Computed
TomographyWhite paper

AI for significantly lower doseand improved image quality
Precise ImageOverview
Philips Precise Image is a novel Philips approach that uses AI* for images
with an appearance that more closely resembles that of typical filtered backprojection images while retaining the noise-reduction capabilities of advanced
iterative reconstruction methods. This provides high-quality images
with a familiar appearance, and at low dose.

Background
Filtered back projection (FBP) was the industry standardfor CT image reconstruction for decades. While it is avery fast method, FBP is a suboptimal algorithm choicefor poorly sampled data or for cases in which noise
overwhelms the image signal, as is the case with low-doseor tube-power–limited acquisitions. Over time, incremental
enhancements have been made to FBP to overcome someof its inherent limitations.
Philips previously introduced a hybrid approachof Philips CT systems, iterative reconstruction has provided(iDose ) and a model-based approach (IMR) to iterativea unique approach to managing important factors inreconstruction to help personalize image quality based

patient care, such as imaging at low energy, low radiation

on individual patient needs at low dose. When used

and low dose.

4in combination with the advanced technologies* According to the definition of AI from the EU High-Level Expert Group.

Traditional algorithms for iterative reconstruction

Now AI has provided the advances that maketypically penalize noisy images in some fashion,possible the next level of dose-reduction technologies,

usually through a function of differences between

combining low dose with more familiar image appearance.

neighboring voxels in the image. While effective in

AI deep-learning reconstruction is trained to quickly

reducing noise, these penalty functions can produce

yield low-noise images from low-dose scans by comparing

an image appearance or noise texture that differs

them to conventional-dose images in a supervised

substantially from the appearance of traditional FBPAI learning process. This supervised learning allows forimages, which have been familiar to many radiologists

an image with a noise texture that more closely resembles

over the years. This non-standard image appearancea typical FBP image, while retaining the noise-reduction

is a significant barrier to adoption of the technology

capabilities of iterative reconstruction methods.

for lowering dose across a range of clinical applications.
While Philips IMR has addressed the computationalburden of model-based reconstruction and its effects
on reconstruction time, computational burden has
remained an issue for many manufacturers.

Philips CT Smart Workflow
Precise Image is one of the many AI-enabled tools of CT Smart Workflow,which includes AI that is deeply embedded into tools clinicians use every
day to be able to apply their expertise to the patient, not the process.

How Precise Image trains neural networks
Precise Image follows a supervised learning process to train a convolutional neural network (CNN) in a specified manner.

Routine-dose
scan data

Routine-dosetarget imageFBP
reconstruction

Low-dose
stimulation
techniqueLow-dosescan data

2.			 Generates low-dose scan data from the2				 routine-dose data by a sophisticated low-dose 						 simulation technique that accurately models both				 photon and electronic noise in low-dose scans.1

CNN

Pre-processing

Figure 1 The training process for Precise Image AI reconstruction.

2				Acquires
data from routine-dose clinical scans.
13.			 Reconstructs routine-dose scan data with3				 a traditional FBP technique.
4.			
4 Trains the CNN to reproduce the image				 appearance of the routine-dose FBP images				 with low-dose scan data.

A closer look at deep learning
Deep Learning is a subcategory of machine learning and AI.

The network is then given the task of replicating the image

A deep neural network (DNN) is an artificial neural network

appearance of the routine-dose images from the low-dosewith artificial neurons or nodes arranged in multiple layers

input. By training the networks in this way, they are more

between the input and output layers of mathematicalrobust to the variety inherent in CT from factors such as

manipulation. Complex DNNs, such as those of Precise

applied radiation dose, patient size and patient anatomy.

