Quantitative Statistical Methods for Image Quality Assessment

Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit)

Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized

Osteogenesis and adipogenesis control in the BMP signaling pathway

Nohe, AnjaAccording to NIH around 50% of women over age 50 are affected by Osteoporosis increasing the risk of fractures and injuries. Osteoporotic conditions may arise from either slow bone formation or increased bone resorption or both. Recent studies showed that the bone marrow of osteoporotic patients have increased accumulation of adipocytes. These adipocytes may either migrate into the bone when the bone becomes brittle or stem cells may differentiate into adipocytes instead of osteoblasts. One of the major growth factors that can determine the cell fate of mesenchymal stem cells into adipocytes or osteoblasts is Bone Morphogenetic Protein 2 (BMP2). We recently identified new protein-protein interaction. We identified that Casein Kinase II binds to the BMP2 type IA receptor. There are three potential CK2 binding sites on the receptor and we designed three peptides blocking specifically the interaction of CK2 with one specific binding site on the receptor. We have also synthesized three mutant varieties of the BMPR1a each having a point mutation at each of these CK2 phosphorylation sites. In this study we have deduced a model for osteogenesis mediated by the mutants. In order to investigate the mechanisms of stem cell differentiation by these peptides we identified Endoglin. Endoglin is a co receptor in the BMP signaling pathway and was identified as a marker for cell fate differentiation. Previous studies have shown that low expression of Endoglin is linked with increased osteogenic potential, while high Endoglin expression is linked with high adipogenic potential. Using RT-PCR we found that Endoglin is upregulated during adipogenesis. Additionally overexpression of Endoglin in mesenchymal cells led to increased adipogenesis suggesting that Endoglin itself is not only a marker, but involved in the mechanism of adipogenesis. Endoglin was specifically upregulated in adipocytes of brown fat origin, since the adipocytes expressed PRDM16 which is a known marker for brown fat. These data reveal a new mechanism of adipocyte differentiation and may assist in the development of new therapeutics decreasing the number of adipocytes in the bone marrow and increasing the number of osteoblasts.University of Delaware, Department of Biological SciencesM.S

Patlak parametric imaging for the preclinical study.

<p>(a) The Patlak influx constant was computed using noisy, Gaussian-denoised, PCA-denoised, HYPR-denoised, NLM-S denoised, and NLM-ST denoised images from an PET mouse study. (b) The top row delineates the signal (red) and background (blue) ROIs used for evaluation. The middle row shows the percentage recovered signal in the hot regions for different denoising methods. The bottom row shows the CNR in the Patlak parametric images, measured as the ratio of the contrast between the signal and the background ROIs to the standard deviation in the background.</p

Fitted kinetic parameters for the organ-wise TACs displayed in figure 2(b).

<p>Fitted kinetic parameters for the organ-wise TACs displayed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081390#pone-0081390-g002" target="_blank">figure 2(b)</a>.</p