A Dynamic Cardiac Phantom for the Validation of Quantitative Nuclear

Cardiology Software

Nigel Williams, Ian Hadley, Alan Williams and Elinor Vinecombe

Departments of Nuclear Medicine and Biomedical Engineering

University Hospitals of Coventry and Warwickshire NHS Trust

Nuclear Cardiology

• Benefit of acquiring gated SPECT myocardial perfusion images is now widely accepted.

• Number of software packages available for data analysis

• Aim of this study – construct a dynamic phantom to assess accuracy of analysis software

Nuclear Cardiology

• Gated MP-SPECT– 99Tcm-tetrofosmin / MIBI or Tl201

_ No. projections / acquisition time similar to non-gated study

– Camera acquisition triggered to R-wave.( 8-16 frames collected per R-R interval)

– Data processed using standard MP SPECT protocol.– Quantitative analysis of 3D data:

• Ejection Fraction• Wall Motion and Thickening

Gated-SPECT Software• Two programs at UHCW:

– 4D-MSPECT (Univ. of Michigan Medical Centre)

– QGS+ (Cedars-Sinai Medical Centre)

• Both programs may be applied to gated and un-gated SPECT myocardial perfusion studies

• For gated studies they both– Use Reconstructed SA SPECT data

– Use edge detection algorithm to define inner and outer walls of myocardium

– Map changes in inner and outer walls to quantify EDV, ESV, EF, Wall Thickening and Motion.

Manufacturer’s Software Validation

• 4D-MSPECTComparison with contrast ventriculography

• QGS+Planar first pass radionuclide ventriculography

Dynamic Cardiac Phantom• Left Ventricle

– Fillable inner chamber: latex ultrasound transducer sheath

– Volume changed using driven syringe

– Wall constructed from foam cone covered with second sheath

– Wall can be filled with solution containing 99Tcm

• Pumping Mechanism– Pumping syringe driven using pneumatic actuator controlled using

pressurised air.

– Timing of systole and diastole strokes controlled by valves: (0.33:0.66)

– Beat rate adjusted by altering flow of air.

• Triggering– Camera triggered using light sensor on syringe

Dynamic Cardiac Phantom: DevelopmentLeft Ventricle

introduce activity

Inlet to fill innervolume

Inlet to connectinginner chamber topumping mechanism

Inlet used to

into the foam wall

Sleeve

Outer wall sheath

Inner wallsheath

Foam wall

Dynamic Cardiac Phantom: Development

Pneumatic and Filling System

Cardiac Simulation

• Eight combinations of EDV and ESV to produce a range of EFs, based on limits of phantom

• Ranges: EDV: 50 - 80ml

ESV: 15 - 55ml

EF: 35 - 80%

• CT and gated SPECT data collected using each combination

• CT data collected using iodine contrast agent in inner chamber to produce contrast between wall and inner volume

CT data• 5mm transaxial slices

collected with phantom at ED and ES (data not gated)

• Data analysed using

Osiris and Matlab programme

Gated SPECT data

• Immediately after CT: gated SPECT.• 20MBq 99Tcm-DTPA (30ml) placed in wall.• Gated SPECT protocol with 16 frames per R-R interval

(204°, 34 steps per head, 3° per step, 25s per step)• Beat rate at ~60bpm 25 beats per step.• Images processed using standard MP-SPECT protocol• Reconstructed SA data analysed with two packages

4D-MSPECT

4D-MSPECT

QGS+

Results: Gated SPECT (1)

• 4D-MSPECT: EF values within ±10% of expected result

EDV and ESV volumes always underestimated

Deviation of Measued Ejection Fractions from CT Data: 4D-MSPECT

y = 0.99x + 1.61

R2 = 0.94

0

20

40

60

80

100

120

0.0 20.0 40.0 60.0 80.0 100.0 120.0

CT EF (%)

Mea

sure

d E

F (

%)

Measured

10%

-10%

Linear (Measured)

Results: Gated SPECT (2)• QGS+: EF results more variable than 4D-MSPECT

EDV and ESV very inconsistent with expect results.

Deviation of Measured Ejection Fraction from CT Data: QGS+

y = 0.88x + 1.90

R2 = 0.89

0

20

40

60

80

100

120

20.0 40.0 60.0 80.0 100.0 120.0

CT EF (%)

Me

as

ure

d E

F (

%)

Measured

10%

-10%

Linear (Measured)

Conclusion

• EDV and ESV estimates must be treated with caution.

• Compared to the QGS+ program the 4D-MSPECT appears to give a more accurate assessment of EF using phantom data

Further Work

• Analysis of CT data for wall thickening and motion.

• Comparison with g-SPECT results• Assessment of software packages for

quantification of defects– Insert different sized defects into foam cone.

– Compare to a ‘normals’ database generated using previously collected SPECT data