Image Quality for Sub-milli Sievert Chest CT Examinations: A - - PowerPoint PPT Presentation

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Image Quality for Sub-milli Sievert Chest CT Examinations: A - - PowerPoint PPT Presentation

Sep 11, 2023 801 likes •1.04k views

HARVARD MEDICAL SCHOOL Role of Massive-Training Artificial Neural Network {MTANN} Algorithm in Radiation Dose Reduction and Image Quality for Sub-milli Sievert Chest CT Examinations: A Preliminary Study Tabari A, MD Singh S , MD Fintelmann

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SLIDE 1

HARVARD MEDICAL SCHOOL

Role of Massive-Training Artificial Neural Network {MTANN} Algorithm in Radiation Dose Reduction and Image Quality for Sub-milli Sievert Chest CT Examinations: A Preliminary Study

Tabari A, MD Singh S , MD Fintelmann FJ, MD McDermott S, MD Gee MS, MD

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SLIDE 2

Financial Disclosure

  • None of the authors had any financial relationship pertinent to the

study

Ethical Committee Approval

  • The study was approved by the IRB and was compliant with HIPAA

guidelines

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SLIDE 3

Background

Decrease in radiation dose

Image processing Algorithm Increased image noise versus standard dose No Image processing Constant / less increased noise versus standard dose Lowered Diagnostic Confidence Acceptable Diagnostic Confidence

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SLIDE 4

Pi PixelSh elShine ine - De Deep ep Lea earning rning

  • Can map any complex “functions”

function

noisy clean

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SLIDE 5

Pi Pixel elShi Shine ne - Deep

p Lear arni ning ng

  • Learns from training examples

noisy clean

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SLIDE 6

Pi Pixel elShi Shine ne - Deep

p Lear arni ning ng

  • A lot of computation for training

Adjust weights by repeated training

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SLIDE 7

Pi Pixel elShi Shine ne - Deep

p Lear arni ning ng

Once training is done,

– Process images using fixed weights – Processing is fast*

*Processing time: about 8 slices / sec

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SLIDE 8
  • Processes reconstructed images
  • Is CT vendor & recon algorithm

agnostic Pi Pixel elShi Shine ne - Deep

p Lear arni ning ng

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SLIDE 9

PURPOSE

To evaluate Massive-Training artificial neural network {Pixelshine} algorithm and Filter Back Projection (FBP) reconstruction techniques for 27% radiation dose reduction and image quality for chest CT

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SLIDE 10

Materials and Methods

  • In an IRB approved and HIPAA study,
  • 13 patients (mean age 63.9 ± 11 years, M:F 9:4, weight 174.8

± 38 lbs)

  • underwent “routine” chest CT with standard and low dose.
  • Patients were scanned on
  • 128 slice MDCT SOMATOM Definition Flash
  • Discovery 750HD
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SLIDE 11

Materials and Methods

  • All scanning parameters, except tube current, were held

constant including

– 120 kVp – 0.984:1 pitch – 39.37 mm table speed per gantry rotation – 0.5 second gantry rotation time – Detail reconstruction kernel-CT chest

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SLIDE 12

Materials and Methods

  • Detailed CT image quality, including
  • bjective image noise,
  • Hounsfield Unit values and
  • contrast to noise ratio (CNR)
  • were measured in
  • thoracic aorta,
  • pectoral muscles,
  • para-spinal muscles,
  • air outside the thoracic cavity.
  • Standard dose images were considered as the reference standard for image

quality and statistical analyses were performed using the t-test

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SLIDE 13

Materials and Methods

  • Radiation dose parameters, including
  • CTDIvol,
  • Dose Length Product and
  • estimated effective dose was calculated as per ICRP103.
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SLIDE 14

– Standard and low dose chest CT examinations were performed for clinical indications, including – metastasis evaluation, – pneumonia, – pulmonary obstructive disease.

  • Low dose chest CT images were acquired at 81%

– {CTDIvol 9.1±6/1.8 ± 0.2 mGy} lower dose. – DLP was 66 ± 2 mGy.cm and 322.7 ± 217 mGy.cm, – effective dose 1 mSv and 4.8 ± 3.3 mSv, for low and standard dose, respectively.

Results

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SLIDE 15
  • Image noise was significantly decreased by 27% {62.3 ± 19/ 84.1 ± 28} in low

dose Pixelshine images as compared to low dose FBP{p <0.004}.

  • HU values were similar in low dose Pixelshine (27.5 ± 23) as compared to standard

dose (39.2 ± 10) (p> 0.1).

  • CNR was significantly improved in Pixelshine compared to standard dose FBP

images {p<0.002}

Results

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SLIDE 16

SIEMENS 100kVp 50mAs 1mm B60f FBP

Pi Pixel elShi Shine ne Lung Study

dy 1

SIEMENS 100kVp 50mAs 1mm B60f FBP + PixelShine

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SLIDE 17

SIEMENS 100kVp 26.3mAs 1mm B60f FBP

Pi Pixel elShi Shine ne Lung Study

dy 2

SIEMENS 100kVp 26.3mAs 1mm B60f FBP + PixelShine

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SLIDE 18

SIEMENS 100kVp 12.1mAs 1mm B60f FBP

Pixel elSh Shine ine Lung Study

dy 3

SIEMENS 100kVp 12.1mAs 1mm B60f FBP + PixelShine

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SLIDE 19

TOSHIBA 120kVp 5mAs 0.5mm FC05 FBP

Pi Pixel elShi Shine ne Lung Study

dy 4

TOSHIBA 120kVp 5mAs 0.5mm FC05 FBP + PixelShine

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SLIDE 20

TOSHIBA 120kVp 5mAs 0.5mm FC05 FBP

Pi Pixel elShi Shine ne Lung Study

dy 5

TOSHIBA 120kVp 5mAs 0.5mm FC05 FBP + PixelShine

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SLIDE 21

Conclusion

  • Pixelshine algorithm reconstructed CT images lowers noise

by 27% in 81% low dose images {1.8 mGy} compared to conventional FBP

  • Low dose chest CT acquired at 1.8 mGy is feasible with

Pixelshine algorithm

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SLIDE 22

Thank You