltze-Jafra S, Speck O, and Nürnberger A (2021): ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning. Paper presented at 29th European Signal processing Conference 2021 (EUSIPCO2021), Dublin, Ireland (online), Aug 23-27, 2021. p. — (1-5; ID: 6484). Oral presentation (online). DOI: 10.23919/EUSIPCO54536.2021.9615963. URL: https://ieeexplore.ieee.org/document/9615963

• Chatterjee S, Prabhu K, Pattadkal M, Bortsova G, Sarasaen C, Dubost F, Mattern H, De Bruijne M, Speck O, and Nürnberger A (2021): DS6, deformation-aware semi-supervised learning: Application to small vessel segmentation with noisy training data. Paper presented at Medical Imaging with Deep Learning (MIDL) 2021, Lübeck, Germany, Jul 7-9, 2021. p. — (1-3; ID: 109). Oral presentation (online). DOI: —. URL: https://openreview.net/forum?id=2t0_AxD1otB

• Chatterjee S, Breitkopf M, Sarasaen C, Yassin H, Rose G, Nürnberger A, and Speck O (2021): ReconResNet: Regularised residual learning for MR image reconstruction of undersampled cartesian and radial data. Paper presented at Medical Imaging with Deep Learning (MIDL) 2021, Lübeck, Germany, Jul 7-9, 2021. p. — (1-3; ID: 110). Oral presentation (online). DOI: —. URL: https://openreview.net/forum?id=KNEKu-W4Avz

• Chatterjee S, Sciarra A, Dünnwald M, Oeltze-Jafra S, Nürnberger A, and Speck O (2020): Retrospective motion correction of MR Images using prior-assisted Deep Learning. Paper presented at Medical Imaging Meets NeurIPS 2020 (34th Conference on Neural Information Processing Systems), Vancouver, Canada (online), Dec 12, 2020. p. — (1-5). Oral presentation (online). DOI: —. URL: http://www.cse.cuhk.edu.hk/~qdou/public/medneurips2020/75_MoCo_MedNeurIPS_reduced.pdf

• Sarasaen C, Chatterjee S, Breitkopf M, Iuso D, Rose G, and Speck O (2019): Breathing deformation model — application to multi-resolution abdominal MRI. Paper presented at 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019), Berlin, Germany, Jul 23-27, 2019. IEEE. p. 2769-72. Oral presentation and poster. DOI: 10.1109/EMBC.2019.8857706. URL: https://ieeexplore.ieee.org/document/8857706

• Ernst P, Chatterjee S, Speck O, and Nürnberger A (2019): CHAOS Challenge - Team OvGU MEMoRIAL. Paper presented at Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) Challenge held in the IEEE International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, Apr 11, 2019. ResearchGATE – Cambridge, MA, USA. p. — (1-3). DOI: 10.13140/RG.2.2.27960.49928. URL: https://www.researchgate.net/publication/337948600_CHAOS_Challenge_-_Team_OvGU_MEMoRIAL

Further Contributions (Abstracts, Talks, Posters)

• Ernst P, Chatterjee S, Rose G, Speck O, and Nürnberger A (2022): Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction. Abstract presented at 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI 2022), Kolkata, India, Mar 28-31, 2022. p. — (ID: 896). Poster. URL: https://rc.signalprocessingsociety.org/conferences/isbi-2022/SPSISBI22VID0066.html?source=IBP

• Chatterjee S, Yassin H, Dubost F, Nürnberger A, and Speck O (2022): Learning to segment brain tumours using an explainable classifier. Abstract presented at 2022 Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM-ESMRMB 2022), London, UK, May 7-12, 2022. p. — (1-3; ID: 5592). Oral presentation. URL: https://www.researchgate.net/publication/358357555_Learning_to_segment_brain_tumours_using_an_explainable_classifier 

• Chatterjee S, Sciarra A, Dünnwald M, Tummala P, Agrawal S K, Jauhari A, Kalra A, Oeltze-Jafra S, Speck O, and Nürnberger A (2022): StRegA: Unsupervised anomaly detection in brain MRIs using compact context-encoding variational autoencoder. Abstract presented at 2022 Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM-ESMRMB 2022), London, UK, May 7-12, 2022. p. — (1-3;  ID: 5531). Oral presentation. URL: https://www.researchgate.net/publication/358357400_StRegA_Unsupervised_Anomaly_Detection_in_Brain_MRIs_using_Compact_Context-encoding_Variational_Autoencoder

• Chatterjee S, Sciarra A, Dünnwald M, Ashoka A B T, Vasudeva M G C, Saravanan S, Sambandham V T, Oeltze-Jafra S, Speck O, and Nürnberger A (2022): Uncertainty quantification for ground-truth free evaluation of deep learning reconstructions. Abstract presented at 2022 Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM-ESMRMB 2022), London, UK, May 7-12, 2022. p. — (1-3; ID: 5631). Poster. URL: https://www.researchgate.net/publication/358357558_Uncertainty_quantification_for_ground-truth_free_evaluation_of_deep_learning_reconstructions

• Chatterjee S, Nürnberger A, and Speck O (2022): Reducing artefacts in MRI using Deep Learning. Abstract presented at MEC-Lab Seminar, Technische Universität (TU) Darmstadt, Germany, Apr-19, 2022. p. —. Invited talk / oral presentation (online).

