Prostate T(1) quantification using a magnetization-prepared spiral technique

T1 Mapping of the Prostate Using Single-Shot T1FLASH: A Clinical Feasibility Study to Optimize Prostate Cancer Assessment

PURPOSE

The aim of this study was to assess the clinical feasibility of magnetic resonance imaging (MRI) T1 mapping using T1FLASH for assessment of prostate lesions.

METHODS

Participants with clinical suspicion for prostate cancer (PCa) were prospectively enrolled between October 2021 and April 2022 with multiparametric prostate MRI (mpMRI) acquired on a 3 T scanner. In addition, T1 mapping was accomplished using a single-shot T1FLASH technique with inversion recovery, radial undersampling, and iterative reconstruction. Regions of interest (ROIs) were manually placed on radiologically identified prostate lesions and representative reference regions of the transitional zone (TZ), benign prostate hyperplasia nodules, and peripheral zone (PZ). Mean T1 relaxation times and apparent diffusion coefficient (ADC) values (b = 50/b = 1400 s/mm 2 ) were measured for each ROI. Participants were included in the study if they underwent ultrasound/MRI fusion-guided prostate biopsy for radiologically or clinically suspected PCa. Histological evaluation of biopsy cores served as reference standard, with grading of PCa according to the International Society of Urological Pathology (ISUP). ISUP grades 2 and above were considered clinically significant PCa for the scope of this study. Histological results of prostate biopsy cores were anatomically mapped to corresponding mpMRI ROIs using biopsy plans. T1 relaxation times and ADC values were compared across prostate regions and ISUP groups. Across different strata, T1 relaxation time, ADC values, and diagnostic accuracy (area under the curve [AUC]) were compared using statistical methods accounting for clustered data.

RESULTS

Of 67 eligible participants, a total of 40 participants undergoing ultrasound/MRI fusion-guided prostate biopsy were included. Multislice T1 mapping was successfully performed in all participants at a median acquisition time of 2:10 minutes without evident image artifacts. A total of 71 prostate lesions was radiologically identified (TZ 49; PZ 22). Among those, 22 were histologically diagnosed with PCa (ISUP groups 1/2/3/4 in n = 3/15/3/1 cases, respectively). In the TZ, T1 relaxation time was statistically significantly lower for PCa compared with reference regions ( P = 0.029) and benign prostate hyperplasia nodules ( P < 0.001). Similarly, in the PZ, PCa demonstrated shorter T1 relaxation times versus reference regions ( P < 0.001). PCa also showed a trend toward shorter T1 relaxation times (median, 1.40 seconds) compared with radiologically suspicious lesions with benign histology (median, 1.47 seconds), although statistical significance was not reached ( P = 0.066). For discrimination of PCa from reference regions and benign prostate lesions, T1 relaxation times and ADC values demonstrated AUC = 0.80 and AUC = 0.83, respectively ( P = 0.519). Discriminating PCa from radiologically suspicious lesions with benign histology, T1 relaxation times and ADC values showed AUC = 0.69 and AUC = 0.62, respectively ( P = 0.446).

CONCLUSIONS

T1FLASH-based T1 mapping yields robust results for quantification of prostate T1 relaxation time at a short examination time of 2:10 minutes without evident image artifacts. Associated T1 relaxation times could aid in discrimination of significant and nonsignificant PCa. Further studies are warranted to confirm these results in a larger patient cohort, to assess the additional benefit of T1FLASH maps in conjunction with mpMRI sequences in the setting of deep learning, and to evaluate the robustness of T1FLASH maps compared with potentially artifact-prone diffusion-weighted imaging sequences.

T1 Mapping of the Prostate using Single-Shot T1FLASH and MOLLI MRI Techniques: Comparison of Artifact Burden and Image Quality

PURPOSE

To assess artifact burden and image quality of different MRI T1 mapping techniques of the prostate.

METHODS

Participants with suspected prostate cancer (PCa) were prospectively enrolled from June-October 2022 and examined with multiparametric prostate MRI (mpMRI; 3 T scanner; T1wi, T2wi, DWI und DCE). T1 mapping was performed before and after administration of gadolinium-based contrast-agent (GBCA) using (i) a modified Look-Locker inversion (MOLLI) technique and (ii) a novel single-shot T1FLASH inversion recovery technique. T2wi, DWI, T1FLASH and MOLLI sequences were systematically examined regarding prevalence of artifacts and image quality using a 5-point Likert-Scale.

RESULTS

A total of n = 100 patients were included (median age: 68 years). T1FLASH maps (pre-and post-GBCA) showed metal artifacts in 7% of cases and susceptibility artifacts in 1%. For MOLLI maps, pre-GBCA metal and susceptibility artifacts were documented in 6.5% of cases each. MOLLI maps post-GBCA showed artifacts in 59% of cases resulting primarily from urinary GBCA excretion and GBCA accumulation at the bladder base (p < 0.01 versus T1FLASH post-GBCA). Image quality for T1FLASH pre-GBCA was rated at a mean 4.9+/-0.4 and for MOLLI at 4.8+/-0.6 (p = 0.14). Post-GBCA image quality was rated at a mean 4.9+/-0.4 for T1FLASH and at 3.7+/-1.1 for MOLLI (p < 0.001).

