Towards fast and accurate temperature mapping with proton resonance frequency-based MR thermometry

Abstract

The capability to image temperature is a very attractive feature of MRI and has been actively exploited for guiding minimally-invasive thermal therapies. Among many MR-based temperature-sensitive approaches, proton resonance frequency (PRF) thermometry provides the advantage of excellent linearity of signal with temperature over a large temperature range. Furthermore, the PRF shift has been shown to be fairly independent of tissue type and thermal history. For these reasons, PRF method has evolved into the most widely used MR-based thermometry method. In the present paper, the basic principles of PRF-based temperature mapping will be reviewed, along with associated pulse sequence designs. Technical advancements aimed at increasing the imaging speed and/or temperature accuracy of PRF-based thermometry sequences, such as image acceleration, fat suppression, reduced field-of-view imaging, as well as motion tracking and correction, will be discussed. The development of accurate MR thermometry methods applicable to moving organs with non-negligible fat content represents a very challenging goal, but recent developments suggest that this goal may be achieved. If so, MR-guided thermal therapies may be expected to play an increasingly-important therapeutic and palliative role, as a minimally-invasive alternative to surgery.

Figure 1.. The gradient waveforms of the multipathway pulse sequence for thermal therapy.

This sequence features signals from two magnetization pathways: PSIF and FISP echoes, both of which are acquired by EPI readouts. Three blips along the G_z(t) waveform manipulates the magnetization pathways and force PSIF echoes to be acquired first. Acquiring PSIF first and FIPS later allows higher temperature sensitivity for both echoes.

Figure 2.. Schematic flow chart illustrates how 2DRF, UNFOLD, and parallel imaging are combined for temperature measurements.

Top-left: A MR image shows the experimental setup. A water gel phantom and a transducer are immersed in a water tank. The focused ultrasound beam is delivered through the water and focused within the phantom. Top-right: 2DRF pulse excites one central lobe and two side lobes. Bottom-right: With FOV reduced to one-eighth of its original size, side lobes and vanishing tail of the central lobe are aliased onto the acquired images. However, the wraparounds can be removed by UNFOLD and parallel imaging, leaving only the central lobe. Bottom-left: Looking at how the phase changes over time, the temperature estimations are obtained.

Figure 3.. Simultaneous fat suppression and rFOV imaging on a fat-water phantom by using the 2DRF pulse at 3T.

A: The reference image produced by the normal FGRE sequence with the normal slice-selective RF pulse. Chemical shift appears along the frequency encoding (FE) direction (vertical) with a narrow receiver bandwidth of 8.06KHz; B-D: Excitation profiles with different field-of-excitation (FOE) values by using a 11-subpulse echo planar 2DRF pulse (each subpulse with 1140 µs duration). When FOE equals to FOV (D), only a fraction of water at the FOV center is excited.

Figure 4.. Illustration of 1D temperature measurements during a 20s sonication in a gel phantom using line scan echo-planar spectroscopic imaging at 1.5T.

Up: This “M-mode” MRI shows the temperature evolution in the line as a function of time. The line was acquired along the direction of the ultrasound beam. By acquiring 32 echoes in one TR of 200 ms and using TE values greater or equal to 27.6 ms, we achieved 4577 measurements in 40 s with a noise level in the imaging with a standard deviation less than ±1°C. Bottom: Plot of temperature rise vs. time at the focus during the sonication.

Figure 5.. Screenshot of a FUS platform interface for real-time visualization of phantom heating using an open-source software package 3D Slicer.

It displays a phantom heating experiment, during which temperature maps were continuously acquired, processed, and displayed. The top panel displays the 3D object and the bottom panel displays three orthogonal tomographic planes of the 3D image. The temperature map is overlaid on the bottom left panel and the heating effect is shown in yellow superimposed on the baseline image. Temperature estimation was updated every 1s with latency of less than 1s.