Peer review is central to the integrity of scientific publishing. At IEEE Transactions on Medical Imaging (TMI), thousands of reviewers and editors work each year to ensure that accepted papers meet our high standards of significance, innovation, evaluation, and reproducibility (SIER) [1]. Yet the rapid growth in submissions, the increasing complexity of papers, and the decreasing availability of reviewers place mounting pressure on the TMI peer review system.

IEEE Transactions on Medical Imaging (TMI) publishes high-quality work that innovates imaging methods and advances medicine, science, and engineering. While artificial intelligence (AI) is currently prominent, the journal's scope extends well beyond AI-based imaging to encompass a full spectrum of imaging methods involving CT, MRI, PET, SPECT, ultrasound, optical, and hybrid systems, image reconstruction and processing (ranging from analytical and iterative algorithms to emerging deep imaging approaches), quantitative imaging and analysis (radiomics, biomarkers, and health analytics), image-guided interventions and therapy, as well as multimodal and multiscale imaging with integration of imaging and nonimaging data.

This article examines how to automate the determination of the coagulant dosage for water treatment plants. Whilst most of the processes for water treatment are automated, determining the coagulant dosage, required for reducing turbidity, depends on well-trained and experienced operators. Based on a time-series data set provided by the Shanghai municipal investment water production company, this article comprehensively surveys existing coagulant prediction methods and utilizes an auto-adjustable and time-consistent model to incorporate the operators' experience. Compared to existing methods, the algorithm introduced in this article produced a better accuracy for predicting the coagulant dosage. Moreover, this article demonstrates that taking seasonal effects into account can approximate operator behavior more accurately. To examine the robustness of the identified models, this article examines the model performance based on water drawn from different locations/sources.

This paper develops readily applicable methods for estimating the intrinsic dimension of multivariate datasets. The proposed methods, which make use of theoretical properties of the empirical distribution functions of (pairwise or pointwise) distances, build on the existing concepts of (i) correlation dimensions and (ii) charting manifolds that are contrasted with (iii) a maximum likelihood technique and (iv) other recently proposed geometric methods including MiND and IDEA. This comparison relies on application studies involving simulated examples, a recorded dataset from a glucose processing facility, as well as several benchmark datasets available from the literature. The performance of the proposed techniques is generally in line with other dimension estimators, specifically noting that the correlation dimension variants perform favorably to the maximum likelihood method in terms of accuracy and computational efficiency.

Low-Dose CT (LDCT) can significantly improve the accuracy of lung cancer diagnosis and thus reduce cancer deaths compared to chest X-ray. The lung cancer risk population is also at high risk of other deadly diseases, for instance, cardiovascular diseases. Therefore, predicting the all-cause mortality risks of this population is of great importance. This paper introduces a knowledge-based analytical method using deep convolutional neural network (CNN) for all-cause mortality prediction. The underlying approach combines structural image features extracted from CNNs, based on LDCT volume at different scales, and clinical knowledge obtained from quantitative measurements, to predict the mortality risk of lung cancer screening subjects. The proposed method is referred as Knowledge-based Analysis of Mortality Prediction Network (KAMP-Net). It constitutes a collaborative framework that utilizes both imaging features and anatomical information, instead of completely relying on automatic feature extraction. Our work demonstrates the feasibility of incorporating quantitative clinical measurements to assist CNNs in all-cause mortality prediction from chest LDCT images. The results of this study confirm that radiologist defined features can complement CNNs in performance improvement. The experiments demonstrate that KAMP-Net can achieve a superior performance when compared to other methods.

This paper introduces a framework to deal with the distribution of descriptive features, which preserves the advantages of the vectorial representation and computational efficiency of histogram-based techniques, and inherits the rigorous theoretical guarantee and competitive performance of metric-based ones. The methods developed under this framework describe the underlying distribution of a set of features as a vectorial feature by utilizing random features. Moreover, the proposed methods asymptotically converge to metric-based methods in terms of the similarity and distance and, depending on a specific kernel function, reduce to histogram-based methods. The experimental results show the benefits of a comparable performance on categorization tasks compared to conventional metric-based methods at a significantly reduced computational cost.

Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.

In[1], please note the updated figure captions for Figures 5, 6, 7, and 8 as follows:

The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses. Stepping towards this goal of incorporating biochemical data into ASD diagnosis, this paper analyzes measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways taken from blood samples of 83 participants with ASD and 76 age-matched neurotypical peers. Fisher Discriminant Analysis enables multivariate classification of the participants as on the spectrum or neurotypical which results in 96.1% of all neurotypical participants being correctly identified as such while still correctly identifying 97.6% of the ASD cohort. Furthermore, kernel partial least squares is used to predict adaptive behavior, as measured by the Vineland Adaptive Behavior Composite score, where measurement of five metabolites of the pathways was sufficient to predict the Vineland score with an R2 of 0.45 after cross-validation. This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis but also that the statistical analysis used here offers tremendous potential for extracting important information from complex biochemical data sets.

