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  • 1.
    Eckerbom, Per
    et al.
    Uppsala universitet, Enheten för radiologi.
    Hansell, Peter
    Uppsala universitet, Integrativ Fysiologi.
    Bjerner, Tomas
    Uppsala universitet, Enheten för radiologi.
    Palm, Fredrik
    Uppsala universitet, Integrativ Fysiologi.
    Weis, Jan
    Uppsala universitet, Enheten för radiologi.
    Liss, Per
    Uppsala universitet, Enheten för radiologi.
    Intravoxel Incoherent Motion MR Imaging of the Kidney: Pilot Study2013In: Advances in Experimental Medicine and Biology, ISSN 0065-2598, E-ISSN 2214-8019, Vol. 765, p. 55-58Article in journal (Refereed)
    Abstract [en]

    MR examinations (Achieva 3 T, Philips, Best, The Netherlands) were performed at five different occasions in a healthy volunteer (male 60 years) and in one renal cancer patient (male 78 years) with normal renal function (creatinine 88 μmol/L). Intravoxel incoherent motion (IVIM) coefficients D + D* were measured using respiratory-triggered diffusion-weighted spin-echo echo-planar imaging. Perfusion data of the patient were acquired using a saturation-recovery gradient-echo sequence and with the bolus of Gd-BOPTA (Multihance). D + D* were computed by monoexponential fitting of MR signal intensity attenuation versus b for b = 0, 50, 100, 150 s/mm2. Perfusion parameters were evaluated with “NordicICE” software. The map of D + D* was compared qualitatively with the perfusion map computed from the Gd scan. D + D* values of the cortex and medulla were in the range 2.3–2.7 and 1.1–1.6 × 10-3 mm2/s, respectively. In conclusion, in this pilot study a good qualitative relation between IVIM variables D + D* and renal perfusion has been found.

  • 2.
    Edlund, Jenny
    et al.
    Uppsala universitet, Enheten för radiologi.
    Hansell, Peter
    Uppsala universitet, Integrativ Fysiologi.
    Fasching, Angelica
    Uppsala universitet, Integrativ Fysiologi.
    Liss, Per
    Uppsala universitet, Integrativ Fysiologi.
    Weis, Jan
    Uppsala universitet, Enheten för radiologi.
    Glickson, Jerry D.
    Palm, Fredrik
    Uppsala universitet, Integrativ Fysiologi.
    Reduced oxygenation in diabetic rat kidneys measured by T2* weighted magnetic resonance micro-imaging2009In: Advances in Experimental Medicine and Biology, ISSN 0065-2598, E-ISSN 2214-8019, Vol. 645, p. 199-204Article in journal (Refereed)
    Abstract [en]

    By applying invasive techniques for direct measurements of oxygen tension, we have reported decreased kidney oxygenation in experimental diabetes in rats. However, the non-invasive MRI technique utilizing the BOLD effect provides several advantages with the possibility to perform repetitive measurements in the same animals and in human subjects. In this study, we applied a modified single gradient echo micro-imaging sequence to detect the BOLD effect in kidneys of diabetic rats and compared the results to normoglycemic controls. All measurements were performed on inactin-anaesthetized adult male Wistar Furth rats. Diabetes was induced by streptozotocin (45 mg/kg) 14 days prior to MRI-analysis. Sixteen T2*-weighted image records (B0=1.5 T) were performed using radiofrequency spoiled gradient echo sequence with 2.6 ms step increments of TE (TE1=12 ms), while TR (75 ms) and bandwidth per pixel (71.4 Hz) were kept constant. T2* maps were computed by mono-exponential fitting of the pixel intensities. Relaxation rates R2* (1/T2*) in cortex and outer stripe of the outer medulla were similar in both groups (cortex for controls 22.3 +/- 0.4 vs. diabetics 23.1 +/- 1.8 Hz and outer stripe of outer medulla for controls 24.9 +/- 0.4 vs. diabetics 26.4 +/- 1.8 Hz; n=4 in both groups), whereas R2* was increased in the inner stripe of the outer medulla in diabetic rats (diabetics 26.1 +/- 2.4 vs. controls 18.8 +/- 1.4 Hz; n=4, P<0.05). This study demonstrates that experimental diabetes in rats induces decreased oxygenation of the renal outer medulla. Furthermore, the proposed T2*-weighted MR micro-imaging technique is suitable for detection of regional changes in kidney oxygenation in experimental animal models.

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