Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs

Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance. Currently, MR motion correction is carried out either prospectively, with the help of motion tracking systems, or retrospectively by mainly utilizing computationally expensive iterative algorithms. In this paper, we utilize a new adversarial framework, titled MedGAN, for the joint retrospective correction of rigid and non-rigid motion artifacts in different body regions and without the need for a reference image. MedGAN utilizes a unique combination of non-adversarial losses and a new generator architecture to capture the textures and fine-detailed structures of the desired artifact-free MR images. Quantitative and qualitative comparisons with other adversarial techniques have illustrated the proposed model performance.Comment: 5 pages, 2 figures, under review for the IEEE International Symposium for Biomedical Image

Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma

Purpose: Several methods have been proposed for the segmentation of 18F-FDG uptake in PET. In this study, we assessed the performance of four categories of 18F-FDG PET image segmentation techniques in pharyngolaryngeal squamous cell carcinoma using clinical studies where the surgical specimen served as the benchmark. Methods: Nine PET image segmentation techniques were compared including: five thresholding methods; the level set technique (active contour); the stochastic expectation-maximization approach; fuzzy clustering-based segmentation (FCM); and a variant of FCM, the spatial wavelet-based algorithm (FCM-SW) which incorporates spatial information during the segmentation process, thus allowing the handling of uptake in heterogeneous lesions. These algorithms were evaluated using clinical studies in which the segmentation results were compared to the 3-D biological tumour volume (BTV) defined by histology in PET images of seven patients with T3-T4 laryngeal squamous cell carcinoma who underwent a total laryngectomy. The macroscopic tumour specimens were collected "en bloc”, frozen and cut into 1.7- to 2-mm thick slices, then digitized for use as reference. Results: The clinical results suggested that four of the thresholding methods and expectation-maximization overestimated the average tumour volume, while a contrast-oriented thresholding method, the level set technique and the FCM-SW algorithm underestimated it, with the FCM-SW algorithm providing relatively the highest accuracy in terms of volume determination (−5.9 ± 11.9%) and overlap index. The mean overlap index varied between 0.27 and 0.54 for the different image segmentation techniques. The FCM-SW segmentation technique showed the best compromise in terms of 3-D overlap index and statistical analysis results with values of 0.54 (0.26-0.72) for the overlap index. Conclusion: The BTVs delineated using the FCM-SW segmentation technique were seemingly the most accurate and approximated closely the 3-D BTVs defined using the surgical specimens. Adaptive thresholding techniques need to be calibrated for each PET scanner and acquisition/processing protocol, and should not be used without optimizatio

Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma

Several methods have been proposed for the segmentation of ¹⁸F-FDG uptake in PET. In this study, we assessed the performance of four categories of ¹⁸F-FDG PET image segmentation techniques in pharyngolaryngeal squamous cell carcinoma using clinical studies where the surgical specimen served as the benchmark

