In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods. This work attempts to address these issues by proposing a unified and generic code framework for formatting, processing and reconstructing acquired multi-modal and multi-dimensional data. The proposed iterative framework processes in the same way elements from list-mode (i.e. events) and histogrammed (i.e. sinogram or other bins) data sets. Each element is processed separately, which opens the way for highly parallel execution. A unique iterative algorithm engine makes use of generic core components corresponding to the main parts of the reconstruction process. Features that are specific to different modalities and algorithms are embedded into specific components inheriting from the generic abstract components. Temporal dimensions are taken into account in the core architecture. The framework is implemented in an open-source C++ parallel platform, called CASToR (customizable and advanced software for tomographic reconstruction). Performance assessments show that the time loss due to genericity remains acceptable, being one order of magnitude slower compared to a manufacturer's software optimized for computational efficiency for a given system geometry. Specific optimizations were made possible by the underlying data set organization and processing and allowed for an average speed-up factor ranging from 1.54 to 3.07 when compared to more conventional implementations. Using parallel programming, an almost linear speed-up increase (factor of 0.85 times number of cores) was obtained in a realistic clinical PET setting. In conclusion, the proposed framework offers a substantial flexibility for the integration of new reconstruction algorithms while maintaining computation efficiency.

).q RSNA, 2014 Purpose:To evaluate if measurement of split renal function (SRF) with dynamic contrast material-enhanced (DCE) magnetic resonance (MR) urography is equivalent to that with renal scintigraphy (RS) in patients suspected of having chronic urinary obstruction. Materials and Methods:The study protocol was approved by the institutional ethics committee of the coordinating center on behalf of all participating centers. Informed consent was obtained from all adult patients or both parents of children. This prospective, comparative study included 369 pediatric and adult patients from 14 university hospitals who were suspected of having chronic or intermittent urinary obstruction, and data from 295 patients with complete data were used for analysis. SRF was measured by using the area under the curve and the Patlak-Rutland methods, including successive review by a senior and an expert reviewer and measurement of intra-and interobserver agreement for each technique. An equivalence test for mean SRF was conducted with an a of 5%. Results:Reproducibility was substantial to almost perfect for both methods. Equivalence of DCE MR urography and RS for measurement of SRF was shown in patients with moderately dilated kidneys (P , .001 with the Patlak-Rutland method). However, in severely dilated kidneys, the mean SRF measurement was underestimated by 4% when DCE MR urography was used compared with that when RS was used. Age and type of MR imaging device had no significant effect. Conclusion:For moderately dilated kidneys, equivalence of DCE MR urography to RS was shown, with a standard deviation of approximately 12% between the techniques, making substitution of DCE MR urography for RS acceptable. For severely dilated kidneys, a mean underestimation of SRF of 4% should be expected with DCE MR urography, making substitution questionable.q RSNA, 2014

The analysis of abdominal and thoracic dynamic contrastenhanced MRI is often impaired by artifacts and misregistration caused by physiological motion. Breath-hold is too short to cover long acquisitions. A novel multipurpose reconstruction technique, entitled dynamic contrast-enhanced generalized reconstruction by inversion of coupled systems, is presented. It performs respiratory motion compensation in terms of both motion artefact correction and registration. It comprises motion modeling and contrast-change modeling. The method feeds on physiological signals and x-f space properties of dynamic series to invert a coupled system of linear equations. The unknowns solved for represent the parameters for a linear nonrigid motion model and the parameters for a linear contrast-change model based on B-splines. Performance is demonstrated on myocardial perfusion imaging, on six simulated data sets and six clinical exams. The main purpose consists in removing motion-induced errors from time-intensity curves, thus improving curve analysis and postprocessing in general. This method alleviates postprocessing difficulties in dynamic contrast-enhanced MRI and opens new possibilities for dynamic contrast-enhanced MRI analysis. Magn Reson Med 65:812-822,

In PET image reconstruction, it would be useful to obtain the entire posterior probability distribution of the image, because it allows for both estimating image intensity and assessing the uncertainty of the estimation, thus leading to more reliable interpretation. We propose a new entirely probabilistic model: the prior is a distribution over possible smooth regions (distance-driven Chinese restaurant process), and the posterior distribution is estimated using a Gibbs MCMC sampler. Data from other modalities (here one or several MR images) are introduced into the model as additional observed data, providing side information about likely smooth regions in the image. The reconstructed image is the posterior mean, and the uncertainty is presented as an image of the size of 95% posterior intervals. The reconstruction was compared to MLEM and OSEM algorithms, with and without post-smoothing, and to a penalized ML or MAP method that also uses additional images from other modalities. Qualitative and quantitative tests were performed on realistic simulated data with statistical replicates and on several clinical examinations presenting pathologies. The proposed method presents appealing properties in terms of obtained bias, variance, spatial regularization, and use of multimodal data, and produces in addition potentially valuable uncertainty information.