Longitudinal clustering analysis and prediction of Parkinson\u27s disease progression using radiomics and hybrid machine learning

Background: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson\u27s disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. Methods: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. Results: We identified 3 distinct progression trajectories. Hotelling\u27s t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. Conclusions: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data

A Review on Deep Learning in Medical Image Reconstruction

Medical imaging is crucial in modern clinics to guide the diagnosis and treatment of diseases. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality medical images for clinical usage at the minimal cost and risk to the patients. Mathematical models in medical image reconstruction or, more generally, image restoration in computer vision, have been playing a prominent role. Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed, and we shall call these models handcrafted models. Later, handcrafted plus data-driven modeling started to emerge which still mostly relies on human designs, while part of the model is learned from the observed data. More recently, as more data and computation resources are made available, deep learning based models (or deep models) pushed the data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs. Both handcrafted and data-driven modeling have their own advantages and disadvantages. One of the major research trends in medical imaging is to combine handcrafted modeling with deep modeling so that we can enjoy benefits from both approaches. The major part of this article is to provide a conceptual review of some recent works on deep modeling from the unrolling dynamics viewpoint. This viewpoint stimulates new designs of neural network architectures with inspirations from optimization algorithms and numerical differential equations. Given the popularity of deep modeling, there are still vast remaining challenges in the field, as well as opportunities which we shall discuss at the end of this article.Comment: 31 pages, 6 figures. Survey pape

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

Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

In this thesis, we focus on the image labeling problem which is the task of performing unique pixel-wise label decisions to simplify the image while reducing its redundant information. We build upon a recently introduced geometric approach for data labeling by assignment flows [ APSS17 ] that comprises a smooth dynamical system for data processing on weighted graphs. Hereby we pursue two lines of research that give new application and theoretically-oriented insights on the underlying segmentation task. We demonstrate using the example of Optical Coherence Tomography (OCT), which is the mostly used non-invasive acquisition method of large volumetric scans of human retinal tis- sues, how incorporation of constraints on the geometry of statistical manifold results in a novel purely data driven geometric approach for order-constrained segmentation of volumetric data in any metric space. In particular, making diagnostic analysis for human eye diseases requires decisive information in form of exact measurement of retinal layer thicknesses that has be done for each patient separately resulting in an demanding and time consuming task. To ease the clinical diagnosis we will introduce a fully automated segmentation algorithm that comes up with a high segmentation accuracy and a high level of built-in-parallelism. As opposed to many established retinal layer segmentation methods, we use only local information as input without incorporation of additional global shape priors. Instead, we achieve physiological order of reti- nal cell layers and membranes including a new formulation of ordered pair of distributions in an smoothed energy term. This systematically avoids bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To access the perfor- mance of our approach we compare two different choices of features on a data set of manually annotated 3 D OCT volumes of healthy human retina and evaluate our method against state of the art in automatic retinal layer segmentation as well as to manually annotated ground truth data using different metrics. We generalize the recent work [ SS21 ] on a variational perspective on assignment flows and introduce a novel nonlocal partial difference equation (G-PDE) for labeling metric data on graphs. The G-PDE is derived as nonlocal reparametrization of the assignment flow approach that was introduced in J. Math. Imaging & Vision 58(2), 2017. Due to this parameterization, solving the G-PDE numerically is shown to be equivalent to computing the Riemannian gradient flow with re- spect to a nonconvex potential. We devise an entropy-regularized difference-of-convex-functions (DC) decomposition of this potential and show that the basic geometric Euler scheme for inte- grating the assignment flow is equivalent to solving the G-PDE by an established DC program- ming scheme. Moreover, the viewpoint of geometric integration reveals a basic way to exploit higher-order information of the vector field that drives the assignment flow, in order to devise a novel accelerated DC programming scheme. A detailed convergence analysis of both numerical schemes is provided and illustrated by numerical experiments

Perceptual and Semantic Contributions to Repetition Priming of Environmental Sounds

Repetition of environmental sounds, like their visual counterparts, can facilitate behavior and modulate neural responses, exemplifying plasticity in how auditory objects are represented or accessed. It remains controversial whether such repetition priming/suppression involves solely plasticity based on acoustic features and/or also access to semantic features. To evaluate contributions of physical and semantic features in eliciting repetition-induced plasticity, the present functional magnetic resonance imaging (fMRI) study repeated either identical or different exemplars of the initially presented object; reasoning that identical exemplars share both physical and semantic features, whereas different exemplars share only semantic features. Participants performed a living/man-made categorization task while being scanned at 3T. Repeated stimuli of both types significantly facilitated reaction times versus initial presentations, demonstrating perceptual and semantic repetition priming. There was also repetition suppression of fMRI activity within overlapping temporal, premotor, and prefrontal regions of the auditory "what” pathway. Importantly, the magnitude of suppression effects was equivalent for both physically identical and semantically related exemplars. That the degree of repetition suppression was irrespective of whether or not both perceptual and semantic information was repeated is suggestive of a degree of acoustically independent semantic analysis in how object representations are maintained and retrieve