해부학적 유도 PET 재구성: 매끄럽지 않은 사전 함수부터 딥러닝 접근까지

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2021. 2. 이재성.Advances in simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) technology have led to an active investigation of the anatomy-guided regularized PET image reconstruction algorithm based on MR images. Among the various priors proposed for anatomy-guided regularized PET image reconstruction, Bowsher’s method based on second-order smoothing priors sometimes suffers from over-smoothing of detailed structures. Therefore, in this study, we propose a Bowsher prior based on the l1 norm and an iteratively reweighting scheme to overcome the limitation of the original Bowsher method. In addition, we have derived a closed solution for iterative image reconstruction based on this non-smooth prior. A comparison study between the original l2 and proposed l1 Bowsher priors were conducted using computer simulation and real human data. In the simulation and real data application, small lesions with abnormal PET uptake were better detected by the proposed l1 Bowsher prior methods than the original Bowsher prior. The original l2 Bowsher leads to a decreased PET intensity in small lesions when there is no clear separation between the lesions and surrounding tissue in the anatomical prior. However, the proposed l1 Bowsher prior methods showed better contrast between the tumors and surrounding tissues owing to the intrinsic edge-preserving property of the prior which is attributed to the sparseness induced by l1 norm, especially in the iterative reweighting scheme. Besides, the proposed methods demonstrated lower bias and less hyper-parameter dependency on PET intensity estimation in the regions with matched anatomical boundaries in PET and MRI. Moreover, based on the formulation of l1 Bowsher prior, the unrolled network containing the conventional maximum-likelihood expectation-maximization (ML-EM) module was also proposed. The convolutional layers successfully learned the distribution of anatomically-guided PET images and the EM module corrected the intermediate outputs by comparing them with sinograms. The proposed unrolled network showed better performance than ordinary U-Net, where the regional uptake is less biased and deviated. Therefore, these methods will help improve the PET image quality based on the anatomical side information.양전자방출단층촬영 / 자기공명영상 (PET/MRI) 동시 획득 기술의 발전으로 MR 영상을 기반으로 한 해부학적 사전 함수로 정규화 된 PET 영상 재구성 알고리즘에 대한 심도있는 평가가 이루어졌다. 해부학 기반으로 정규화 된 PET 이미지 재구성을 위해 제안 된 다양한 사전 중 2차 평활화 사전함수에 기반한 Bowsher의 방법은 때때로 세부 구조의 과도한 평활화로 어려움을 겪는다. 따라서 본 연구에서는 원래 Bowsher 방법의 한계를 극복하기 위해 l1 norm에 기반한 Bowsher 사전 함수와 반복적인 재가중치 기법을 제안한다. 또한, 우리는 이 매끄럽지 않은 사전 함수를 이용한 반복적 이미지 재구성에 대해 닫힌 해를 도출했다. 원래 l2와 제안 된 l1 Bowsher 사전 함수 간의 비교 연구는 컴퓨터 시뮬레이션과 실제 데이터를 사용하여 수행되었다. 시뮬레이션 및 실제 데이터에서 비정상적인 PET 흡수를 가진 작은 병변은 원래 Bowsher 이전보다 제안 된 l1 Bowsher 사전 방법으로 더 잘 감지되었다. 원래의 l2 Bowsher는 해부학적 영상에서 병변과 주변 조직 사이에 명확한 분리가 없을 때 작은 병변에서의 PET 강도를 감소시킨다. 그러나 제안 된 l1 Bowsher 사전 방법은 특히 반복적 재가중치 기법에서 l1 노름에 의해 유도된 희소성에 기인한 특성으로 인해 종양과 주변 조직 사이에 더 나은 대비를 보여주었다. 또한 제안된 방법은 PET과 MRI의 해부학적 경계가 일치하는 영역에서 PET 강도 추정에 대한 편향이 더 낮고 하이퍼 파라미터 종속성이 적음을 보여주었다. 또한, l1Bowsher 사전 함수의 닫힌 해를 기반으로 기존의 ML-EM (maximum-likelihood expectation-maximization) 모듈을 포함하는 펼쳐진 네트워크도 제안되었다. 컨볼루션 레이어는 해부학적으로 유도 재구성된 PET 이미지의 분포를 성공적으로 학습했으며, EM 모듈은 중간 출력들을 사이노그램과 비교하여 결과 이미지가 잘 들어맞게 수정했다. 제안된 펼쳐진 네트워크는 지역의 흡수선량이 덜 편향되고 편차가 적어, 일반 U-Net보다 더 나은 성능을 보여주었다. 따라서 이러한 방법들은 해부학적 정보를 기반으로 PET 이미지 품질을 향상시키는 데 유용할 것이다.Chapter 1. Introduction 1 1.1. Backgrounds 1 1.1.1. Positron Emission Tomography 1 1.1.2. Maximum a Posterior Reconstruction 1 1.1.3. Anatomical Prior 2 1.1.4. Proposed l_1 Bowsher Prior 3 1.1.5. Deep Learning for MR-less Application 4 1.2. Purpose of the Research 4 Chapter 2. Anatomically-guided PET Reconstruction Using Bowsher Prior 6 2.1. Backgrounds 6 2.1.1. PET Data Model 6 2.1.2. Original Bowsher Prior 7 2.2. Methods and Materials 8 2.2.1. Proposed l_1 Bowsher Prior 8 2.2.2. Iterative Reweighting 13 2.2.3. Computer Simulations 15 2.2.4. Human Data 16 2.2.5. Image Analysis 17 2.3. Results 19 2.3.1. Simulation with Brain Phantom 19 2.3.2.Human Data 20 2.4. Discussions 25 Chapter 3. Deep Learning Approach for Anatomically-guided PET Reconstruction 31 3.1. Backgrounds 31 3.2. Methods and Materials 33 3.2.1. Douglas-Rachford Splitting 33 3.2.2. Network Architecture 34 3.2.3. Dataset and Training Details 35 3.2.4. Image Analysis 36 3.3. Results 37 3.4. Discussions 38 Chapter 4. Conclusions 40 Bibliography 41 Abstract in Korean (국문 초록) 52Docto

