PET is widely considered the most sensitive technique available for noninvasively studying physiology, metabolism, and molecular pathways in the living human being. However, the utility of PET, being a photon-deficient modality, remains constrained by factors including low signal-to-noise ratio, long imaging times, and concerns about radiation dose. Two developments offer the potential to dramatically increase the effective sensitivity of PET. First by increasing the geometric coverage to encompass the entire body, sensitivity can be increased by a factor of about 40 for total-body imaging or a factor of about 4-5 for imaging a single organ such as the brain or heart. The world's first total-body PET/CT scanner is currently under construction to demonstrate how this step change in sensitivity affects the way PET is used both in clinical research and in patient care. Second, there is the future prospect of significant improvements in timing resolution that could lead to further effective sensitivity gains. When combined with total-body PET, this could produce overall sensitivity gains of more than 2 orders of magnitude compared with existing state-of-the-art systems. In this article, we discuss the benefits of increasing body coverage, describe our efforts to develop a first-generation total-body PET/CT scanner, discuss selected application areas for total-body PET, and project the impact of further improvements in time-of-flight PET.

https://jnm.snmjournals.org/content/jnumed/early/2017/09/21/jnumed.116.184028.full.pdf

Total-Body PET: Maximizing Sensitivity to Create New Opportunities for Clinical Research and Patient Care.

The use of radiopharmaceuticals and the imaging of their biodistribution and kinetics with modern instrumentation are key components to successful developments in PET. Clever design and synthesis of sensitive and specific radiopharmaceuticals is the necessary first step. Each tracer must be targeted to measure a physiological parameter of interest such as blood flow, metabolism, receptor content, etc., in one or more organs or regions. State-of-the-art PET instrumentation produces high-quality 3-dimensional images after injection of tracer into a patient, normal volunteer, or research animal. With an appropriate reconstruction algorithm and with proper corrections for the physical effects such as attenuation and scatter, quantitatively accurate measurements of regional radioactivity concentration can be obtained. These images of tracer distribution can be usefully applied to answer clinical and scientific questions. With the additional use of tracer kinetic modeling techniques, however, there is the potential for a substantial improvement in the kind and quality of information that can be extracted from these biological data. The purpose of a mathematical model is to define the relationship between the measurable data and the physiological parameters that affect the uptake and metabolism of the tracer. In this chapter, the concepts of mathematical modeling as applied to PET are presented. Many of these concepts can be applied to radioactivity measurements from small animals made by tissue sampling or quantitative autoradiography. The primary focus in this chapter will be on methods applicable to data that can be acquired with PET imaging technology. The advantages and disadvantages of various modeling approaches are presented. Then, classes of models are introduced, followed by a detailed description of compartment modeling and of the process of model development and application. Finally, the factors to be considered in choosing and using various model-based methods are presented.

Tracer Kinetic Modeling in PET

Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ~15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole-body PET acquisition protocol of ~45 min total length is presented, composed of (i) an initial 6 min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (six passes × seven bed positions, each scanned for 45 s). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of ten different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ~35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging.

http://iopscience.iop.org/article/10.1088/0031-9155/58/20/7391/pdf

Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application

The Mathematics of Financial Derivatives: Preface

In this article, we discuss dynamic whole-body (DWB) positron emission tomography (PET) as an imaging tool with significant clinical potential, in relation to conventional standard uptake value (SUV) imaging. DWB PET involves dynamic data acquisition over an extended axial range, capturing tracer kinetic information that is not available with conventional static acquisition protocols. The method can be performed within reasonable clinical imaging times, and enables generation of multiple types of PET images with complementary information in a single imaging session. Importantly, DWB PET can be used to produce multi-parametric images of (i) Patlak slope (influx rate) and (ii) intercept (referred to sometimes as “distribution volume”), while also providing (iii) a conventional ‘SUV-equivalent’ image for certain protocols. We provide an overview of ongoing efforts (primarily focused on FDG PET) and discuss potential clinically relevant applications. Overall, the framework of DWB imaging [applicable to both PET/CT(computed tomography) and PET/MRI (magnetic resonance imaging)] generates quantitative measures that may add significant value to conventional SUV image-derived measures, with limited pitfalls as we also discuss in this work.