CONCLUSIONS

T1FLASH maps provide a fast and robust method for quantification of T1 relaxation times of the prostate. T1FLASH is suitable for T1 mapping of the prostate following administration of contrast agents, while MOLLI T1 mapping is impaired through GBCA accumulation at the bladder base leading to severe image artifacts and reduced image quality.

Feasibility of Novel Three-Dimensional Magnetic Resonance Fingerprinting of the Prostate Gland: Phantom and Clinical Studies

Objective

To evaluate the feasibility of a new three-dimensional (3D) MR fingerprinting (MRF) technique for the prostate gland by conducting phantom and clinical studies.

Materials and Methods

The new 3D MRF technique used in this study enables quick data acquisition and has a high resolution. For the phantom study, the MRF T1 and T2 values in an in-house phantom were compared with those of gold-standard mapping methods using linear regression analysis. For the clinical study, we evaluated 90 patients who underwent prostate imaging with MRF for suspected prostate cancer between September 2019 and February 2020. The mean T1 and T2 values were compared in the peripheral zone, transition zone, and focal lesions using paired t tests. The differences in the T1 and T2 values according to cancer aggressiveness were evaluated using one-way analysis of variance.

Results

In the phantom study, the MRF T1 and T2 values showed a perfect correlation with the gold-standard T1 and T2 values (R > 0.99). In the clinical study, the T1 and T2 values in the peripheral zone were significantly higher than those in the transitional zone (p < 0.001, both). The T1 and T2 values in prostate cancer were significantly lower than those in the peripheral and transitional zones. The higher the grade of cancer, the lower the T2 values.

Conclusion

The T1 and T2 values obtained from the 3D MRF showed a perfect correlation with the gold standard values in the phantom study. Differences in the T1 and T2 values among the different zones of the prostate gland were identified using 3D MRF in patients.

Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence

Purpose of this study was to evaluate the diagnostic performance of T1 relaxation time (T1) for differentiating prostate cancer (PCa) from benign tissue as well as high- from low-grade PCa. Twenty-three patients with suspicion for PCa were included in this prospective study. 3 T MRI including a Modified Look-Locker inversion recovery sequence was acquired. Subsequent targeted and systematic prostate biopsy served as a reference standard. T1 and apparent diffusion coefficient (ADC) value in PCa and reference regions without malignancy as well as high- and low-grade PCa were compared using the Mann-Whitney U test. The performance of T1, ADC value, and a combination of both to differentiate PCa and reference regions was assessed by receiver operating characteristic (ROC) analysis. T1 and ADC value were lower in PCa compared to reference regions in the peripheral and transition zone (p < 0.001). ROC analysis revealed high AUCs for T1 (0.92; 95%-CI, 0.87-0.98) and ADC value (0.97; 95%-CI, 0.94 to 1.0) when differentiating PCa and reference regions. A combination of T1 and ADC value yielded an even higher AUC. The difference was statistically significant comparing it to the AUC for ADC value alone (p = 0.02). No significant differences were found between high- and low-grade PCa for T1 (p = 0.31) and ADC value (p = 0.8). T1 relaxation time differs significantly between PCa and benign prostate tissue with lower T1 in PCa. It could represent an imaging biomarker for PCa.

MR elastography to measure the effects of cancer and pathology fixation on prostate biomechanics, and comparison with T 1, T 2 and ADC

MRI is under evaluation for image-guided intervention for prostate cancer. The sensitivity and specificity of MRI parameters is determined via correlation with the gold-standard of histopathology. Whole-mount histopathology of prostatectomy specimens can be digitally registered to in vivo imaging for correlation. When biomechanical-based deformable registration is employed to account for deformation during histopathology processing, the ex vivo biomechanical properties are required. However, these properties are altered by pathology fixation, and vary with disease. Hence, this study employs magnetic resonance elastography (MRE) to measure ex vivo prostate biomechanical properties before and after fixation. A quasi-static MRE method was employed to measure high resolution maps of Young's modulus (E) before and after fixation of canine prostate and prostatectomy specimens (n  =  4) from prostate cancer patients who had previously received radiation therapy. For comparison, T ₁, T ₂ and apparent diffusion coefficient (ADC) were measured in parallel. E (kPa) varied across clinical anatomy and for histopathology-identified tumor: peripheral zone: 99(±22), central gland: 48(±37), tumor: 85(±53), and increased consistently with fixation (factor of 11  ±  5; p  <  0.02). T ₂ decreased consistently with fixation, while changes in T ₁ and ADC were more complex and inconsistent. The biomechanics of the clinical prostate specimens varied greatly with fixation, and to a lesser extent with disease and anatomy. The data obtained will improve the precision of prostate pathology correlation, leading to more accurate disease detection and targeting.

Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging

PURPOSE

There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).

METHODS

This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng∕ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.

RESULTS

For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.

CONCLUSIONS

This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accurately localizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.