Proton resonance frequency-based thermometry for aqueous and adipose tissues.

PURPOSE: The proton resonance frequency (PRF)-based thermometry uses heating-induced phase variations to reconstruct magnetic resonance (MR) temperature maps. However, the measurements of the phase differences may be corrupted by the presence of fat due to its phase being insensitive to heat. The work aims to reconstruct the PRF-based temperature maps for tissues containing fat. METHODS: This work proposes a PRF-based method that eliminates the fat's phase contribution by estimating the temperature-insensitive fat vector. A vector in a complex domain represents a given voxel's magnetization from an acquired, complex MR image. In this method, a circle was fit to a time series of vectors acquired from a heated region during a heating experiment. The circle center served as the fat vector, which was then subtracted from the acquired vectors, leaving only the temperature-sensitive vectors for thermal mapping. This work was verified with the gel phantoms of 10%, 15%, and 20% fat content and the ex vivo phantom of porcine abdomen tissue during water-bath heating. It was also tested with an ex vivo porcine tissue during focused ultrasound (FUS) heating. RESULTS: A good agreement was found between the temperature measurements obtained from the proposed method and the optical fiber temperature probe in the verification experiments. In the gel phantoms, the linear regression provided a slope of 0.992 and an R2 of 0.994. The Bland-Altman analysis gave a bias of 0.49°C and a 95% confidence interval of ±1.60°C. In the ex vivo tissue, the results of the linear regression and Bland-Altman methods provided a slope of 0.979, an intercept of 0.353, an R2 of 0.947, and a 95% confidence interval of ±3.26°C with a bias of -0.14°C. In FUS tests, a temperature discrepancy of up to 28% was observed between the proposed and conventional PRF methods in ex vivo tissues containing fat. CONCLUSIONS: The proposed PRF-based method can improve the accuracy of the temperature measurements in tissues with fat, such as breast, abdomen, prostate, and bone marrow.

Ultrasound-based sensors to monitor physiological motion.

PURPOSE: Medical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as "organ configuration motion" (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing-related signals. METHODS: By design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from magnetic resonance imaging (MRI) and optical tracking signals in five and eight volunteers, respectively. RESULTS: Breathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively. CONCLUSION: The present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.

Improved PRF-based MR thermometry using k-space energy spectrum analysis.

PURPOSE: Proton resonance frequency (PRF) thermometry encodes information in the phase of MRI signals. A multiplicative factor converts phase changes into temperature changes, and this factor includes the TE. However, phase variations caused by B0 and/or B1 inhomogeneities can effectively change TE in ways that vary from pixel to pixel. This work presents how spatial phase variations affect temperature maps and how to correct for corresponding errors. METHODS: A method called "k-space energy spectrum analysis" was used to map regions in the object domain to regions in the k-space domain. Focused ultrasound heating experiments were performed in tissue-mimicking gel phantoms under two scenarios: with and without proper shimming. The second scenario, with deliberately de-adjusted shimming, was meant to emulate B0 inhomogeneities in a controlled manner. The TE errors were mapped and compensated for using k-space energy spectrum analysis, and corrected results were compared with reference results. Furthermore, a volunteer was recruited to help evaluate the magnitude of the errors being corrected. RESULTS: The in vivo abdominal results showed that the TE and heating errors being corrected can readily exceed 10%. In phantom results, a linear regression between reference and corrected temperatures results provided a slope of 0.971 and R2 of 0.9964. Analysis based on the Bland-Altman method provided a bias of -0.0977°C and 95% limits of agreement that were 0.75°C apart. CONCLUSION: Spatially varying TE errors, such as caused by B0 and/or B1 inhomogeneities, can be detected and corrected using the k-space energy spectrum analysis method, for increased accuracy in proton resonance frequency thermometry.