Rusinek, Henry

Intravoxel incoherent motion MRI parameters derived from two software packages demonstrated diagnostic value for breast cancer, with multivariable combinations of histogram metrics achieving high diagnostic accuracy.

AbstractPurpose has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging–Dynamic Contrast‐Enhanced (OSIPI‐DCE) challenge was designed to benchmark methods to better help the efforts to standardize measurement.MethodsA framework was created to evaluate values produced by DCE‐MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' values, the applied software, and a standard operating procedure. These were evaluated using the proposed score defined with accuracy, repeatability, and reproducibility components.ResultsAcross the 10 received submissions, the score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0–1 = lowest–highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility.ConclusionsThis study reports results from the OSIPI‐DCE challenge and highlights the high inter‐software variability within estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real‐world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Blood oxygen level-dependent (BOLD) MRI data of kidney, while indicative of tissue oxygenation level (Po2), is in fact influenced by multiple confounding factors, such as R2, perfusion, oxygen permeability, and hematocrit. We aim to explore the feasibility of extracting tissue Po2 from renal BOLD data. A method of two steps was proposed: first, a Monte Carlo simulation to estimate blood oxygen saturation (SHb) from BOLD signals, and second, an oxygen transit model to convert SHb to tissue Po2. The proposed method was calibrated and validated with 20 pigs (12 before and after furosemide injection) in which BOLD-derived tissue Po2 was compared with microprobe-measured values. The method was then applied to nine healthy human subjects (age: 25.7 ± 3.0 yr) in whom BOLD was performed before and after furosemide. For the 12 pigs before furosemide injection, the proposed model estimated renal tissue Po2 with errors of 2.3 ± 5.2 mmHg (5.8 ± 13.4%) in cortex and −0.1 ± 4.5 mmHg (1.7 ± 18.1%) in medulla, compared with microprobe measurements. After injection of furosemide, the estimation errors were 6.9 ± 3.9 mmHg (14.2 ± 8.4%) for cortex and 2.6 ± 4.0 mmHg (7.7 ± 11.5%) for medulla. In the human subjects, BOLD-derived medullary Po2 increased from 16.0 ± 4.9 mmHg (SHb: 31 ± 11%) at baseline to 26.2 ± 3.1 mmHg (SHb: 53 ± 6%) at 5 min after furosemide injection, while cortical Po2 did not change significantly at ∼58 mmHg (SHb: 92 ± 1%). Our proposed method, validated with a porcine model, appears promising for estimating tissue Po2 from renal BOLD MRI data in human subjects.