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DECONVOLUTION ANALYSIS OF
 RENOGRAPHIC CURVES AND
 TRANSIT TIME ASSESSMENT
     Dr V Sivasubramaniyan
    Dept. of Nuclear Medicine
             SSSIHMS
                *
       Dr G Venkataraman
      Mr Hari Nanda Kumar
              SSSIHL
                *
AIMS
• REPORT INDIGENOUS SOFTWARE

• CREATED BY OUR OWN STUDENTS

• Renal Parenchymal Transit Time Analysis

• UTILITY & CLINICAL SIGNIFICANCE
DECONVOLUTION ANALYSIS
     OF RENOGRAM.
•Kenny et al – BMJ 1975.
•Diffey et al – JNM 1976.
•Piepsy et al – BJR 1982.
•Lupton et al – NMC 1984
•Britton et al – BJU 1987.
CONVOLUTION & DECONVOLUTION

• f (x) = A* exp (-a1*x) + B* exp (-a2*x)
• g (x) = 500./1 + exp {(x-xg) / x01}

Convolution = Viewing ‘g(x)’ via the lens
 ‘f(x)’ or vice versa.

Deconvolution = Deriving ‘g(x)’ from
 known ‘h(x)’ and ‘f(x)’.
METHODOLOGY
        Platform : MATLAB Ver 5.0

• Simulation studies.
• Deconvolution with discrete data
  sets.
• Randomness checking.
• Smoothing procedures.
• Check out analysis from literature.
ANALYTICAL STEPS

• Greaderauto

• Enter the file name of the curve

• H: 55470_k2  20021209  savescreen.2.jpg

• Enter the name of the patient

• Anushree sahoo
RESULTS
• OBSTRUCTIVE       70 8 to 15 min

• NON-OBSTRUCTIVE 60 1.8 to 2.8 min

• TOTAL CASES      130
MERITS
• INDIGENOUSLY MADE

• BASED ON NATIVE PATIENT DATA

• WORKS ON P. C PLATFORM

• EASY & EFFECTIVE
DEMERITS
• MULTIPLE STEPS INVOLVED

• OPERATOR DEPENDANT

• REPRODUCIBILITY DIFFICULT

• REQUIRES PATIENCE & TRAINING
Decon va

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Decon va

  • 1.
  • 2. DECONVOLUTION ANALYSIS OF RENOGRAPHIC CURVES AND TRANSIT TIME ASSESSMENT Dr V Sivasubramaniyan Dept. of Nuclear Medicine SSSIHMS * Dr G Venkataraman Mr Hari Nanda Kumar SSSIHL *
  • 3. AIMS • REPORT INDIGENOUS SOFTWARE • CREATED BY OUR OWN STUDENTS • Renal Parenchymal Transit Time Analysis • UTILITY & CLINICAL SIGNIFICANCE
  • 4. DECONVOLUTION ANALYSIS OF RENOGRAM. •Kenny et al – BMJ 1975. •Diffey et al – JNM 1976. •Piepsy et al – BJR 1982. •Lupton et al – NMC 1984 •Britton et al – BJU 1987.
  • 5. CONVOLUTION & DECONVOLUTION • f (x) = A* exp (-a1*x) + B* exp (-a2*x) • g (x) = 500./1 + exp {(x-xg) / x01} Convolution = Viewing ‘g(x)’ via the lens ‘f(x)’ or vice versa. Deconvolution = Deriving ‘g(x)’ from known ‘h(x)’ and ‘f(x)’.
  • 6. METHODOLOGY Platform : MATLAB Ver 5.0 • Simulation studies. • Deconvolution with discrete data sets. • Randomness checking. • Smoothing procedures. • Check out analysis from literature.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12. ANALYTICAL STEPS • Greaderauto • Enter the file name of the curve • H: 55470_k2 20021209 savescreen.2.jpg • Enter the name of the patient • Anushree sahoo
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. RESULTS • OBSTRUCTIVE 70 8 to 15 min • NON-OBSTRUCTIVE 60 1.8 to 2.8 min • TOTAL CASES 130
  • 30. MERITS • INDIGENOUSLY MADE • BASED ON NATIVE PATIENT DATA • WORKS ON P. C PLATFORM • EASY & EFFECTIVE
  • 31. DEMERITS • MULTIPLE STEPS INVOLVED • OPERATOR DEPENDANT • REPRODUCIBILITY DIFFICULT • REQUIRES PATIENCE & TRAINING