PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single-valued population-based lung LAC, and better estimation is needed to improve quantification. Given the under-appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single-valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission-based schemes. Potential strategies for future developments are also presented

4D Image Reconstruction with Dual Respiratory and Cardiac Motion Correction for Cardiac PET

4D image reconstruction with motion correction is the solution to improve image quality and resolution degraded by respiratory motion (RM) and cardiac motion (CM) in cardiac PET scans. The improved image quality can potentially improve clinical diagnosis, and can be traded for reduced injected radiation dose or reduced imaging time for improving patient comfort. There are three steps for 4D image reconstruction with motion correction: 1) 4D data generation (gating), 2) 4D respiratory and cardiac (R&C) motion estimation, and 3) 4D R&C motion correction. We have developed and evaluated multiple methods for each step including (step 1) data-driven gating, MRI-navigator-gating, (step 2) 4 different methods for dual R&C motion estimation after reconstruction (MEAR), CM estimation during reconstruction (MEDR), RM estimation before reconstruction (MEBR), and (step 3) dual R&C motion correction after (MCAR), during (MCDR), and before (MCBR) image reconstruction. Realistic Monte Carlo simulated 4D cardiac PET data using the 4D XCAT phantom and accurate models of the scanner design parameters and performance characteristics and clinical patient data were used to evaluate all different methods. Data-driven gating method was shown to provide robust gating results in high myocardium uptake situations while MRI-navigator based gating showed better results in low myocardium uptake situations. Separate R&C MEAR with modeling of RM on CM estimation was shown to be the best option for accurate estimation of dual R&C motion estimation. The MCDR method yields the best performance for different noise situations for both patient and simulation, while MCBR reduces computational time dramatically but the resultant 4D cardiac gated PET images has overall inferior image quality when compared to that from the MCAR and MCDR approaches in the ‘almost’ noise free case. Also, the MCBR method has better noise handling properties when compared with MCAR and provides better quantitative result in high noise cases. In general, our developed methods demonstrated the importance of motion correction on image qualities, our work also provide a general guideline for different applications that requires either highly quantitative data or qualitative images. Our works also provide practical means for applying 4D image reconstruction with reasonable computational cost

DEVELOPMENT AND APPLICATIONS OF FEATURE-GUIDED CARDIAC MOTION ESTIMATION METHODS FOR 4D CARDIAC PET

The aim of this dissertation research is to develop, implement and evaluate methods to extract useful information about cardiac motion and myocardial contractility from 4D cardiac PET images with much improved image quality. First, to reduce the influence of respiratory motion and improve the quality of cardiac PET images used in motion estimation, data-driven respiratory gating methods are proposed to allow accurate extraction of respiratory motion signal from the list-mode data. Time-of-flight PET information is incorporated into respiratory signal extraction, and background correction method is developed to improve the quality and accuracy of the extracted respiratory signal. The methods were applied and evaluated using clinical list-mode cardiac PET data. With improved image quality, anatomical feature such as papillary muscles and the interventricular sulcus become increasingly detectable in gated cardiac PET images. For more accurate cardiac motion estimation, these anatomical features in human heart were extracted and used in combination with a priori knowledge of cardiac function to guide the cardiac motion estimation process. Initial estimates of the cardiac motion vector field were obtained based on the motion of the features for the traditional optical-flow algorithm. For further improvement, motion of the anatomical feature was used as additional constraint in the motion estimation algorithm to reduce the effect of the classical aperture problem. Different from previous cardiac motion extraction and estimation studies that only provide qualitative evaluation of the motion estimation results due to unavailability of ground truth for clinical cardiac datasets, this study employed simulation data from a realistic digital phantom with known cardiac motion for both qualitative and quantitative evaluation. Motion estimation results from simulation data indicate the feature-based cardiac motion estimation method is able to improve the accuracy of the cardiac motion field estimates, especially for motion components parallel to edges and therefore difficult to estimate using the conventional optical-flow based method. The proposed research will allow PET imaging to provide unprecedented cardiac motion information in addition to its functional information thus improving diagnosis of cardiac diseases including perfusion and motion abnormalities, and patient care with reduced cost. Also, more accurate estimation of cardiac motion will help to further improve the quality of 4D cardiac PET imaging with cardiac motion compensation

