A new phantom and empirical formula for apparent diffusion coefficient measurement by a 3 Tesla magnetic resonance imaging scanner

Abstract

The aim of this study was to create a new phantom for a 3 Tesla (3T) magnetic resonance imaging (MRI) device for the calculation of the apparent diffusion coefficient (ADC) using diffusion-weighted imaging (DWI), and to mimic the ADC values of normal and tumor tissues at various temperatures, including the physiological body temperature of 37°C. The phantom was produced using several concentrations of sucrose from 0 to 1.2 M, and the DWI was performed using various phantom temperatures. The accurate ADC values were calculated using the DWIs of the phantoms, and an empirical formula was developed to calculate the ADC values of the phantoms from an arbitrary sucrose concentration and arbitrary phantom temperature. The empirical formula was able to produce ADC values ranging between 0.33 and 3.02×10−3 mm2/sec, which covered the range of ADC values of the human body that have been measured clinically by 3T MRI in previous studies. The phantom and empirical formula developed in this study may be available to mimic the ADC values of the clinical human lesion by 3T MRI.

Development of a temperature‐controlled phantom for magnetic resonance quality assurance of diffusion, dynamic, and relaxometry measurements

“…Additionally, PVP solutions are known to be chemically stable over a long period of time, in contrast to the sucrose solutions previously used for quality assurance of diffusion measurements. 13,14 Malyarenko et al 15 used ice-water as a universal temperature control fluid in diffusion measurements, removing the need to adjust for the known temperature dependence of T 1 and ADC values. Subsequently, Boss et al 16 proposed an ice-water temperature-controlled phantom with PVP solutions for the quality assurance of diffusion measurements providing reliable and repeatable values relevant to the physiological range.…”

An acetone‐based phantom for quantitative diffusion MRI

“…To determine the feasibility of ADC tuning by changing the concentration of the solute (sucrose, PVP, and D 2 O) in the phantoms, ADC measured in multiple vials with increasing solute concentrations (as described above) were compared for each type of phantom. Wide range of ADC . The attainable ADC values should cover the entire clinically relevant range (0.6·10 −3 –2.6·10 −3 mm 2 ·s −1 ) . The range of ADC values measured from DW‐MRS and DW‐EPI data of each phantom was evaluated.…”

“…For this reason, the use of ice‐water baths has become a well‐accepted means to maintain the temperature of a diffusion phantom at a highly reproducible temperature (0°C). Unfortunately, water has limited ADC (ADC <1.1·10 −3 mm 2 ·s −1 ) at 0°C, while ADC in tissue may be up to 2.6·10 −3 mm 2 ·s −1 at body temperature . Although scanning water‐based phantoms at higher temperatures (eg, 37.5°C) is possible, accurate control at such a temperature is very challenging compared with temperature control at 0°C.…”

Linear Intensity-Based Image Registration

“…2) Apparent diffusion coefficient (ADC): Diffusion weighted imaging (DWI) measures the diffusion of water molecules within different tissues [9]. Normal prostate gland tissue has a higher water diffusion rate than cancer tissue [9].DWI is inherently T2WI but, unlike conventional T2WI, prostate cancer usually demonstrates increased SI on DWI effecting the lesion difficult to visualize within the normal high signal peripheral zone [9].To minimize the effect of this T2WI sine through effect that may result in the depiction of a false positive high intensity lesion [10], the ADC is calculated [8], which is calculated based on diffusionweighted imaging(DWI) using various b values, is useful for differentiate existence lesion either benign or malignant [11].Furthermore, Prostate lesion visible as a high signal region on DWI but as low signal region on ADC map. Fig.…”

“…Where J (x, y) is a reference image, J' (X, Y) is the target image and M, N are the dimensions of the images. A lower value signifies lower similarity error and higher similarity between the images [11] D. Optimizer Regular Step Gradient Descent (RSGD) is applied as optimization method. This optimizer suits most for minimization problem in image registration [23].Hence, it is chosen to minimize the similarity measure between two images.…”