Kasam, Mallikarjunarao

Background and Objectives: Peroxisome proliferator activated receptor-gamma agonists, such as pioglitazone (PIO) enhanced hematoma resolution and improved functional recovery in our animal model of intracerebral hemorrhage (ICH). We conducted a translational Phase II randomized, controlled clinical trial to determine the maximum tolerated dose (MTD) of PIO in patients with spontaneous ICH and to explore the rate of hematoma resolution and clinical outcome. Methods: Patients with spontaneous ICH within 24 hours of symptom onset were randomly allocated 1:1 to placebo or PIO. Patients received escalating doses of PIO daily for three days, followed by a 30mg maintenance dose for the duration of treatment. Duration of treatment was when 75% of the ICH had resolved as determined by serial MRI or 10 weeks of treatment, whichever occurred first. The primary safety outcome was mortality at Day 14. Secondary measures of safety include any mortality, symptomatic cerebral edema, congestive heart failure, edema, hypoglycemia, anemia, and hepatotoxicity. Secondary measures of efficacy include hematoma resolution and clinical outcome. The MTD was determined using the Continual Reassessment Method. Results: From March 2009 to April 2013, 84 patients (42 PIO, 42 control) were enrolled into 11 dose tiers, with a planned range from 0.1-2.0 mg/kg/d. Table 1 demonstrates preliminary baseline and clinical characteristics of patients by treatment group. Overall, 2/84 patients died within 2 weeks after ICH; however mortality rate never exceeded prespecified criteria. The study will be complete in October 2013 and the treatment team remains blinded to treatment allocation. Secondary outcomes by treatment group and the MTD of PIO will be reported. Conclusions: We have completed the treatment phase of the SHRINC Trial. Long term follow-up is on-going. These results will provide the foundation for an efficacy trial evaluating PIO as a potential treatment for patients with spontaneous ICH.

Methods Patient exams were performed under an IRB-approved protocol. Two 3D SWIRLS [3] single phase contrast-enhanced angiographies with 1mm3 spatial resolution were used for this work. Clinical exams were acquired on 3.0 T scanners (GE, DVMR 20.1IB, Milwaukee, WI). Data analysis The angiogram was post-processed using tree analysis and virtual endoscopy modules of Analyze 11.0 software (Analyze 11.0; Biomedical Imaging Resource, Mayo Clinic, Rochester, MN) to fly through the artery. A statistical parameter called Brightness Area Product (BAP) was defined as the sum of the intensities above the sample minimum intensity/threshold set by the user. Results and Discussions Figures (I-IV) show the methodology to fly through the part of anterior carotid artery (ACA) and all statistics of part of the artery (Fig. IV) with angles from the “Root” to its different branches of A1 to G2 (Fig. III). Figure II shows the snap shot of the fly through/internal cross sectional structure of ACA of branch “E2”, at the location marked as “red dot”. Table (IV) represents different statistics including length, angle from the root, area of cross section and the BAP of the branches mentioned in Fig. III. The computation time for this method is < 5 minutes compared to the other reconstruction techniques (>~30 minutes). We proposed a simple and fast automated post-processing method to fly through the artery using Analyze 11.0 software. This technique can be extended to any arteries to monitor internal vasculature and to estimate the stenosis using the statistical parameter BAP, which generates a master profile/database of arteries. References 1 Shu Y, Bernstein MA, ISMRM 2009 p764.

PurposeLow contrast (LC) detectability is a common test criterion for diagnostic radiologic quality control (QC) programs. Automation of this test is desirable in order to reduce human variability and to speed up analysis. However, automation is challenging due to the complexity of the human visual perception system and the ability to create algorithms that mimic this response. This paper describes the development and testing of an automated LC detection algorithm for use in the analysis of magnetic resonance (MR) images of the American College of Radiology (ACR) QC phantom.MethodsThe detection algorithm includes fuzzy logic decision processes and various edge detection methods to quantify LC detectability. Algorithm performance was first evaluated using a single LC phantom MR image with the addition of incremental zero mean Gaussian noise resulting in a total of 200 images. A c‐statistic was calculated to determine the role of CNR to indicate when the algorithm would detect ten spokes. To evaluate inter‐rater agreement between experienced observers and the algorithm, a blinded observer study was performed on 196 LC phantom images acquired from nine clinical MR scanners. The nine scanners included two MR manufacturers and two field strengths (1.5 T, 3.0 T). Inter‐rater and algorithm‐rater agreement was quantified using Krippendorff's alpha.ResultsFor the Gaussian noise added data, CNR ranged from 0.519 to 11.7 with CNR being considered an excellent discriminator of algorithm performance (c‐statistic = 0.9777). Reviewer scoring of the clinical phantom data resulted in an inter‐rater agreement of 0.673 with the agreement between observers and algorithm equal to 0.652, both of which indicate significant agreement.ConclusionsThis study demonstrates that the detection of LC test patterns for MR imaging QC programs can be successfully developed and that their response can model the human visual detection system of expert MR QC readers.