Sounds in noise: Behavioral and neural studies of illusory continuity and discontinuity

ability to parse an auditory scene into meaningful components varies greatly between individuals; some are able to parse out and write down competing musical pieces while others struggle to understand each word whenever they have to converse in a noisy environment. Using a simple discrimination task, healthy, normally-heari ng adult participants were asked to judge whether a pure tone (with or without amplitude modulation) was continuous or contained a gap. One quarter of the participants consistently heard a gap when none was present, if the tone was accompanied by a higher-frequency noise burst with a lower edge beginning one octave away from the tone (that did not have any energy overlapping the tone). This novel form of informational masking (perceptual interference between components with non-overlapping sound energy) was named 'illusory auditory discontinuity\u2019. The phenomenon appears to reflect natural differences in auditory processing rather than differences in decision-making strategies because: (1) susceptibility to illusory discontinuity correlates with individual differences in auditory streaming (measured using a classical ABA sequential paradigm); and (2) electroencephalographic responses elicited by tones overlaid by short noise bursts (when these sounds are not the focus of attention) are significantly correlated with the occurrence of illusory auditory discontinuity in both an early event-related potential (ERP) component (40-66 ms), and a later ERP component (270-350 ms) after noise onset. Participants prone to illusory discontinuity also tended not to perceive the \u2018auditory continuity illusion\u2019 (in which a tone is heard continuing under a burst of noise centered on the tone frequency that completely masks it) at short noise durations, but reliably perceived the auditory continuity illusion at longer noise durations. These results suggest that a number of attributes describing how individuals differentially parse complex auditory scenes are related to individual differences in two potentially independent attributes of neural processing, reflected here by EEG waveform differences at ~50 msec and ~300 msec after noise onset. Neural correlates of the auditory continuity illusion were also investigated by adjusting masker loudness, so that when listeners were given physically identical stimuli, they correctly detected the gap in a target tone on some trials, while on other trials they reported the tone as continuous (experiencing illusory continuity). High er power of low-frequency EEG activity (in the delta-theta range, <6 Hz) was observed prior to the onset of tones that were subsequently judged as discontinuous, with no other consistent EEG differences found after the onset of tones. These data suggest that the occurrence of the continuity illusion may depend on the brain state that exists immediately before a trial begins

Texture Analysis and Machine Learning to Predict Pulmonary Ventilation from Thoracic Computed Tomography

Chronic obstructive pulmonary disease (COPD) leads to persistent airflow limitation, causing a large burden to patients and the health care system. Thoracic CT provides an opportunity to observe the structural pathophysiology of COPD, whereas hyperpolarized gas MRI provides images of the consequential ventilation heterogeneity. However, hyperpolarized gas MRI is currently limited to research centres, due to the high cost of gas and polarization equipment. Therefore, I developed a pipeline using texture analysis and machine learning methods to create predicted ventilation maps based on non-contrast enhanced, single-volume thoracic CT. In a COPD cohort, predicted ventilation maps were qualitatively and quantitatively related to ground-truth MRI ventilation, and both maps were related to important patient lung function and quality-of-life measures. This study is the first to demonstrate the feasibility of predicting hyperpolarized MRI-based ventilation from single-volume, breath-hold thoracic CT, which has potential to translate pulmonary ventilation information to widely available thoracic CT imaging

Contributions of Continuous Max-Flow Theory to Medical Image Processing

Discrete graph cuts and continuous max-flow theory have created a paradigm shift in many areas of medical image processing. As previous methods limited themselves to analytically solvable optimization problems or guaranteed only local optimizability to increasingly complex and non-convex functionals, current methods based now rely on describing an optimization problem in a series of general yet simple functionals with a global, but non-analytic, solution algorithms. This has been increasingly spurred on by the availability of these general-purpose algorithms in an open-source context. Thus, graph-cuts and max-flow have changed every aspect of medical image processing from reconstruction to enhancement to segmentation and registration. To wax philosophical, continuous max-flow theory in particular has the potential to bring a high degree of mathematical elegance to the field, bridging the conceptual gap between the discrete and continuous domains in which we describe different imaging problems, properties and processes. In Chapter 1, we use the notion of infinitely dense and infinitely densely connected graphs to transfer between the discrete and continuous domains, which has a certain sense of mathematical pedantry to it, but the resulting variational energy equations have a sense of elegance and charm. As any application of the principle of duality, the variational equations have an enigmatic side that can only be decoded with time and patience. The goal of this thesis is to show the contributions of max-flow theory through image enhancement and segmentation, increasing incorporation of topological considerations and increasing the role played by user knowledge and interactivity. These methods will be rigorously grounded in calculus of variations, guaranteeing fuzzy optimality and providing multiple solution approaches to addressing each individual problem

Functional neuroanatomy of spatial sound processing in Alzheimer's disease.

Deficits of auditory scene analysis accompany Alzheimer's disease (AD). However, the functional neuroanatomy of spatial sound processing has not been defined in AD. We addressed this using a "sparse" fMRI virtual auditory spatial paradigm in 14 patients with typical AD in relation to 16 healthy age-matched individuals. Sound stimulus sequences discretely varied perceived spatial location and pitch of the sound source in a factorial design. AD was associated with loss of differentiated cortical profiles of auditory location and pitch processing at the prescribed threshold, and significant group differences were identified for processing auditory spatial variation in posterior cingulate cortex (controls > AD) and the interaction of pitch and spatial variation in posterior insula (AD > controls). These findings build on emerging evidence for altered brain mechanisms of auditory scene analysis and suggest complex dysfunction of network hubs governing the interface of internal milieu and external environment in AD. Auditory spatial processing may be a sensitive probe of this interface and contribute to characterization of brain network failure in AD and other neurodegenerative syndromes