• Chatterjee S, Bajaj H, Siddiquee I H, Nandish B S, Simon S, Shashidhar S B, Speck O, and Nürnberger A (2022): Multi-scale UNet with self-constructing graph latent for deformable image registration. Abstract presented at 2022 Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM-ESMRMB 2022), London, UK, May 7-12, 2022. p. — (1-4; ID: 5563). Oral presentation. URL: https://www.researchgate.net/publication/358357668_Multi-scale_UNet_with_Self-Constructing_Graph_Latent_for_Deformable_Image_Registration

• Sarasaen C, Chatterjee S, Saad F, Breitkopf M, Rose G, Nürnberger A, and Speck O (2021): Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge. Abstract presented at The 29th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2021), — (online), May 15-20, 2021. p. — (1-3; ID: 1778). Oral presentation (online). URL: https://www.researchgate.net/publication/349588965_Fine-tuning_deep_learning_model_parameters_for_improved_super-resolution_of_dynamic_MRI_with_prior-knowledge

• Sarasaen C, Chatterjee S, Nürnberger A, and Speck O (2021): DDoS: Dynamic dual-channel U-Net for improving deep learning based super-resolution of abdominal dynamic MRI. Abstract presented at ESMRMB 2021 Online — 38th Annual Scientific Meeting, Oct 7-9, 2021. p. S44. Oral presentation (online; ID: S06.O3). DOI: 10.1007/s10334-021-00947-8. URL: https://link.springer.com/article/10.1007/s10334-021-00947-8

• Chatterjee S, Sciarra A, Dünnwald M, Agrawal S K, Tummala P, Setlur D, Kalra A, Jauhari A, Oeltze-Jafra S, Speck O, and Nürnberger A (2021): Unsupervised reconstruction based anomaly detection using a Variational Auto Encoder. Abstract presented at The 29th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2021), — (online), May 15-20, 2021. p. — (1-2; ID: 2399). Oral presentation (online). URL: https://www.researchgate.net/publication/349589052_Unsupervised_reconstruction_based_anomaly_detection_using_a_Variational_Auto_Encoder

• Chatterjee S, Sarasaen C, Sciarra A, Breitkopf M, Oeltze-Jafra S, Nürnberger A, and Speck O (2021): Going beyond the image space: undersampled MRI reconstruction directly in the k-space using a complex valued residual neural network. Abstract presented at The 29th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2021), — (online), May 15-20, 2021. p. — (1-4; ID: 1757). Digital poster. DOI: —. URL: https://www.researchgate.net/publication/349589092_Going_beyond_the_image_space_undersampled_MRI_reconstruction_directly_in_the_k-space_using_a_complex_valued_residual_neural_network

• Chatterjee S, Prabhu K, Pattadkal M, Bortsova G, Sarasaen C, Dubost F, Mattern H, De Bruijne M, Speck O, and Nürnberger A (2021): DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data. Abstract presented at Stanford University / Contrastive & SS Learning Group, Stanford, CA, USA, Mar 12, 2021. p. —. Invited talk / oral presentation (online). URL: https://youtu.be/p1RrvlMMhOI

• Chatterjee S, Das A, Mandal C, Mukhopadhyay B, Vipinraj M, Shukla A, Speck O, and Nürnberger A (2021): Interpretability techniques for deep learning based segmentation models. Abstract presented at The 29th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2021), — (online), May 15-20, 2021. p. — (1-4; ID: 2400). Oral presentation (online). URL: https://www.researchgate.net/publication/349589153_Interpretability_Techniques_for_Deep_Learning_based_Segmentation_Models

• Chatterjee S, Bajaj H, Shashidhar S B, Indushekar S B, Simon S, Siddiquee I H, Nandish B S, Speck O, and Nürnberger A (2021): A comparative study of deep learning based deformable image registration techniques. Abstract presented at The 29th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2021), — (online), May 15-20, 2021. p. — (1-3; ID: 2436). Oral presentation (online). URL: https://www.researchgate.net/publication/349588959_A_Comparative_Study_of_Deep_Learning_Based_Deformable_Image_Registration_Techniques

• Chatterjee S (2021): Automatic Vessel Segmentation: Part 2 — DL-method: DS6, Deformation-aware semi-supervised learning: Application to small vessel Ssegmentation with noisy training data. Abstract presented at Center for Behavioral Brain Sciences (CBBS) / Vascular Research Group (VARG), Magdeburg, Germany, Jul 28, 2021. p. —. Invited talk / oral presentation (online).

• Chatterjee S (2021): Automatic Vessel Segmentation: Part 1 — A gentle introduction to vessel segmentation and Deep Learning. Abstract presented at Center for Behavioral Brain Sciences (CBBS) / Vascular Research Group (VARG), Magdeburg, Germany, Jun 30, 2021. p. —. Invited talk / oral presentation (online).