Image, have many layers and the ability to model complex
non-linear relationships. The design of a DNN acts as

Validating the neural network

the foundation that will allow the network to achieve its

Trained Precise Image neural networks are validated using

optimization target in an efficient manner. With Precise

patient data obtained with a variety of scan parameters

Image, the network was designed to address the specificfrom a diverse population. Philips begins by providing

challenges of image reconstruction and has optimized thelow-dose data simulated from routine-dose scans as input

number of nodes and layers within the network in a way

to the neural networks. The resulting low-dose images

that addresses the need for reduced latency and fastof Precise Image are compared to routine-dose images

runtime while solving the complex optimization challenge.

reconstructed using standard methods. When image qualityof low-dose images of Precise Image meets or exceeds

Training the neural network

routine-dose standard reconstructions, sufficient training

While a well-designed DNN presents a great deal of promise
in solving complex optimization problems, it is importantof the neural network is confirmed.

it has been provided. Correctly done, a supervised training

Inference allows for fast
clinical workflows

strategy involves assembling a set of inputs and outputs

Once networks have been trained, the weights of the nodes

that provide a sufficient sampling of the problem space to beand layers of the DNN are fixed. This means new inputs insolved. A well-reasoned and thorough approach at this point

the form of patient data can be rapidly processed to support

is critical for achieving robustness of the network. To train

high-throughput clinical workflows with the improved

Precise Image neural networks, we begin with routine-dose

diagnostic confidence delivered by Precise Image.

scans with a clinically desired image appearance. From there,

With the smart design of the network as the foundation

low-dose scan data is simulated in a way that accurately

and the robust training complete, Precise Image delivers

models both photon and electronic noise.

the fastest AI-based reconstruction in the industry.

to realize that it is only as good as the training with which

AI-enabled image reconstruction
Average reconstruction times for common protocols

Philips Precise Image is the newest,
image reconstruction, using recent
technological leaps in AI. Precise
Image is a reconstruction techniquethat uses a trained deep-learningneural network. Precise Image
offers the industry’s fastest
reconstruction speed while
maintaining the conventional
appearance of FBP images.

Total reconstruction time (seconds)

most robust method of Philips CT403020100Abdomen

Brain CTA

Chest/Abd/Pelvis

Brain Helical

Chest PE

Figure 2 Precise Image allows for average reconstruction times of 30 seconds
or less for common protocols.

3Going beyond phantom studies to clinical data
Philips Precise Image has been extensively tested on both phantom andclinical data. Many general image quality metrics are computed using phantom
images. However, Precise Image uses primarily clinical images in the training
procedure, rather than phantom images, to ensure that networks are not trained
to simply give good results on performance phantoms, but to provide improved
clinical images. Nevertheless, these clinical benefits can also be measured on
traditional phantoms with excellent results, as shown in the following sections.

Noise-power spectrum
A common complaint with iterative reconstruction images

A nearly constant normalized NPS can be maintainedis that the noise texture differs significantly from FBP

with Precise Image – regardless of the magnitude ofimages. Precise Image is trained to reproduce the noisethe noise reduction – that closely matches the NPS given

texture of FBP, while at the same time delivering significant

by FBP reconstruction. Thus, image noise texture cannoise reductions. An established metric for quantifying

be customized to closely match that of FBP images, evennoise texture is the noise-power spectrum (NPS). For thisfor low doses and strong levels of noise reduction (Figure 4).

measurement, a 30 cm water phantom was scanned at
300 mAs, and again at 100 mAs. Images for Precise Image
were generated from the 100 mAs scan with increasing
noise reduction to create images with high image quality
and reduced noise. A series of normalized NPS values werethen computed for each of the images for Precise Image,as well as for the high-dose FBP image (Figure 3).

NPS curve

MTF curve
100FBP
iDose4
Precise Image

0.20
0.15
0.10
0.05
001234567

lp/cm
Figure 3 Normalized noise-power spectrum measurements froma 30 cm water phantom.
4

Modulation-transfer function

Noise-power spectrum

0.25

8FBP
iDose4
Precise Image

9080
7060504030
201000246810121416

lp/cm
Figure 4 Resolution expressed as modulation-transfer function
comparison of FBP and AI-enabled reconstruction.

