Soumick Chatterjee

Human Technopole · Genomics Research Centre

MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work aims to reconstruct highly under...

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning-based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this pap...

A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning, and magnetic resonance imaging is the principal imaging modality for diagnosi...

CT and MRI are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower r...

Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tunin...

Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-b...

Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data. The reconstruction can be accompanied by the segmentation of the liver - for better visualisation and for generating com...

Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer’s disease. With the adv...

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment fordistinct solid tumour entities. In HT, the tumour tissue is exogenously heated to minimal temperatures of 40 to 41 Cfor 60 minutes. Temperature monitoring can be performed non-invasively using dynamic Magnetic ResonanceImaging (MRI). However,...

Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corre...

Additive manufacturing (AM) is increasingly gaining interest as a low-waste production technique, capable of producing objects using a computer-aided design file. It is particularly interesting for rapid prototyping of parts and manufacturing objects that have complex shapes. However, as in the case of AM through selective laser melting (SLM), manu...

Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-b...

Dynamic MRI is an essential tool for interventions to visualise movements or changes in the target organ. However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution-also known as the spatio-temporal trade-off. Several approaches, including deep learning based super-resolution approaches, have been proposed t...

In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified orig...

Motion artefacts in magnetic resonance images can critically affect diagnosis and the quan-tification of image degradation due to their presence is required. Usually, image quality assessment is carried out by experts such as radiographers, radiologists and researchers. However, subjective evaluation requires time and is strongly dependent on the e...

Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. If the motion artefacts alter a correct delineation of structure and substructures of the brain, lesions, tumours and so on, the patients need to be re-scanned. Otherwise, neuro-ra...

Deep learning models have shown their potential for several applications. However, most of the models are opaque and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some fields, such as medicine, require a high degree of transparency to accept and adopt such technologies. Consequently, creating explainab...

In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified orig...

Many deep learning-based techniques have been proposed in recent years to reconstruct undersampled MRI – showing their potential for shortening the acquisition time. Before using them in actual practice, they are usually evaluated by comparing their results against the available ground-truth – which is not available during real applications. This r...

Deep learning methods are typically trained in a supervised with annotated data for analysing medical images with the motivation of detecting pathologies. In the absence of manually annotated training data, unsupervised anomaly detection can be one of the possible solutions. This work proposes StRegA, an unsupervised anomaly detection pipeline base...

Deep learning pipelines typically require manually annotated training data and the complex reasoning done by such methods make them appear as “black-boxes” to the end-users, leading to reduced trust. Unsupervised or weakly-supervised techniques could be a possible candidate for solving the first issue, while explainable classifiers or applying post...

Cartesian sampling techniques are available to speed up the measurement of dynamic MRI, such as k-t GRAPPA. However, radial samplings, such as iGRASP, are more robust to motion and can be applied for abdominal dynamic MRI. In this work, k-t GRAPPA inspired iGRASP has been created (so called k-t GRASP)–which acquires the subsequent time points by st...

Deep Learning based deformable registration techniques such as Voxelmorph, ICNet, FIRE, do not explicitly encode global dependencies and track large deformations. This research attempts to encode semantics, i.e. structure and overall view of the anatomy in the supplied image, by incorporating self-constructing graph network in the latent space of a...

Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and most commonly in medical imaging. Deep Learning based techniques have been applied successfully to tackle various complex medical image proc...

Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution -...

Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corre...

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this pap...

The trade-off between spatial and temporal resolution in dynamic MRI is a hindrance for MR-guided interventions, which require high temporal resolution while visualizing details. Deep learning based super-resolution (SR) has shown promising results in dealing with this trade-off. Nevertheless, the available temporal information of dynamic MRI has n...

Purpose Quantitative assessment of prospective motion correction (PMC) capability at 7T MRI for compliant healthy subjects to improve high-resolution images in the absence of intentional motion. Methods Twenty-one healthy subjects were imaged at 7 T. They were asked not to move, to consider only unintentional motion. An in-bore optical tracking sy...

A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning and magnetic resonance imaging is the principal imaging modality for diagnosti...

Deep learning algorithms have been used extensively in tackling medical image registration issues. However, these methods have not thoroughly evaluated on datasets representing real clinic scenarios. Hence in this survey, three state-of-the-art methods were compared against the gold standards ANTs and FSL, for performing deformable image registrati...

