1naresh
Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)
1naresh
Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)


Table 3: 
√MSE calculated for the fitting of the Patlak linea
90–240 Seconds30–90 Seconds0–90 Seconds0–90 Seconds
Reference StandardDelay CorrectionDelay CorrectionNo Delay Correction
Nonischemic1.56 (1.36,1.79)1.12 (0.97–1.30)2.38 (1.74–3.26)4.04 (3.04–5.36)
P < .001P = .002P < .001
Infarct1.79 (1.49, 2.15)1.19 (1.02–1.39)2.54 (1.85–3.49)4.05 (3.03–5.38)
P < .001P = .026P < .001
Tissue at risk1.65 (1.45, 1.87)1.14 (1.00–1.29)2.86 (2.07–3.95)4.25 (3.24–5.56)
P< .001P< .001P< .001
  • 

    a Mean √MSE and corresponding 95% CIs are shown for different region-of-interest and acquisition-time datasets, as well as P values derived from the GEE models. √MSE is a measure of variability of data points around a straight line: a value close to zero indicates a smaller spread of data points around the line, corresponding to a better fit.