Simultaneous Estimation of Attenuation and Activity Images Using Optimization Transfer

This paper addresses the application of optimization transfer to simultaneous statistical estimation of attenuation and activity images in tomographic Image reconstruction. Although the technique we propose has wider applicability, we focus on the problem of reconstructing from data acquired via a post-injection transmission scan protocol. In this protocol, emission scan data Is supplemented with transmission scan data that is acquired after the patient has received the Injection of radio-tracer. The negative loglikelihood function for this data is a complicated function of the activity and attenuation images, leading to an objective function for the model that is difficult to minimize for the purpose of estimation. Previous work on this problem showed that when either the attenuation or activity image was held fixed, a paraboloidal surrogate could be found for the negative loglikelihood as a function of the remaining variables. This led to an algorithm In which the model's objective function is alternately minimized as a function of the attenuation and activity, using the optimization transfer technique. In the work we present here, however, we develop bivariate surrogates for the loglikelihood, i.e., functions that serve as surrogates with respect to both the attenuation and activity variables. Hence, simultaneous minimization in all variables can be carried out, potentially leading to convergence in fewer surrogate minimizations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85885/1/Fessler169.pd

An Expanded Theoretical Treatment of Iteration-Dependent Majorize-Minimize Algorithms

The majorize-minimize (MM) optimization technique has received considerable attention in signal and image processing applications, as well as in statistics literature. At each iteration of an MM algorithm, one constructs a tangent majorant function that majorizes the given cost function and is equal to it at the current iterate. The next iterate is obtained by minimizing this tangent majorant function, resulting in a sequence of iterates that reduces the cost function monotonically. A well-known special case of MM methods are expectation-maximization algorithms. In this paper, we expand on previous analyses of MM, due to Fessler and Hero, that allowed the tangent majorants to be constructed in iteration-dependent ways. Also, this paper overcomes an error in one of those earlier analyses. There are three main aspects in which our analysis builds upon previous work. First, our treatment relaxes many assumptions related to the structure of the cost function, feasible set, and tangent majorants. For example, the cost function can be nonconvex and the feasible set for the problem can be any convex set. Second, we propose convergence conditions, based on upper curvature bounds, that can be easier to verify than more standard continuity conditions. Furthermore, these conditions allow for considerable design freedom in the iteration-dependent behavior of the algorithm. Finally, we give an original characterization of the local region of convergence of MM algorithms based on connected (e.g., convex) tangent majorants. For such algorithms, cost function minimizers will locally attract the iterates over larger neighborhoods than typically is guaranteed with other methods. This expanded treatment widens the scope of the MM algorithm designs that can be considered for signal and image processing applications, allows us to verify the convergent behavior of previously published algorithms, and gives a fuller understanding overall of how these algorithms behave.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85958/1/Fessler34.pd

Fast interpolation operations in non-rigid image registration

Much literature on image registration1–3 has worked with purely geometric image deformation models. For such models, interpolation/resampling operations are often the computationally intensive steps when iteratively minimizing the deformation cost function. This article discusses some techniques for efficiently implementing and accelerating these operations. To simplify presentation, we discuss our ideas in the context of 2D imaging. However, the concepts readily generalize to 3D. Our central technique is a table-lookup scheme that makes somewhat liberal use of RAM, but should not strain the resources of modern processors if certain design parameters are appropriately selected. The technique works by preinterpolating and tabulating the grid values of the reference image onto a finer grid along one of the axes of the image. The lookup table can be rapidly constructed using FFTs. Our results show that this technique reduces iterative computation by an order of magnitude. When a minimization algorithm employing coordinate block alternation is used, one can obtain still faster computation by storing certain intermediate quantities as state variables. We refer to this technique as state variable hold-over. When combined with table-lookup, state variable hold-over reduces CPU time by about a factor two, as compared to table-lookup alone.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85925/1/Fessler207.pd

Joint Estimation of Respiratory Motion and Activity in 4D PET Using CT Side Information

In previous work, we proposed a Poisson statistical model for gated PET data in which the distribution was parametrized in terms of both image intensity and motion parameters. The motion parameters related the activity image in each gate to that of a base image in some fixed gate. By doing maximum loglikelihood (ML) estimation of all parameters simultaneously, one obtains an estimate of the base gate image that exploits the full set of measured sinogram data. Previously, this joint ML approach was compared, in a highly simplified single-slice setting, to more conventional methods. Performance was measured in terms of the recovery of tracer uptake in a synthetic lung nodule. This paper reports the extension to 3D with much more realistic simulated motion. Furthermore, in addition to pure ML estimation, we consider the use of side information from a breath-hold CT scan to facilitate regularization, while preserving hot lesions of the kind seen in FDG oncology studies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85812/1/Fessler219.pd

Joint Estimation of Image and Deformation Parameters in Tomographic Image Reconstruction

We consider an emission tomography reconstruction problem in which projection measurements from several successive time frames are available. Two strategies for doing motion-corrected image reconstruction are compared. In the first strategy, separate images are reconstructed from the measurements at each time frame. They are then consolidated by post-registration and averaging procedures. In the second strategy, parameters to describe the effects of motion are added to the statistical model of the projections. Joint maximum likelihood estimation of image and motion parameters is then carried out.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85809/1/Fessler184.pd