High-speed Volumetric Functional Imaging with Light Field Microscopy

The continuous advancement in microscopy has been unveiling the hidden world of tissues, cells, and molecules. In the quest for deeper spatiotemporal insights into biological processes, light field microscopy (LFM) has emerged as a powerful and intriguing tool. Unlike traditional imaging systems that capture focused images, LFM records multiplexed signals with single snapshot that encodes information within a three-dimensional (3D) volume. By leveraging computational reconstruction algorithms, this approach enables the observation of transient volumetric dynamics with remarkable efficiency and speed. This thesis presents a series of efforts to apply LFM in functional imaging, enabling researchers to monitor real-time changes in live organisms, including ion fluxes, electrical signaling, and cells interactions. The exceptional temporal resolution makes LFM a unique tool to visualize and analyze rapid processes that are difficult to capture with conventional 3D microscopy. We demonstrated calcium imaging of motor neurons in freely moving C. elegans and tracked flowing blood cells in-vivo within a beating zebrafish heart. The excessive and unpredictable motion observed in these processes requires capturing hundreds of 3D volumes per second, a demanding but necessary task that provides insights into the underlying mechanisms of neural and cardiac functions. We further moved forward to voltage imaging, a frontier in neuroscience, which directly measures the neural action potential as well as sub-thresholding activities. Our LFM provides kilohertz volumetric imaging on leech ganglion and mouse hippocampus. It measures 7.3 gigavoxels per second in a 3D field of view of 550×550×300 μm^3, which makes it capable of recording the accurate timing and waveform of neural spikes across entire volume. These demonstrations are achieved through several innovative redesigns of LFM, detailed in Chapter 3 to 5. The first approach, VCD-LFM, addresses the inherent trade-off between spatial resolution and depth information in light field imaging by introducing a learning-based reconstruction algorithm. By incorporating data priors and constraints, this method aims to mitigate the issues of low spatial resolution and artifacts in conventional LFM without compromising imaging speed. The second approach, Squeezed Light Field Microscopy (SLIM), leverages data redundancy in light fields and revises the optical hardware to achieve kilohertz volume rate. Designed to meet the high-speed demands of voltage imaging, SLIM offers a powerful and robust imaging tool for sparse volumetric processes. Lastly, the third approach, Light Field Tomography (LIFT), adapts LFM for one-dimensional (1D) measurements through optical Radon transformation. This method enables the use of low-dimensional detectors, such as line sensor, to capture high-dimensional light fields, resulting in enhanced sensitivity, reduced cost and even greater temporal resolution

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

The nuclear medicine technologist will see you now

Background: It has been estimated that an additional 3500 radiographers alone are needed over the next 5 years. Assistant Practitioners, Advanced Practitioners and Radiologists equals further 2500 positions. A major expansion in the imaging workforce is a must to fulfil the increasing demand for radiology services. Recruitment within existing radiology workforce and training in Nuclear Medicine had proven insufficient. Development of Apprenticeship for Nuclear Medicine degree at Cumbria University was essential. Registration with The Academy for Healthcare Science (AHCS) was guaranteed upon completion. Methods used: Data analysis from the first University intake in 2017 through 2018, 2019 and the very challenging 2020 cohort of apprentices. Assessment of the recruitment process including candidate background, experience and education. Students’ journey and feedback from their degree level 6 studies. Data for the number of graduating students across cohorts. Retention data of newly qualified professionals in training departments. Summary: Recruiting candidates internally, ensuring they have a healthcare experience, facilitate retention post qualification. Fulfilment of University requirements regarding UCAS points proves to be a valuable tool to ensure studies completion. UHS alone managed to recruit four candidates. Two already qualified with 1st hons degree and working at band 5 level and the other two are determined to progress within the profession upon graduation. Conclusion: It had been proved that candidates with prior healthcare experience are more likely to successfully complete studies. They perform well within the role and progress guaranteeing retention. Structured training with university input ensured highly qualified workforce registered with AHCS