DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT

There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5% and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT

Power consumption in fpga based bit-serial and bit-parallel digital filter systems

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Development and Objective Task-Based Evaluation of Computational Methods for Clinical SPECT

Single-photon emission computed tomography (SPECT) is widely used for imaging radiotracer distribution in vivo. In several SPECT applications, patients are administered a significant amount of radiation dose and thus, it is desirable to reduce the dose level. However, reducing the dose level results in low-count data, which leads to a decrease in image quality in terms of the performance on clinical tasks. Moreover, SPECT in applications such as alpha-particle radiopharmaceutical therapies (α-RPT) is inherently count limited. Methods to improve the image quality in low-dose/low-count settings for SPECT images are thus much needed. The goal of this dissertation is to develop computational methods to fulfill this need and to objectively evaluate such methods on clinical estimation and detection tasks. To compensate for image-degrading processes of attenuation and scatter of photons in SPECT, a separate CT scan is often performed. However, CT-based compensation method leads to higher dose and possibility of misalignment between CT and SPECT scans. Thus, investigation towards quantifying the information content present in SPECT emission data for jointly estimating the attenuation and activity map is of significant importance. For this purpose, we developed a Fisher information analysis framework to quantify the information content in list-mode (LM) SPECT data. We demonstrated that LM SPECT emission data contains information for the joint estimation task. In applications such as α-RPT, SPECT provides an opportunity to quantify absorbed dose, but this task is challenged by the low number of detected counts. Thus, developing methods to reconstruct SPECT images that extracts maximal possible information from detected photons are required. Toward this goal, we developed a LM reconstruction method that uses data from multiple energy windows and includes energy attributes of detected photons. The proposed method yielded improved quantification performance compared to a conventional method that uses data from a single energy window and incorporates data in binned-mode format. The next part of this dissertation focuses on developing methods to improve image quality in the context of low-dose/low-count myocardial perfusion imaging (MPI) SPECT. In MPI SPECT, which is a widely used imaging modality, an important clinical task is the detection of perfusion defects. There is an important need for methods to process/acquire MPI SPECT images in low-dose settings. Toward this goal, we first developed a deep learning-based task-specific denoising method, DEMIST. We demonstrated that the DEMIST method significantly outperformed low-dose images and the conventional deep learning-based denoising methods on the task of detecting perfusion defects. A second approach to improve image quality is to optimize the image acquisition protocol in low-dose setting. In such low-dose settings, we observed that current clinical acquisition protocols yield sub-optimal image quality when applied to patients displaying outlier anatomical characteristics, such as those with large body habitus, or female patients with large breasts. To address this issue, we developed a detection-task-specific protocol optimization method based on the anatomical characteristics of each patient. We demonstrated that an optimized protocol for each patient yielded significant improvement compared to the clinical protocol on the task of detecting perfusion defects. Overall, this dissertation demonstrates that development of new computational methods can assist with improving performance on clinical tasks in SPECT in low dose and low-count settings

Dietary iodine deficiency in the Gippsland region of Victoria, Australia

Background Iodine is an essential micronutrient for the production of thyroid hormones and normal neurodevelopment. A deficiency in iodine causes a number of defects collectively known as Iodine Deficiency Disorder (IDD). Even mild iodine deficiency in pregnancy is a risk factor for babies as it may result in impaired intellectual development; this is the most serious consequence of mild to moderate dietary iodine deficiency. Australia overall is iodine deficient. However, in the National Iodine Nutrition Study (NINS), Victoria had the worst status with regard to iodine deficiency in school children. The Gippsland region of Victoria has a long history of iodine deficiency. In 1960, Gippsland was described in a WHO monograph series on endemic goitre as the ‘home of goitre’ in Victoria. Despite this history the Gippsland population has not been screened for iodine deficiency since 1948. In response to this data we devised a four part study to provide details of the current regional and sub regional iodine status of Gippsland. Hypothesis There is a recurrence of dietary iodine deficiency in the Gippsland region. Objective The research plan initially included three objectives • To examine the historical evidence of dietary Iodine deficiency. • To research environmental iodine status. • To estimate the population iodine status and factors affecting it. Two further objectives were added during the project • To develop an effective and easy method to regularly analyse and monitor the iodine status (deficiency) of pregnant women and their new born babies. • To measure the effect on the iodine status of a cohort of Gippsland pregnant women of the recently commenced Food Standards of Australia and New Zealand (FSANZ) nationwide bread iodine fortification program. Methods This is a four part study using different methods to test our hypothesis: • Part 1: Search for historical evidence of iodine deficiency using archival research, rare book and journal collection searches and asking for Gippsland people to recall previous iodine supplementation programs they may have participated in. • Part 2: Research for environmental evidence of iodine deficiency by measuring the iodine concentration of drinking water from 18 water treatment plants and rain water tanks across central, west and south Gippsland. • Part 3: Victorian (including Gippsland) Neonatal population iodine status estimation by retrospective neonatal Thyroid Stimulating Hormone (TSH) data analysis from 2001 to 2006. • Part 4: Gippsland pregnant women iodine status estimation by urinary iodine concentration measurement and an exploration of factors influencing iodine status during pregnancy by application of a self-reported food questionnaire. Results There was an iodine tablet supplementation program in Gippsland schools from the late 1940s to late 1960s. Gippsland drinking water shows an iodine concentration indicative of environmental iodine deficiency. Analysis of the Victorian neonatal Thyroid Stimulating Hormone (TSH) database from 2001 to 2006 shows that the incidence of iodine deficiency is increasing in Gippsland and Victoria among newborns at the population level. The urinary iodine concentration in a cohort of Gippsland pregnant women indicates an inadequate intake of iodine during pregnancy and that the bread iodine fortification program appears to be ineffective in this cohort. Conclusion Results from this study indicate that dietary iodine deficiency is a reemerging problem in the Gippsland region. In Gippsland the assumption of the average Australian population daily intake of iodine through drinking water by FSANZ is misleading and FSANZ needs to take account of regional variation when calculating the influence of this critical dietary component. The current iodine fortification program needs to be extended to other foods and focus on the prevention of iodine deficiency during pregnancy. Neonatal TSH levels should be used to monitor the Australian population iodine (deficiency) status and the effectiveness of fortification, especially in the population groups most at risk of iodine deficiency