Dynamic imaging is required during interventions to assess the physiological changes. Unfortunately, while achieving a high temporal resolution the spatial resolution is compromised. To overcome the spatiotemporal trade-off, in this work deep learning based super-resolution approach has been utilized and fine-tuned using prior-knowledge. 3D dynamic...

While commonly used approach for disease localization, we propose an approach to detect anomalies by differentiating them from reliable models of anatomies without pathologies. The method is based on a Variational Auto Encoder to learn the anomaly free distribution of the anatomy and a novel image subtraction approach to obtain pixel-precise segmen...

One of the common problems in MRI is the slow acquisition speed, which can be solved using undersampling. But this might result in image artefacts. Several deep learning based techniques have been proposed to mitigate this problem. Most of these methods work only in the image space. Fine anatomical structures obscured by artefacts in the image can...

In medical image analysis, it is desirable to decipher the black-box nature of Deep Learning models in order to build confidence in clinicians while using such methods. Interpretability techniques can help understand the model’s reasonings, e.g. by showcasing the anatomical areas the network focuses on. While most of the available interpretability...

MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work aims to reconstruct highly under...

Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) in high spatial resolution would play an important role in visualising such fibre tracts in a supe...

Blood vessels of the brain are providing the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as in Alzheimer's disease. Wit...

Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however , can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmen...

Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tunin...

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the perf...

In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods , such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts. Recently, several other methods based on deep learning approaches hav...

In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts. Recently, several other methods based on deep learning approaches have...

Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segment...

Introduction Dynamic MRI suffers from the spatial-temporal resolution trade-off of MRI, making the acquisition of 3D Dynamic MRI a challenging task. Deep Learning has been proven to be a successful tool for performing super-resolution on MRIs. The fast speed of inference of deep learning based models makes them perfect for real-time dynamic MRI for...

Despite continuing efforts in solving the problem of motion artifacts in MRI, subject motion remains one of the major sources of image degradation, in research applications and more importantly, in clinical routine acquisitions. It is fundamental to differentiate between images with an acceptable level of motion corruption and those that cannot be...

Image processing for MRIs can be done in both Spatial and Frequency Domain. Wavelet is one of the frequency domain which can be used for such operations. The use of discrete wavelet transform (DWT) to remove the artifacts in the images are done in compressed sensing, where the image is thresholded iteratively in the wavelet domain, making it a time...

Removing motion artifacts in MR images remains a challenging task. In this work, we employed 2 convolutional neural networks, a conditional generative adversarial network (c-GAN), also known as pix2pix, as well as a network based on the residual network (ResNet) architecture, to remove synthetic motion artifacts for phantom images and T1-w brain im...

In this study, contrast prediction is used as an auxiliary tool to regularize underdetermined image reconstructions. This novel regularization strategy enables to share information across individual reconstructions and outperforms state of the art regularizations for high acceleration factors.

Image segmentation is a process of dividing an image into multiple coherent regions. Segmentation of biomedical images can assist diagnosis and decision making. Manual segmentation is time consuming and requires expert knowledge. One solution is to segment medical images by using deep neural networks, but traditional supervised approaches need a la...

Originating from the initial segment of the middle cerebral artery of the human brain, Lenticulostriate Arteries (LSA) are a collection of perforating vessels that supply blood to the basal ganglia region. With the advancement of 7 Tesla scanner, we are able to detect these LSA which are linked to Small Vessel Diseases(SVD) and potentially a cause...

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) p...

[https://arxiv.org/abs/2001.06535] Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL mod...

With the course of progress in the field of medicine, most of the patients lives can be saved. The only thing required is the proper attention at the proper time. Our wearable solution tries to solve this issue by taking the patients vitals and transmitting them to the server for live monitoring using the mobile app along with the patients current...

Dynamic imaging technique has been employed for MR intervention, offering higher temporal resolution but lower spatial resolution compared with daily clinical images. This work aims to mitigate a trade-off between spatial and temporal resolution by making use of prior data. In order to conduct 4D MRI, first, the respiratory signal was artificially...

Dynamic MRI is a technique of acquiring a series of images continuously to follow the physiological changes over time. However, such fast imaging results in low resolution images. In this work, abdominal deformation model computed from dynamic low resolution images have been applied to high resolution image, acquired previously, to generate dynamic...

One of the major problem of MRI is the slow acquisition speed, which can be increased by subsampling the measured signal data, which in turn leads to image artifacts after reconstruction due to a violation of the Nyquist theorem. In this work, the authors have proposed a deep learning based approach, which can remove undersampling artefacts from bo...