Generative Models for Preprocessing of Hospital Brain Scans

I will in this thesis present novel computational methods for processing routine clinical brain scans. Such scans were originally acquired for qualitative assessment by trained radiologists, and present a number of difficulties for computational models, such as those within common neuroimaging analysis software. The overarching objective of this work is to enable efficient and fully automated analysis of large neuroimaging datasets, of the type currently present in many hospitals worldwide. The methods presented are based on probabilistic, generative models of the observed imaging data, and therefore rely on informative priors and realistic forward models. The first part of the thesis will present a model for image quality improvement, whose key component is a novel prior for multimodal datasets. I will demonstrate its effectiveness for super-resolving thick-sliced clinical MR scans and for denoising CT images and MR-based, multi-parametric mapping acquisitions. I will then show how the same prior can be used for within-subject, intermodal image registration, for more robustly registering large numbers of clinical scans. The second part of the thesis focusses on improved, automatic segmentation and spatial normalisation of routine clinical brain scans. I propose two extensions to a widely used segmentation technique. First, a method for this model to handle missing data, which allows me to predict entirely missing modalities from one, or a few, MR contrasts. Second, a principled way of combining the strengths of probabilistic, generative models with the unprecedented discriminative capability of deep learning. By introducing a convolutional neural network as a Markov random field prior, I can model nonlinear class interactions and learn these using backpropagation. I show that this model is robust to sequence and scanner variability. Finally, I show examples of fitting a population-level, generative model to various neuroimaging data, which can model, e.g., CT scans with haemorrhagic lesions

PET Reconstruction With an Anatomical MRI Prior Using Parallel Level Sets.

The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) offers unique possibilities. In this paper we aim to exploit the high spatial resolution of MRI to enhance the reconstruction of simultaneously acquired PET data. We propose a new prior to incorporate structural side information into a maximum a posteriori reconstruction. The new prior combines the strengths of previously proposed priors for the same problem: it is very efficient in guiding the reconstruction at edges available from the side information and it reduces locally to edge-preserving total variation in the degenerate case when no structural information is available. In addition, this prior is segmentation-free, convex and no a priori assumptions are made on the correlation of edge directions of the PET and MRI images. We present results for a simulated brain phantom and for real data acquired by the Siemens Biograph mMR for a hardware phantom and a clinical scan. The results from simulations show that the new prior has a better trade-off between enhancing common anatomical boundaries and preserving unique features than several other priors. Moreover, it has a better mean absolute bias-to-mean standard deviation trade-off and yields reconstructions with superior relative l2-error and structural similarity index. These findings are underpinned by the real data results from a hardware phantom and a clinical patient confirming that the new prior is capable of promoting well-defined anatomical boundaries.This research was funded by the EPSRC (EP/K005278/1) and EP/H046410/1 and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. M.J.E was supported by an IMPACT studentship funded jointly by Siemens and the UCL Faculty of Engineering Sciences. K.T. and D.A. are partially supported by the EPSRC grant EP/M022587/1.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TMI.2016.254960

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Positron emission tomography (PET) images are widely used in many clinical applications such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both standard-dose and low-dose PET data into a common space and then performing patch based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level Canonical Correlation Analysis (mCCA) scheme to solve this problem. Specifically, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value