Evidence map›Paper›PMID 36066364›Full record

ArticleRadiology2023

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.

Francis I Baffour, Nathan R Huber, Andrea Ferrero, Kishore Rajendran, Katrina N Glazebrook, Nicholas B Larson, Shaji Kumar, Joselle M Cook, Shuai Leng, Elisabeth R Shanblatt and 2 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
5.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 72 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Francis I BaffourFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0001-8005-4823
Nathan R HuberFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0002-6285-789X
Andrea FerreroFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).
Kishore RajendranFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0002-8314-3642
Katrina N GlazebrookFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0002-3089-9999
Nicholas B LarsonFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0002-3468-4215
Shaji KumarFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0001-5392-9284
Joselle M CookFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0001-5335-9533
Shuai LengFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0002-6453-9481
Elisabeth R ShanblattFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0003-3272-0590
Cynthia H McColloughFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0002-5346-332X
Joel G FletcherFrom the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).ORCID 0000-0002-8941-5434
Mayo Clinic in Florida · US

Funding

Comprehensive characterization of coronary atherosclerotic disease using photon-counting-detector dual-source CT and its impact on patient managementR01EB028590 · NIBIB · MAYO CLINIC ROCHESTER · PI LENG, SHUAI, MCCOLLOUGH, CYNTHIA H · 2020 to 2023
$2.6M
NIBIB NIH HHS R01 EB028590
6 · The paper itself

Abstract

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatial resolution in whole-body low-dose CT scans for viewing multiple myeloma by using PCD CT with deep learning denoising compared with conventional EID CT. Materials and Methods Between April and July 2021, adult participants who underwent a whole-body EID CT scan were prospectively enrolled and scanned with a PCD CT system in ultra-high-resolution mode at matched radiation dose (8 mSv for an average adult) at an academic medical center. EID CT and PCD CT images were reconstructed with Br44 and Br64 kernels at 2-mm section thickness. PCD CT images were also reconstructed with Br44 and Br76 kernels at 0.6-mm section thickness. The thinner PCD CT images were denoised by using a convolutional neural network. Image quality was objectively quantified in two phantoms and a randomly selected subset of participants (10 participants; median age, 63.5 years; five men). Two radiologists scored PCD CT images relative to EID CT by using a five-point Likert scale to detect findings reflecting multiple myeloma. The scoring for the matched reconstruction series was blinded to scanner type. Reader-averaged scores were tested with the null hypothesis of equivalent visualization between EID and PCD. Results Twenty-seven participants (median age, 68 years; IQR, 61-72 years; 16 men) were included. The blinded assessment of 2-mm images demonstrated improvement in viewing lytic lesions, intramedullary lesions, fatty metamorphosis, and pathologic fractures for PCD CT versus EID CT (

Indexed as

Deep LearningMultiple MyelomaAdultAgedFemaleHumansMaleMiddle AgedPhantoms, ImagingPhotonsTomography, X-Ray Computed

Identifiers

PMID36066364
PMCPMC9771909
OpenAlexW4294756236

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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.