ARRS 2022 Abstracts

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ERS3344. Comparison of Quality and Lesion Detection of Deep Learning-Based T2-Weighted Reconstruction to Clinical T2-Weighted Images on Prostate MRI
Authors * Denotes Presenting Author
  1. Barun Bagga *; New York University School of Medicine
  2. Robert Petrocelli; New York University School of Medicine
  3. Paul Smereka; New York University School of Medicine
  4. Hersh Chandarana; New York University School of Medicine
  5. Angela Tong; New York University School of Medicine
Objective:
The purpose of the study was to compare image quality and lesion detection of a prototype accelerated T2 weighted deep learning reconstruction (DL-T2) sequence compared to a clinical T2-weighted TSE (C-T2) on biparametric MRI of the prostate.

Materials and Methods:
DL-T2 (Siemens Healthineers, Erlangen, Germany) is an accelerated deep learning-based reconstruction, with k-space under-sampling via GRAPPA parallel imaging sampling, resulting in a reduction of average acquisition time from 3 min 22 seconds to 49 seconds. We retrospectively evaluated 80 consecutive patients (ages 47-84, PSA 0.4-25.4ng/mL) who underwent prostate MRI with C-T2 and DL-T2. All had a prostate biopsy within 6 months of MRI or stability of PSA or MRI for at least a year. C-T2 and DL-T2 images were anonymized and randomly presented to two blinded readers for image quality assessment on a Likert scale of 1(non-diagnostic)-4(excellent). Each reviewer evaluated bi-parametric MRI consisting of clinical diffusion-weighted images and either C-T2 or DL-T2 utilizing PI-RADS v2.1. Wilcoxon signed-rank test was used to compare the image quality scores. McNemar test and receiver operator characteristic (ROC) curve analysis were performed to compare lesion detection.

Results:
Biopsy results were available for 55/80 patients (18/55 = Gleason Grade Group 2 or higher). 25 patients had PI-RADS 1 or 2 results on prostate MRI with stable follow up PSA or MRI. The image quality results were as follows; Reader 1: overall quality (mean+/-SD: 3.88+/-0.40 (DL-T2); 3.73+/-0.53 (C-T2); p-value=0.03), clarity of capsule (mean+/-SD: 3.91+/-0.33 (DL-T2); 3.76+/-0.51 (C-T2); p-value=0.02), clarity of peripheral and transitional zone boundary (mean+/-SD: 3.91+/-0.33 (DL-T2); 3.76+/-0.51 (C-T2); p-value=0.02), and clarity of periurethral area (mean+/-SD: 3.85+/-0.39 (DL-T2); 3.74+/-0.52 (C-T2); p-value=0.09). Reader 2, overall quality (mean+/-SD: 3.31+/-0.74 (DL-T2); 3.33+/-0.82 (C-T2); p-value=0.97), clarity of capsule (mean+/-SD: 3.53+/-0.69 (DL-T2); 3.46+/-0.75 (C-T2); p-value=0.46), clarity of peripheral and transitional zone boundary (mean+/-SD: 3.19+/-0.84 (DL-T2); 3.28+/-0.83 (C-T2); p-value=0.39), and clarity of periurethral area (mean+/-SD: 3.34+/-0.75 (DL-T2); 3.36+/-0.77 (C-T2); p-value=0.81). Intra-class correlation coefficient for overall image quality was fair-to-good for DL-T2 (ICC: 0.57; range: 0.33-0.72), and was good for C-T2 (ICC: 0.72; range: 0.56 - 0.82). Frequency of lesion detection was not significantly different between DL-T2 + DWI (94 lesions) and C-T2 +DWI (91 lesions), p-value=0.77. We utilized PI-RADS 3 or greater as positive for prostate cancer. For reader 1, area under the curve (AUC) of ROC: 0.75 (clinical exam (CE)), 0.75 (deep learning exam (DLE)); Sensitivity (Sn): 0.61 (CE), 0.61 (DLE); Specificity (Sp): 0.74 (CE), 0.81 (DLE);. For reader 2, AUC of ROC: 0.65 (CE), 0.63 (DLE); Sn: 0.56 (CE), 0.61(DLE); Sp: 0.61 (CE), 0.50 (DLE).

Conclusion:
Novel accelerated DL-T2 has the potential to reduce scan time for prostate MRI and shows comparable image quality and diagnostic performance to clinical T2-weighted imaging.