Low-contrast detectability
A low-contrast detectability (LCD) test is an established

The LCD test for Precise Image uses the MITA low-contrast

method for measuring the dose reduction capabilities ofphantom CT 189 and focuses on the 10 mm diameter,

reconstruction algorithms. A human or model observer

3 HU contrast pin. The model observer is a channelized

is presented with many different noisy images, some

Hotelling observer (CHO) with 3-DOG channels, as

containing a known low-contrast object and some withdescribed in the IQmodelo tool.2 We use 200 image pairs

no object present, and for each image the observer must

(object present, object absent), and compare the d-prime

decide if the object is present or not. Success at making

of FBP at a dose of 10 mGy to Precise Image at 4 mGy

the correct determination for each noisy image is measured,and 2 mGy (60% and 80% dose reduction, respectively).

and these scores can be used to derive a detectability index

Example images can be compared.

(d-prime) that reflects the statistical success of detecting
the object with a given dose and reconstruction method.

Results of the LCD test show detectability withA d-prime = 0 corresponds to no better than random

Precise Image at 4 mGy is more than 80% betterguessing (AUC = 0.5), while a d-prime = 4.38 corresponds

than FBP at 10 mGy. Detectability with Precise Image

to nearly perfect detectability (AUC = 0.999). “AUC” is theat 2 mGy is more than 43% better than FBP at 10 mGy.

area under the receiver operating characteristic curveThis test shows that with Precise Image, users can get

and is a measure of how well a system can discriminate

both significant dose reduction and greatly improved

between two categories.

low-contrast imaging at the same time, all while retaining
a more traditional noise texture than with other recent
reconstruction techniques.

5

Clinical studies and example images
A team of experienced radiologists reviewed images

For each attribute assessed, ratings from the two imageof the chest, abdomen and pelvis from 40 patients using

sets were compared using a two-sample Welch’s t-test

iDose4 and Precise Image. Both image sets for each patient(α=5%) to check for statistically significant differences in thewere rated for diagnostic confidence, sharpness, noiseratings. Results showed an improvement in each attributelevel, image texture and artifacts on a 5-point Likert scale,with images from Precise Image reconstructed

where 1 was the worst and 5 was the best. All scans wereat 50% of the acquired dose (Figure 6).

performed at routine dose levels, and iDose4 images werereconstructed at the acquired dose. Images using Precise
Image were reconstructed at 50% of the routine acquired
dose using low-dose simulation techniques.

Precise Image improves diagnostic confidence at half the dose
5iDose44PreciseImage

3210Sharpness

Noise level

Image textureArtifacts

Figure 6 Image-quality ratings for Precise Image reconstructed at 50% of the routine dose were higher thanthose for iDose4 images reconstructed at 100% of the routine dose.

Clinical image comparisons

iDose4 7.4 mSv
6Precise Image 3.7 mSv

iDose4 6.6 mSv

Precise Image 3.3 mSv

iDose4 1.4 mSv

iDose4 1.5 mSv

iDose4 1.8 mSv

Precise Image 0.7 mSv

iDose4 5.1 mSv

Precise Image 2.6 mSv

Precise Image 0.75 mSv

iDose4 5.4 mSv

Precise Image 2.6 mSv

Precise Image 0.8 mSv

Precise Image CTAConclusion
Precise Image offers a significant advance in the speed of CT image reconstruction at lowdose, producing images with a noise texture that more closely resembles a typical FBP image.
Results of the clinical evaluation demonstrated that images reconstructed with Precise Image offer
a significant advance in CT image reconstruction at half the dose, compared to iDose4 images.

7References
1 Žabic S, Wang E, Morton T, Brown KM. A low dose simulation tool for CT systems with energy integrating detectors.
Med Phys. 2013;40(3):1–14. DOI: 10.1118/1.4789628.
2. Wunderlich A, et al. Exact confidence intervals for channelized Hotelling observer performance in image quality studies.
IEEE Trans Med Imaging. 2015;34.2:453-464. DOI: 10.1109/TMI.2014.2360496. PMCID: PMC5542023.

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