Bayesian design of measurements for magnetorelaxometry imaging
Bayesian design of measurements for magnetorelaxometry imaging
复制标题
磁松弛测量成像的贝叶斯测量设计
DOI：
10.1088/1361-6420/ad07fd
复制
发表时间：
2023
期刊：
影响因子：
2.1
通讯作者：
Juha
中科院分区：
文献类型：
--
作者：
T. Helin;Nuutti Hyvönen;Jarno Maaninen;Juha
The aim of magnetorelaxometry imaging is to determine the distribution of magnetic nanoparticles inside a subject by measuring the relaxation of the superposition magnetic field generated by the nanoparticles after they have first been aligned using an external activation magnetic field that has subsequently been switched off. This work applies techniques of Bayesian optimal experimental design to (sequentially) selecting the positions for the activation coil in order to increase the value of data and enable more accurate reconstructions in a simplified measurement setup. Both Gaussian and total variation (TV) prior models are considered for the distribution of the nanoparticles. The former allows simultaneous offline computation of optimized designs for multiple consecutive activations, while the latter introduces adaptability into the algorithm by using previously measured data in choosing the position of the next activation. The TV prior has a desirable edge-enhancing characteristic, but with the downside that the computationally attractive Gaussian form of the posterior density is lost. To overcome this challenge, the lagged diffusivity iteration is used to provide an approximate Gaussian posterior model and allow the use of the standard Bayesian A- and D-optimality criteria for the TV prior as well. Two-dimensional numerical experiments are performed on a few sample targets, with the conclusion that the optimized activation positions lead, in general, to better reconstructions than symmetric reference setups when the target distribution or region of interest are nonsymmetric in shape.
登录
查看更多内容
影响因子：
3.9
作者：
Jaufenthaler, Aaron;Schier, Peter;Baumgarten, Daniel
通讯作者：
Baumgarten, Daniel
非线性反问题的 A 最优编码权重，及其在亥姆霍兹反问题中的应用
影响因子：
2.1
作者：
Crestel, Benjamin;Alexanderian, Alen;Stadler, Georg;Ghattas, Omar
通讯作者：
Ghattas, Omar
影响因子：
2
作者：
Schier, Peter;Liebl, Maik;Baumgarten, Daniel
通讯作者：
Baumgarten, Daniel
影响因子：
2.1
作者：
Foecke, Janic;Baumgarten, Daniel;Burger, Martin
通讯作者：
Burger, Martin
影响因子：
3.5
作者：
Liebl, M.;Steinhoff, U.;Trahms, L.
通讯作者：
Trahms, L.