• Sciarra A, Dünnwald M, Chatterjee S, Speck O, and Oeltze-Jafra S (2020): Classification of motion corrupted brain MR images using Deep Learning techniques. Abstract presented at ESMRMB 2020 Online - 37th Annual Scientific Meeting, — (online), Sep 30-Oct 2, 2020. p. S33-34. Oral presentation (online; ID: S03.09). DOI: 10.1007/s10334-020-00874-0. URL: https://link.springer.com/article/10.1007/s10334-020-00874-0

• Sciarra A, Chatterjee S, Dünnwald M, Speck O, and Oeltze-Jafra S (2020): Evaluation of Deep Learning techniques for motion artifacts removal. Abstract presented at ISMRM & SMRT Virtual Conference & Exhibition 2020, — (online), Aug 8-14, 2020. p. — (1-3; ID: 3370). Digital poster. URL: https://www.researchgate.net/publication/343223853_Evaluation_of_Deep_Learning_Techniques_for_Motion_Artifacts_Removal

• Sarasaen C, Chatterjee S, Nürnberger A, and Speck O (2020): Super resolution of dynamic MRI using deep learning, enhanced by prior-knowledge. Abstract presented at ESMRMB 2020 Online - 37th Annual Scientific Meeting, Sep 30-Oct 2, 2020. p. S28-29. Oral presentation (online; ID: S03.04). DOI: 10.1007/s10334-020-00874-0. URL: https://link.springer.com/article/10.1007/s10334-020-00874-0

• Mitta D, Chatterjee S, Speck O, and Nürnberger A (2020): Unsupervised learning for abdominal MRI segmentation using 3D attention W-Net. Abstract presented at ISMRM & SMRT Virtual Conference & Exhibition 2020, — (online), Aug 8-14, 2020. p. — (1-2; ID: 3556). Oral presentation (online). URL: https://www.researchgate.net/publication/343635761_Unsupervised_learning_for_Abdominal_MRI_Segmentation_using_3D_Attention_W-Net

• Mattern H, Sciarra A, Dünnwald M, Chatterjee S, Müller U, Oeltze-Jafra S, and Speck O (2020): Contrast prediction-based regularization for iterative reconstructions (PROSIT). Abstract presented at ISMRM & SMRT Virtual Conference & Exhibition 2020, — (online), Aug 8-14, 2020. p. — (1-3; ID: 3462). Digital poster. URL: https://www.researchgate.net/publication/343223914_Contrast_prediction-based_regularization_for_iterative_reconstructions_PROSIT

• Chatterjee S, Putti P, Nürnberger A, and Speck O (2020): Wavelet filtering of undersampled MRI using trainable wavelets and CNN. Abstract presented at ESMRMB 2020 Online - 37th Annual Scientific Meeting, — (online), Sep 30-Oct 2, 2020. p. S162-63. Oral presentation (online) and digital poster (ID: L01.106). DOI: 10.1007/s10334-020-00876-y. URL: https://link.springer.com/article/10.1007/s10334-020-00876-y

• Sarasaen C, Chatterjee S, Breitkopf M, Rose G, and Speck O (2019): Generating breathing deformation model from low resolution 4D MRI. Abstract presented at Recent progress and developments: 4th Conference on Image-Guided Interventions & Digitalization in Medicine (IGIC 2019), Mannheim, Germany, Nov 4-5, 2019. p. —. Poster. URL: http://www.igic.de/upload/IGIC-2019/Programm_engl_20191024.pdf

• Sarasaen C, Chatterjee S, Breitkopf M, Rose G, and Speck O (2019): Konzeptstudie eines interventionellen Computertomographen. Abstract presented at Recent progress and developments: 4th Conference on Image-Guided Interventions & Digitalization in Medicine (IGIC 2019), Mannheim, Germany, Nov 4-5, 2019. p. —. Poster. URL: http://www.igic.de/deutsch/igic-2019/willkommen-2019/index.html

• Chatterjee S, Breitkopf M, Sarasaen C, Rose G, Nürnberger A, and Speck O (2019): Comparison between the usage of same and different variable density undersampling patterns for Deep Learning based MRI reconstruction. Abstract presented at Recent progress and developments: 4th Conference on Image-Guided Interventions & Digitalization in Medicine (IGIC 2019), Mannheim, Germany, Nov 4-5, 2019. p. —. Oral presentation. URL: https://www.researchgate.net/publication/350325057_Comparison_between_the_usage_of_same_and_different_variable_density_undersampling_patterns_for_Deep_Learning_based_MRI_Reconstruction

• Chatterjee S, Breitkopf M, Sarasaen C, Rose G, Nürnberger A, and Speck O (2019): A deep learning approach for reconstruction of undersampled cartesian and radial data. Abstract presented at The 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB 2019), Rotterdam, Netherlands, Oct 3-5, 2019. p. — (1-4). Oral presentation. URL: https://www.researchgate.net/publication/335473585_A_deep_learning_approach_for_reconstruction_of_undersampled_Cartesian_and_Radial_data