Evidence of a Generalized Defect of Acinar Cell Function in Shwachman-Diamond Syndrome

: Because the acinar cells of the exocrine pancreas in patients with Shwachman-Diamond syndrome (SDS) are severely depleted, we hypothesized that a similar deficiency may be present in acinar cells of the parotid gland.

Generalized expansion of nociceptive reflex receptive fields in chronic pain patients

Widespread central hypersensitivity is present in chronic pain and contributes to pain and disability. According to animal studies, expansion of receptive fields of spinal cord neurons is involved in central hypersensitivity. We recently developed a method to quantify nociceptive receptive fields in humans using spinal withdrawal reflexes. Here we hypothesized that patients with chronic pelvic pain display enlarged reflex receptive fields. Secondary endpoints were subjective pain thresholds and nociceptive withdrawal reflex thresholds after single and repeated (temporal summation) electrical stimulation. 20 patients and 25 pain-free subjects were tested. Electrical stimuli were applied to 10 sites on the foot sole for evoking reflexes in the tibialis anterior muscle. The reflex receptive field was defined as the area of the foot (fraction of the foot sole) from which a muscle contraction was evoked. For the secondary endpoints, the stimuli were applied to the cutaneous innervation area of the sural nerve. Medians (25-75 percentiles) of fraction of the foot sole in patients and controls were 0.48 (0.38-0.54) and 0.33 (0.27-0.39), respectively (P=0.008). Pain and reflex thresholds after sural nerve stimulation were significantly lower in patients than in controls (P<0.001 for all measurements). This study provides for the first time evidence for widespread expansion of reflex receptive fields in chronic pain patients. It thereby identifies a mechanism involved in central hypersensitivity in human chronic pain. Reverting the expansion of nociceptive receptive fields and exploring the prognostic meaning of this phenomenon may become future targets of clinical research.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics

Locally affine (polyaffine) image registration methods capture intersubject non-linear deformations with a low number of parameters, while providing an intuitive interpretation for clinicians. Considering the mandible bone, anatomical shape differences can be found at different scales, e.g. left or right side, teeth, etc. Classically, sequential coarse to fine registration are used to handle multiscale deformations, instead we propose a simultaneous optimization of all scales. To avoid local minima we incorporate a prior on the polyaffine transformations. This kind of groupwise registration approach is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images, with varying transformations for each region. In this paper, we reformulate polyaffine deformations in a generative statistical model, which enables us to incorporate deformation statistics as a prior in a Bayesian setting. We find optimal transformations by optimizing the maximum a posteriori probability. We assume that the polyaffine transformations follow a normal distribution with mean and concentration matrix. Parameters of the prior are estimated from an initial coarse to fine registration. Knowing the region structure, we develop a blockwise pseudoinverse to obtain the concentration matrix. To our knowledge, we are the first to introduce simultaneous multiscale optimization through groupwise polyaffine registration. We show results on 42 mandible CT images.