Space partitioning trees, which sequentially divide and subdivide a space into disjoint subsets using splitting hyperplanes, play a key role in accelerating the query of samples in the cybernetics and computer vision domains. Associated methods, however, suffer from the curse of dimensionality or stringent assumptions on the data distribution. This paper presents a new concept, termed kernel dimension reduction-tree (KDR-tree), that relies on linear projections computed based on an unsupervised kernel dimension reduction approach. The proposed concept does not rely on any assumption on the data distribution and can capture higher-order statistical information encapsulated within the data. This paper then develops two variants of the KDR-tree concept: 1) to handle residual data [i.e., the residual-based KDR-tree (rKDR-tree) algorithm] and 2) to cope with larger datasets, [i.e., the sampling-based KDR-tree (sKDR-tree) algorithm]. By directly comparing the KDR-tree concept to competitive techniques, involving several benchmark datasets, this paper shows that the sKDR-tree yields a better performance for non-Gaussian distributed datasets. Based on the analysis of three datasets, this paper highlights, experimentally, that the rKDR-tree has the potential to discover the intrinsic dimension. This paper also provides a theoretical analysis about the KDR-tree concept to outline why it outperforms existing techniques if the data distribution is non-Gaussian.

Determining the number of pedestrians from video surveillance has become a very important task in recent years. Available techniques in support of this task include regression-based approaches, which have shown a satisfactory performance in estimating this number from a crowd of pedestrians. However, most of these approaches suffer from treating a surveillance video as a sequence of separate frames, resulting in some temporal information being lost. To address this issue, this paper proposes a semisupervised methodology to extract temporal consistency in a continuous sequence of unlabeled frames. In addition to the temporal consistency, this paper also employs spatial consistency in the sum of pedestrians in subgroups, or subblobs, to determine the total number of pedestrians, or the ground truth. This is effectively achieved by incorporating regularization terms in the objective function to account for temporal and spatial consistencies. The experimental results show that the proposed technique, based on temporal and spatial consistencies, is more robust and can be trained with relatively few labeled frames (e.g., ten frames).

Canonical correlation analysis (CCA) is a well established technique for identifying linear relationships among two variable sets. Kernel CCA (KCCA) is the most notable nonlinear extension but it lacks interpretability and robustness against irrelevant features. The aim of this article is to introduce two nonlinear CCA extensions that rely on the recently proposed Hilbert-Schmidt independence criterion and the centered kernel target alignment. These extensions determine linear projections that provide maximally dependent projected data pairs. The paper demonstrates that the use of linear projections allows removing irrelevant features, whilst extracting combinations of strongly associated features. This is exemplified through a simulation and the analysis of recorded data that are available in the literature.

Most partitioning algorithms iteratively partition a space into cells that contain underlying linear or nonlinear structures using linear partitioning strategies. The compactness of each cell depends on how well the (locally) linear partitioning strategy approximates the intrinsic structure. To partition a compact structure for complex data in a nonlinear context, this paper proposes a nonlinear partition strategy. This is a principal curve tree (PC-tree), which is implemented iteratively. Given that a PC passes through the middle of the data distribution, it allows for partitioning based on the arc length of the PC. To enhance the partitioning of a given space, a residual version of the PC-tree algorithm is developed, denoted here as the principal component analysis tree (PCR-tree) algorithm. Because of its residual property, the PCR-tree can yield the intrinsic dimension of high-dimensional data. Comparisons presented in this paper confirm that the proposed PC-tree and PCR-tree approaches show a better performance than several other competing partitioning algorithms in terms of vector quantization error and nearest neighbor search. The comparison also shows that the proposed algorithms outperform competing linear methods in total average coverage which measures the nonlinear compactness of partitioning algorithms.

The determination of principal curves relies on the arc-length as a global index to describe the middle of the data distribution. With a non-constant data distribution, however, curves that are constructed by the approach introduced in reference may not reflect the middle of data distribution, as demonstrated in this article. This is particularly so for curve segments that have a large curvature and a high data density. To overcome this problem, the paper revisits the projection of the samples onto the curve by incorporating Riemannian distances. This analysis suggests estimating the density value of each sample relative to its neighbors and utilize this value to compute the projection index for the curve. The use of density values, in turn, allows penalizing distances between samples along with the arc-length. In a similar fashion to conventional principal curves algorithms, for example proposed by Hastie and Stuetzle and Tibshirani, the incorporation of Riemannian distances gives rise to an iterative algorithm that includes a projection and a self-consistent step. Application studies to simulated and experimental data sets shows that the proposed modification has the potential to outperform existing algorithms in areas of high curvature under an non-constant data distribution.

This paper presents two new approaches for use in complete process monitoring. The first concerns the identification of nonlinear principal component models. This involves the application of linear principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented. © 2008 Wiley Periodicals, Inc.

This paper revisits the construction of principal curves. Although they have a solid theoretical foundation as a nonlinear extension to principal components, this paper shows that they are difficult to implement in practice if the data distribution is sparse and uneven or if the data contain outliers. These issues may hamper the application of principal curves to an intelligent transportation system. To address these problems, this paper introduces an adaptive constraint K-segment principal curve (ACKPC) algorithm that can be applied in the presence of uneven and sparse distributions, as well as outliers. The benefits of the ACKPC algorithm are as follows: (1) It utilizes predefined endpoints of the curve to reduce the computational effort, and (2) it shows to be less sensitive to parameter settings and outliers. These benefits are demonstrated using two benchmark studies and experimental data from a freeway traffic stream system as well as recorded data from a Global Positioning System (GPS) data from a low-precision GPS receiver.