Joint Estimation of Image and Deformation Parameters in Motion-Corrected PET

We consider an emission tomography reconstruction problem in which projection measurements from several time frames are available. Two strategies for doing motion-corrected image reconstruction are compared. In the first strategy, separate images are reconstructed from the measurements at each time frame. They are then consolidated by post-registration and averaging procedures. In the second strategy, we incorporate parameters to describe the effects of motion into the statistical model of the projections. Joint maximum likelihood estimation of image and motion parameters is then carried out. Each of these strategies involves the minimization of non-convex cost functions. Accordingly, we also propose some relevant optimization algorithm design options.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85808/1/Fessler188.pd

Behavioral Marital Therapy: An Evaluation of Treatment Effects Across High and Low Risk Settings

The present study examined the generalization of treatment effects of a cognitive- behavioral treatment program for marital distress. Following a baseline phase, each of four couples received two phases of marital therapy within a multiple baseline across subject design. The first phase of treatment was behavioral marital therapy (BMT) focusing on communication and problem solving skills. The second phase was cognitive- behavioral marital therapy (CBMT) which focused on conflict management skills in high risk interactive settings at home. Couples’ communication was assessed in a training setting in the clinic and each of two generalization probe settings at home (a low risk and a high risk) setting. The BMT phase produced a clear reduction in communication negativity in the training setting which generalized to both the low and the high risk setting. The CMBT phase produced little additional changes in communication, however, it was associated with changes on a measure of positive and negative partner- referent thoughts

Melatonin and sleep responses following exercise in elite female athletes

To determine the melatonin concentrations and subsequent sleep indices of elite netball athletes following a training day when compared to a control day. Ten elite female netball athletes (mean ± SD; age = 23 ± 6 yrs) provided saliva samples PRE (17:15h) and POST (22:00h) a training session, and a day with no training (CONTROL). Sleep monitoring was performed using wrist actigraphy to assess total time in bed (TTB), total sleep time (TST), sleep efficiency (SE) and sleep latency (SL). Melatonin levels were significantly lower (p < 0.05), both PRE and POST the training condition (6.2 and 17.6 pg/mL, respectively) when compared to the CONTROL (14.8 and 24.3 pg/mL, respectively). There were no significant differences observed between conditions for any of the sleep variables. However, a small reduction in TST could be observed following the training session condition compared to the CONTROL condition. The scheduling of netball training in the evening is shown to suppress salivary melatonin levels. This may have an influence on subsequent sleep following night-time exercise

Age- and stress-associated C. elegans granulins impair lysosomal function and induce a compensatory HLH-30/TFEB transcriptional response.

The progressive failure of protein homeostasis is a hallmark of aging and a common feature in neurodegenerative disease. As the enzymes executing the final stages of autophagy, lysosomal proteases are key contributors to the maintenance of protein homeostasis with age. We previously reported that expression of granulin peptides, the cleavage products of the neurodegenerative disease protein progranulin, enhance the accumulation and toxicity of TAR DNA binding protein 43 (TDP-43) in Caenorhabditis elegans (C. elegans). In this study we show that C. elegans granulins are produced in an age- and stress-dependent manner. Granulins localize to the endolysosomal compartment where they impair lysosomal protease expression and activity. Consequently, protein homeostasis is disrupted, promoting the nuclear translocation of the lysosomal transcription factor HLH-30/TFEB, and prompting cells to activate a compensatory transcriptional program. The three C. elegans granulin peptides exhibited distinct but overlapping functional effects in our assays, which may be due to amino acid composition that results in distinct electrostatic and hydrophobicity profiles. Our results support a model in which granulin production modulates a critical transition between the normal, physiological regulation of protease activity and the impairment of lysosomal function that can occur with age and disease

Development of immunohistochemistry services for cancer care in western Kenya: Implications for low- and middle-income countries

Background Cancer is becoming a major cause of mortality in low- and middle-income countries. Unlike infectious disease, malignancy and other chronic conditions require significant supportive infrastructure for diagnostics, staging and treatment. In addition to morphologic diagnosis, diagnostic pathways in oncology frequently require immunohistochemistry (IHC) for confirmation. We present the experience of a tertiary-care hospital serving rural western Kenya, which developed and validated an IHC laboratory in support of a growing cancer care service. Objectives, methods and outcomes Over the past decade, in an academic North-South collaboration, cancer services were developed for the catchment area of Moi Teaching and Referral Hospital in western Kenya. A major hurdle to treatment of cancer in a resource-limited setting has been the lack of adequate diagnostic services. Building upon the foundations of a histology laboratory, strategic investment and training were used to develop IHC services. Key elements of success in this endeavour included: translation of resource-rich practices to a resource-limited setting, such as using manual, small-batch IHC instead of disposable- and maintenance-intensive automated machinery, engagement of outside expertise to develop reagent-efficient protocols and supporting all levels of staff to meet the requirements of an external quality assurance programme. Conclusion Development of low- and middle-income country models of services, such as the IHC laboratory presented in this paper, is critical for the infrastructure in resource-limited settings to address the growing cancer burden. We provide a low-cost model that effectively develops these necessary services in a challenging laboratory environment