Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. Here, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study, described in Part I (Concepts and Applications), was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. Here, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study, described in Part I (Concepts and Applications), was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

The Probe of Inflation and Cosmic Origins (PICO) is a probe-class mission concept currently under study by NASA. PICO will probe the physics of the Big Bang and the energy scale of inflation, constrain the sum of neutrino masses, measure the growth of structures in the universe, and constrain its reionization history by making full sky maps of the cosmic microwave background with sensitivity 80 times higher than the Planck space mission. With bands at 21-799 GHz and arcmin resolution at the highest frequencies, PICO will make polarization maps of Galactic synchrotron and dust emission to observe the role of magnetic fields in Milky Way's evolution and star formation. We discuss PICO's optical system, focal plane, and give current best case noise estimates. The optical design is a two-reflector optimized open-Dragone design with a cold aperture stop. It gives a diffraction limited field of view (DLFOV) with throughput of 910 cm2sr at 21 GHz. The large 82 square degree DLFOV hosts 12,996 transition edge sensor bolometers distributed in 21 frequency bands and maintained at 0.1 K. We use focal plane technologies that are currently implemented on operating CMB instruments including three-color multi-chroic pixels and multiplexed readouts. To our knowledge, this is the first use of an open-Dragone design for mm-wave astrophysical observations, and the only monolithic CMB instrument to have such a broad frequency coverage. With current best case estimate polarization depth of 0.65 μKCMB-arcmin over the entire sky, PICO is the most sensitive CMB instrument designed to date.

The Probe of Inflation and Cosmic Origins (PICO) is a NASA-funded study of a Probe-class mission concept. The toplevel science objectives are to probe the physics of the Big Bang by measuring or constraining the energy scale of inflation, probe fundamental physics by measuring the number of light particles in the Universe and the sum of neutrino masses, to measure the reionization history of the Universe, and to understand the mechanisms driving the cosmic star formation history, and the physics of the galactic magnetic field. PICO would have multiple frequency bands between 21 and 799 GHz, and would survey the entire sky, producing maps of the polarization of the cosmic microwave background radiation, of galactic dust, of synchrotron radiation, and of various populations of point sources. Several instrument configurations, optical systems, cooling architectures, and detector and readout technologies have been and continue to be considered in the development of the mission concept. We will present a snapshot of the baseline mission concept currently under development.