MR fingerprinting of the prostate

Abstract

Multiparametric magnetic resonance imaging (mpMRI) has been adopted as the key tool for detection, localization, characterization, and risk stratification of patients suspected to have prostate cancer. Despite advantages over systematic biopsy, the interpretation of prostate mpMRI has limitations including a steep learning curve, leading to considerable interobserver variation. There is growing interest in clinical translation of quantitative imaging techniques for more objective lesion assessment. However, traditional mapping techniques are slow, precluding their use in the clinic. Magnetic resonance fingerprinting (MRF) is an efficient approach for quantitative maps of multiple tissue properties simultaneously. The T₁ and T₂ values obtained with MRF have been validated with phantom studies as well as in normal volunteers and patients. Studies have shown that MRF-derived T₁ and T₂ along with ADC values are all significant independent predictors in the differentiation between normal prostate tissue and prostate cancer, and hold promise in differentiating low and intermediate/high-grade cancers. This review seeks to introduce the basics of the prostate MRF technique, discuss the potential applications of prostate MRF for the characterization of prostate cancer, and describes ongoing areas of research.

Keywords: Magnetic resonance fingerprinting; Magnetic resonance imaging; Prostate; Quantitative imaging.

Figure 1:

Overview of the MRF workflow. (Left top) Data are acquired using an MRF pulse sequence with variable acquisition settings (FA and TR). (Left bottom) The MRF pulse sequence parameters and a large set of tissue property values (i.e. T₁ and T₂) are entered as inputs to a Bloch equation simulation to generate the MRF dictionary. (Middle top) The MRF pulse sequence is used to collect highly accelerated images at the MRI scanner; the signal timecourse of one voxel over time (orange curve) is dictated by the properties of the tissue in that voxel along with the pulse sequence settings. (Middle bottom) The measured signal from one voxel is compared to the dictionary in the pattern matching step and the best match found. (Right) The tissue properties used to make the best matching dictionary entry are assigned as the T₁ and T₂ values for that voxel, and the process repeated for all voxels.

Figure 2:

A schematic of the pulse sequence used for prostate MRF at 3T. The readout used in many implementations is a uniform density spiral. The repetitions times (TR) and flip angles (α) change after each data acquisition block, and are shown on the left hand side of Figure 1.

Figure 3:

Images and maps collected in a patient with prostate cancer (Gleason score 9) at 3T. (top left) T₂-weighted axial image with a large hypointense cancer in the PZ (white thick arrow). (top right) The cancer demonstrates a lower diffusion coefficient on the Apparent Diffusion Coefficient (ADC) map. (bottom row) MRF-derived T₂ and T₁ maps, respectively. The cancer is marked on these maps by an ROI with a black border denoted again by the white arrow, and the normal-appearing PZ ROI marked with the simple black border.

Figure 4:

Comparison of ADC, T₁ and T₂ values for targeted biopsy-proven prostate cancer (A-D), prostatitis (E-H) and benign prostatic tissue (I-L) collected at 3T. Prostate cancer: T2w image (A) shows focal dark lesion against diffuse dark background signal in right peripheral zone with ADC of 0.87 × 10⁻³ mm²/s (B). T₁ and T₂ values were 1560 ms and 42 ms respectively. Prostatitis: T2w (E) shows a wedge-shaped mildly dark lesion in left peripheral zone with ADC of 0.87 × 10⁻³ mm²/s (F). T₁ and T₂ values were higher than cancer at 1770 ms and 83 ms respectively. Benign prostatic tissue: T2w (I) shows a focal lesion in right apical peripheral zone with ADC of 0.82 × 10⁻³ mm²/s. Based on suspicious morphology on clinical MRI, biopsy was performed which revealed benign prostatic tissue. T₁ and T₂ values were higher than cancer at 2310 ms and 73 ms respectively. Figure reproduced from A. Panda et al., “Targeted Biopsy Validation of Peripheral Zone Prostate Cancer Characterization With Magnetic Resonance Fingerprinting and Diffusion Mapping,” Invest. Radiol., p. 1, 2019, doi: 10.1097/rli.0000000000000569. with permission from Wolters Kluwer Health, Inc.

Figure 5:

Quantitative characterization with combined MRF-relaxometry and ADC mapping at 3T. (a) Scatterplot of T₂ versus ADC for prostatitis (n = 15), low-grade cancers (n =10) and clinically significant cancers (n = 53). ADC value of 1.04×10⁻³ mm²/s is sensitive but not specific for differentiating all cancers from prostatitis (right vertical line). ADC value of 0.78×10⁻³ mm²/s (left vertical line) is the best cut-off for differentiating clinically significant cancers from low-grade cancers and prostatitis. In the ADC overlap zone (between two vertical lines), a T₂ ≤ 68 ms is additionally helpful in differentiating cancers from prostatitis (horizontal line). (b) Scatterplot of T₁ versus ADC for non-cancers including prostatitis (n = 15), negative biopsies (n = 26), low-grade cancers (n =10) and clinically significant cancers (n = 53). ADC values of 0.75×10⁻³ mm²/s followed by T₁ of 1720 ms are the best cut-offs for differentiating cancers from non-cancers (horizontal line). In the ADC overlap zone (between vertical lines), while five clinically significant cancers had T₁ > 1720 ms, they also had T₂ ≤ 68 ms. Figure reproduced from A. Panda et al., “Targeted Biopsy Validation of Peripheral Zone Prostate Cancer Characterization With Magnetic Resonance Fingerprinting and Diffusion Mapping,” Invest. Radiol., p. 1, 2019, doi: 10.1097/rli.0000000000000569. with permission from Wolters Kluwer Health, Inc.

Figure 6:

Comparison of images from (left to right) axial T₂-weighted MRI, apparent diffusion coefficient (ADC) mapping, and T₁ and T₂ MR fingerprinting mapping for targeted biopsy-proven prostate cancer, prostatitis, and a benign prostatic hyperplasia (BPH) nodule all collected at 3T. A–D, Biopsy-proven prostate cancer (arrow). Mean T₁, T₂, and ADC were 1450 msec, 43 msec, and 0.51×10⁻³ mm²/sec, respectively. E–H, Biopsy-proven prostatitis (arrow). Mean T₁, T₂, and ADC were 1615 msec, 63 msec, and 0.83×10⁻³ mm²/sec, respectively. I–L, For the BPH nodule (arrow), mean T₁, T₂, and ADC were 1600 msec, 43 msec, and 0. 87×10⁻³ mm²/sec, respectively. Note the difference in T₁ relaxation times between transition zone cancer and noncancers despite the lesions having similar hypointense signal intensity on T₂-weighted images. Figure reproduced from A. Panda et al., “MR Fingerprinting and ADC Mapping for Characterization of Lesions in Transition Zone of the Prostate Gland,” Radiology. 2019; 292:685–694, permission pending.

Figure 7:

Scatterplot of apparent diffusion coefficient (ADC) versus T₁ for normal transition zone (NTZ) (n = 66), biopsy-proven noncancers (n = 38), and prostate cancers (n = 37) shows that cancers are well separated from biopsy-proven noncancers and NTZ in a quantitative space. Regions defined by the optimal model are denoted by the solid line (prostate cancers vs noncancers) and the dashed line (prostate cancers vs NTZ). Figure reproduced from A. Panda et al., “MR Fingerprinting and ADC Mapping for Characterization of Lesions in Transition Zone of the Prostate Gland,” Radiology. 2019; 292:685–694, permission pending.