Measuring temperature in polyvinylpyrrolidone (PVP) solutions using MR spectroscopy

Abstract

Background: Polyvinylpyrrolidone (PVP) water solutions could be used for cross-site and cross-vendor validation of diffusion-related measurements. However, since water diffusivity varies as a function of temperature, knowing the temperature of the PVP solution at the time of the measurement is fundamental in accomplishing this task.

Purpose: MR spectroscopy (MRS) could provide absolute temperature measurements since the water peak moves relative to any stable peak as temperature changes. In this work, the PVP proton spectrum was investigated to see if any stable peaks would allow for temperature determination. Reproducibility and repeatability for three scanners from three vendors were also assessed.

Methods: A spherical 17 cm container filled with 40% PVP w/w in distilled water was used for the experiments. A Point REsolved Spectroscopy Sequence (PRESS) with water suppression was employed on three 3T scanners from different vendors-GE, Siemens, and Philips. Frequency separation (in ppm) between peaks was measured in a voxel at the location of a fiber optic temperature probe and mapped to probe measured temperature. The center peak of the first methylene proton triplet closest to water peak was selected for analysis in jMRUI due to its ease of identification and echo time shift invariance. Shift in ppm of the central methylene peak proton was mapped against measured temperatures. Repeatability and reproducibility across the three scanners were determined at room temperature using 10 repeated PRESS scans. MRS established ppm shift versus temperature relationship was used to predict temperature in different PVP phantoms which were then compared against fiber optic probe measured temperature values.

Results: Several ¹H peaks were identified on all scans of the PVP phantom. The water peak moved by ∼-0.01 ppm/°C on the three scanners relative to a central methylene peak. The maximum mean absolute temperature difference over a temperature range of 18-35°C between the three scanners was 0.16°C while the minimum was 0.057°C. Repeatability on each scanner was excellent (std range: 0.00-0.14°C) over 10 repeated PRESS scans. Reproducibility across the three scanners was also excellent with mean temperature difference between scanners ranging between 0.1 and 0.4°C. Temperature values from MRS were within prediction bounds on the three scanners for another in-house prepared 40% PVP phantom (maximum difference<0.3°C), while they were consistently overestimated for another 30% PVP phantom (<1°C) and underestimated for a CaliberMRI 40% PVP phantom (<2.8°C).

Conclusions: PVP solutions exhibit stable proton peaks, one of which was used for assessing the temperature of the solution using MR proton spectroscopy. These measurements are fast and feasible with standard sequences and postprocessing MRS software and provide fundamental information for calibration of diffusion MRI using PVP solutions.

Keywords: PVP phantom; PVP spectroscopy; polyvinylpyrrolidone; temperature mapping.

FIGURE 1

PVP phantom used for spectroscopy has an inner diameter of 170 mm and is filled with 40% w/w in distilled water used for spectroscopy. The capped opening at the top (red arrow) allowed for insertion of fiber optic probe to measure temperature.

FIGURE 2

Schematic of MRS acquisition and analysis used in this work. The water peak is maintained at center frequency which resulted in temperature stable peaks (central methylene proton here) moving relative to water peak.

FIGURE 3

(a) Methylene proton peaks of PVP MR spectroscopy from GE scanner analyzed in jMRUI with the HLSVD algorithm. Central methylene peak (peak 21 at 1.469 ppm) was used for analysis. The estimate and residue are also shown. (b) Methylene proton peaks of PVP MR spectroscopy from Siemens scanner analyzed in jMRUI with the HLSVD algorithm. Central methylene peak (peak 15 at 1.448 ppm) was used for analysis. The estimate and residue are also shown. (c) Methylene proton peaks of PVP MR spectroscopy from Philips scanner analyzed in jMRUI with the HLSVD algorithm. Central methylene peak (peak 9 at 1.451 ppm) was used for analysis. The estimate and residue are also shown.

FIGURE 4

Temperature versus Δppm data for the three scanners along with linear fits. The R ² values were 0.9991, 0.9954, and 0.9974 for GE, Philips, and Siemens scanners, respectively.

FIGURE 5

Bland–Altman plot based on aggregate data of temperature obtained from the probe and MRS for all three scanners. Bias between the two was negligible at 2.3446 × 10⁻⁵°C while 95% confidence interval was ±0.5649°C.

FIGURE 6

(a) Temperature as a function of Δppm obtained on the GE scanner. Data points, linear fit, and 95% prediction interval (2δ = ±0.414°C) are shown. (b) Temperature as a function of Δppm obtained on the Siemens scanner. Data points, linear fit, and 95% prediction interval (2δ = ±0.801°C) are shown. (c) Temperature as a function of Δppm obtained on the Philips scanner. Data points, linear fit, and 95% prediction interval (2δ = ±0.590°C) are shown.