The effect of and correction for through-slice dephasing on 2D gradient-echo double angle B 1 + mapping.

PURPOSE: To show that B 0 $$ {\mathrm{B}}_0 $$ variations through slice and slice profile effects are two major confounders affecting 2D dual angle B 1 + $$ {\mathrm{B}}_1^{+} $$ maps using gradient-echo signals and thus need to be corrected to obtain accurate B 1 + $$ {\mathrm{B}}_1^{+} $$ maps. METHODS: The 2D gradient-echo transverse complex signal was Bloch-simulated and integrated across the slice dimension including nonlinear variations in B 0 $$ {\mathrm{B}}_0 $$ inhomogeneities through slice. A nonlinear least squares fit was used to find the B 1 + $$ {\mathrm{B}}_1^{+} $$ factor corresponding to the best match between the two gradient-echo signals experimental ratio and the Bloch-simulated ratio. The correction was validated in phantom and in vivo at 3T. RESULTS: For our RF excitation pulse, the error in the B 1 + $$ {\mathrm{B}}_1^{+} $$ factor scales by approximately 3.8% for every 10 Hz/cm variation in B 0 $$ {\mathrm{B}}_0 $$ along the slice direction. Higher accuracy phantom B 1 + $$ {\mathrm{B}}_1^{+} $$ maps were obtained after applying the proposed correction; the root mean square B 1 + $$ {\mathrm{B}}_1^{+} $$ error relative to the gold standard B 1 + $$ {\mathrm{B}}_1^{+} $$ decreased from 6.4% to 2.6%. In vivo whole-liver T 1 $$ {\mathrm{T}}_1 $$ maps using the corrected B 1 + $$ {\mathrm{B}}_1^{+} $$ map registered a significant decrease in T 1 $$ {\mathrm{T}}_1 $$ gradient through slice. CONCLUSION: B 0 $$ {\mathrm{B}}_0 $$ inhomogeneities varying through slice were seen to have an impact on the accuracy of 2D double angle B 1 + $$ {\mathrm{B}}_1^{+} $$ maps using gradient-echo sequences. Consideration of this confounder is crucial for research relying on accurate knowledge of the true excitation flip angles, as is the case of T 1 $$ {\mathrm{T}}_1 $$ mapping using a spoiled gradient recalled echo sequence.

Optimal flip angles for in vivo liver 3D T1 mapping and B1+ mapping at 3T.

PURPOSE: The spoiled gradient recalled echo (SPGR) sequence with variable flip angles (FAs) enables whole liver T 1 $$ {T}_1 $$ mapping at high spatial resolutions but is strongly affected by B 1 + $$ {B}_1^{+} $$ inhomogeneities. The aim of this work was to study how the precision of acquired T 1 $$ {T}_1 $$ maps is affected by the T 1 $$ {T}_1 $$ and B 1 + $$ {B}_1^{+} $$ ranges observed in the liver at 3T, as well as how noise propagates from the acquired signals into the resulting T 1 $$ {T}_1 $$ map. THEORY: The T 1 $$ {T}_1 $$ variance was estimated through the Fisher information matrix with a total noise variance including, for the first time, the B 1 + $$ {B}_1^{+} $$ map noise as well as contributions from the SPGR noise. METHODS: Simulations were used to find the optimal FAs for both the B 1 + $$ {B}_1^{+} $$ mapping and T 1 $$ {T}_1 $$ mapping. The simulations results were validated in 10 volunteers. RESULTS: Four optimized SPGR FAs of 2°, 2°, 15°, and 15° (TR = 4.1 ms) and B 1 + $$ {B}_1^{+} $$ map FAs of 65° and 130° achieved a T 1 $$ {T}_1 $$ coefficient of variation of 6.2 ± 1.7% across 10 volunteers and validated our theoretical model. Four optimal FAs outperformed five uniformly spaced FAs, saving the patient one breath-hold. For the liver B 1 + $$ {B}_1^{+} $$ and T 1 $$ {T}_1 $$ parameter space at 3T, a higher return in T 1 $$ {T}_1 $$ precision was obtained by investing FAs in the SPGR acquisition rather than in the B 1 + $$ {B}_1^{+} $$ map. CONCLUSION: A novel framework was developed and validated to calculate the SPGR T 1 $$ {T}_1 $$ variance. This framework efficiently identifies optimal FA values and determines the total number of SPGR and B 1 + $$ {B}_1^{+} $$ measurements needed to achieve a desired T 1 $$ {T}_1 $$ precision.

Ultrasound-based sensors for respiratory motion assessment in multimodality PET imaging.

Breathing motion can displace internal organs by up to several cm; as such, it is a primary factor limiting image quality in medical imaging. Motion can also complicate matters when trying to fuse images from different modalities, acquired at different locations and/or on different days. Currently available devices for monitoring breathing motion often do so indirectly, by detecting changes in the outline of the torso rather than the internal motion itself, and these devices are often fixed to floors, ceilings or walls, and thus cannot accompany patients from one location to another. We have developed small ultrasound-based sensors, referred to as 'organ configuration motion' (OCM) sensors, that attach to the skin and provide rich motion-sensitive information. In the present work we tested the ability of OCM sensors to enable respiratory gating duringin vivoPET imaging. A motion phantom involving an FDG solution was assembled, and two cancer patients scheduled for a clinical PET/CT exam were recruited for this study. OCM signals were used to help reconstruct phantom andin vivodata into time series of motion-resolved images. As expected, the motion-resolved images captured the underlying motion. In Patient #1, a single large lesion proved to be mostly stationary through the breathing cycle. However, in Patient #2, several small lesions were mobile during breathing, and our proposed new approach captured their breathing-related displacements. In summary, a relatively inexpensive hardware solution was developed here for respiration monitoring. Because the proposed sensors attach to the skin, as opposed to walls or ceilings, they can accompany patients from one procedure to the next, potentially allowing data gathered in different places and at different times to be combined and compared in ways that account for breathing motion.