/pdf/dynamic-whole-body-pet-imaging-principles-potentials-and-c9ttjyyv7f.pdf

Dynamic whole-body PET imaging: principles, potentials and applications

A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations — A review

A rarely used technique for enabling closure of large ventral hernias with loss of domain is preoperative progressive pneumoperitoneum (PPP), which uses intermittent insufflation to gradually stretch the contracted abdominal wall muscles, increasing the capacity of the abdominal cavity. This allows the re-introduction of herniated viscera into the abdominal cavity and assists in closure of giant hernias which may otherwise be considered inoperable. This was a prospective study assessing 16 patients between 2013 and 2015 with multi-recurrent ventral hernia. All patients were treated preoperatively with both Botulinum Toxin A (BTA) injections to the lateral abdominal wall muscles to confer flaccid paralysis, and short-term PPP to passively expand the abdominal cavity. All patients underwent serial abdominal CT imaging, with pre- and post-treatment circumference measurements of the peritoneal cavity and hernia sac, prior to undergoing operative mesh repair of their hernia. The mean hernia defect size was 236 cm2, with mean 28 % loss of domain. The mean overall duration of PPP was 6.2 days. The mean gain in abdominal circumference was 4.9 cm (5.6 %) (p 0.002) after BTA and PPP. Fascial closure and mesh hernia repair were performed in all 16 patients, with no patients suffering from postoperative abdominal hypertension, ventilatory impairment, or wound dehiscence. There are no hernia recurrences to date. Eight patients (50 %) experienced PPP-related complications, consisting of subcutaneous emphysema, pneumothorax, pneumomediastinum, pneumocardium, and metabolic acidosis. No complication required intervention. PPP is a useful adjunct in the repair of complex ventral hernia. It passively expands the abdominal cavity, allowing viscera to re-establish right of domain. At the same time, it helps to minimize the risks of postoperative abdominal compartment syndrome and the sequelae of fascial closure under tension. However, its benefits must be carefully weighed with the risk of serious complications, such as infection, perforation, pneumothorax, and pneumomediastinum.

Preoperative progressive pneumoperitoneum complementing chemical component relaxation in complex ventral hernia repair

One of the most accurate and non-invasive prostate imaging methods is magnetic resonance imaging (MRI). Segmentation is needed to find the boundary of the prostate, either automatically or semi-automatically. Recently, fully convolutional neural networks (FCNN) are being used for this purpose. In this paper, to improve the FCNN performance for prostate MRI segmentation, we analyze various structures of shortcut connections together with the size of a deep network and suggest eight different FCNNs-based deep 2D network structures for automatic MRI prostate segmentation. Our evaluations on the PROMISE12 dataset with ten-fold cross-validation indicate improved and competitive results. We analyze the results in detail, considering MRI slices, MRI volumes, test folds, and also the impact on prostate segmentation of using an EndoRectal Coil to capture the prostate MRI. Our best 2D network outperforms the state-of-the-art 3D FCNN-based methods for prostate MRI segmentation on publicly available data, without any further post-processing.

/pdf/convolutional-neural-networks-for-prostate-magnetic-3hfm71w166.pdf

Convolutional neural networks for prostate magnetic resonance image segmentation

Whole-body positron emission tomography (PET) has recently emerged as an important imaging tool for cancer detection and staging. Initial applications of the technique have been primarily qualitative. One of the major reasons is the limits imposed by kinetically undersampled data over the whole body, as opposed to the standard method of continuous dynamic sampling in one body location. Here, a new estimation method using weighted nonlinear least squares (WNLS) for the first bed position and Bayesian regression (BR) for subsequent positions is proposed. A general criterion for designing optimal sampling schedules which maximizes the measurement information with multiple bed positions is developed. The overall approach is illustrated with the problem of estimating the metabolic rate of glucose (MRGlu) in tumors at different axial positions (image bed positions) in the body by using computer simulations and patient data. The results show that estimates of MRGlu using sparse data and the optimized Bayesian approach are comparable with those obtained by standard methods and fully sampled data. This study demonstrates the potential of the technique described for quantification where several bed positions have to be used to image all the regions of interest (ROI).

Optimized sampling and parameter estimation for quantification in whole body PET

Interpreting medical images and summarising them in the form of radiology reports is a challenging, tedious, and complex task. A radiologist provides a complete description of a medical image in the form of radiology report by describing normal or abnormal findings and providing a summary for decision making. Research shows that the radiology practice is error-prone due to the limited number of experts, increasing patient volumes, and the subjective nature of human perception. To reduce the number of diagnostic errors and to alleviate the task of radiologists, there is a need for a computer-aided report generation system that can automatically generate a radiology report for a given medical image. We propose an encoder-decoder based framework that can automatically generate radiology reports from medical images. Specifically, we use a Convolutional Neural Network as an encoder coupled with a multi-stage Stacked Long Short-Term Memory as a decoder to generate reports. We perform experiments on the Indiana University Chest X-ray collection, a publicly available dataset, to measure the effectiveness of our model. Experimental results show the effectiveness of our model in automatically generating radiology reports from medical images.

Kevin Ho-Shon

Papers

A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations — A review

Preoperative progressive pneumoperitoneum complementing chemical component relaxation in complex ventral hernia repair

Convolutional neural networks for prostate magnetic resonance image segmentation

Optimized sampling and parameter estimation for quantification in whole body PET

From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach