Evidence map›Paper›PMID 34796532›Full record

ArticleMedical physics2022

Dose reduction potential in diagnostic single energy CT through patient-specific prefilters and a wider range of tube voltages.

Jörg Steidel, Joscha Maier, Stefan Sawall, Marc Kachelrieß

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Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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9citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jörg SteidelMedical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld, Heidelberg, Germany.
Joscha MaierMedical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld, Heidelberg, Germany.
Stefan SawallMedical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld, Heidelberg, Germany.
Marc KachelrießMedical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeVarious studies have demonstrated that additional prefilters and/or reduced tube voltages have the potential to significantly increase the contrast-to-noise ratios at unit dose (CNRDs) and thereby to significantly reduce patient dose in clinical CT. An exhaustive analysis, accounting for a wide range of filter thicknesses and a wide range of tube voltages extending beyond the 70 to 150 kV range of today's CT systems, including their specific choice depending on the patient size, is, however, missing. Therefore, this work analyzes the dose reduction potential for patient-specific selectable prefilters combined with a wider range of tube voltages. We do so for soft tissue and iodine contrast in single energy CT. The findings may be helpful to guide further developments of x-ray tubes and automatic filter changers.

methodsCT acquisitions were simulated for different patient sizes (semianthropomorphic phantoms for child, adult, and obese patients), tube voltages (35-150 kV), prefilter materials (tin and copper), and prefilter thicknesses (up to 5 mm). For each acquisition soft tissue and iodine CNRDs were determined. Dose was calculated using Monte Carlo simulations of a computed tomography dose index (CTDI) phantom. CNRD values of acquisitions with different parameters were used to evaluate dose reduction.

resultsDose reduction through patient-specific prefilters depends on patient size and available tube current among others. With an available tube current time product of 1000 mAs dose reductions of 17% for the child, 32% for the adult and 29% for the obese phantom were achieved for soft tissue contrast. For iodine contrast dose reductions were 57%, 49%, and 39% for child, adult, and obese phantoms, respectively. Here, a tube voltage range extended to lower kV is important.

conclusionsSubstantial dose reduction can be achieved by utilizing patient-specific prefilters. Tube voltages lower than 70 kV are beneficial for dose reduction with iodine contrast, especially for small patients. The optimal implementation of patient-specific prefilters benefits from higher tube power. Tin prefilters should be available in 0.1 mm steps or lower, copper prefilter in 0.3 mm steps or lower. At least 10 different prefilter thicknesses should be used to cover the dose optima of all investigated patient sizes and contrast mechanisms. In many cases it would be advantageous to adapt the prefilter thickness rather than the tube current to the patient size, that is, to always use the maximum available tube current and to control the exposure by adjusting the thickness of the prefilter.

Indexed as

Drug TaperingTomography, X-Ray ComputedAdultChildHumansMonte Carlo MethodPhantoms, ImagingRadiation Dosagecomputed tomographyimage qualityradiation dose reductionx-ray filtration

Identifiers

PMID34796532
PMCPMC13655647

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.