Clinical Implementation of Dual-Energy CT for Gastrointestinal Imaging

Mileto, Achille; Ananthakrishnan, Lakshmi; Morgan, Daniel J; Yeh, Benjamin M.; Marin, Daniele; Kambadakone, Avinash

doi:10.2214/ajr.20.25093

Cited by 43 publications

(14 citation statements)

References 82 publications

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“…As a result, noise increases in the material decomposition images, and the quantitative accuracy reduces [29]. Since the tube current for the lower energy spectra of the rapid kVp switching DECT variant remains fixed, photon starvation artifacts and increased noise are commonly observed in patients who weigh more than 250 pounds or in scenarios where the arms cannot be raised above a patient's head for body exams [29,54]. The impact of noise on the proposed method was observed in Figure 6d, where a shield placed over the abdomen attenuated X-rays, which then increased noise throughout the organs under the shield.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

et al. 2022

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We map single energy CT (SECT) scans to synthetic dual-energy CT (synth-DECT) material density iodine (MDI) scans using deep learning (DL) and demonstrate their value for liver segmentation. A 2D pix2pix (P2P) network was trained on 100 abdominal DECT scans to infer synth-DECT MDI scans from SECT scans. The source and target domain were paired with DECT monochromatic 70 keV and MDI scans. The trained P2P algorithm then transformed 140 public SECT scans to synth-DECT scans. We split 131 scans into 60% train, 20% tune, and 20% held-out test to train four existing liver segmentation frameworks. The remaining nine low-dose SECT scans tested system generalization. Segmentation accuracy was measured with the dice coefficient (DSC). The DSC per slice was computed to identify sources of error. With synth-DECT (and SECT) scans, an average DSC score of 0.93±0.06 (0.89±0.01) and 0.89±0.01 (0.81±0.02) was achieved on the held-out and generalization test sets. Synth-DECT-trained systems required less data to perform as well as SECT-trained systems. Low DSC scores were primarily observed around the scan margin or due to non-liver tissue or distortions within ground-truth annotations. In general, training with synth-DECT scans resulted in improved segmentation performance with less data.

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Section: Discussionmentioning

confidence: 99%

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

et al. 2022

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“…Isolated small bowel perforations are rare and often develop after penetrating ileus, mesenteric ischemia, traumas, malignancies, and rarely after radiotherapy [ 19 ]. In the literature, evaluation studies of DECT, which involve small bowel pathologies, are increasing day by day [ 4 , 20 , 21 ]. It was underscored in the study of Gosangi et al, which examined the radiological findings of duodenal pathologies, that DECT could be a good alternative [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

The Role of Dual-Energy Computed Tomography in Locating Gastrointestinal Tract Perforations

Baş

Zarbaliyev

2021

Cureus

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Objective With each passing day, dual-energy computed tomography (DECT) is being used more frequently in the evaluation of abdominal pathologies. In this article, we aimed to assess the role of dual-energy CT in locating gastrointestinal perforations, which are among the causes of acute abdomen. Materials and methods All patients who underwent DECT due to acute abdomen in a COVID-19 designated hospital between June 1st, 2020 and December 31st, 2020, who were found to have gastrointestinal tract (GIT) perforation and underwent surgery were included in the study. DECT results and intraoperative findings of the patients were compared. Results Thirteen patients (nine males and four females) who underwent DECT for acute abdomen and were diagnosed with perforation in the gastrointestinal system were included in the study. The mean age of the patients was 57.6 years (range: 11-85 years). Two patients had gastric perforation, three had duodenal perforations, and one patient had a perforation in the gallbladder wall. Two patients were diagnosed with jejunal perforations, one patient with Meckel's diverticulum, and three patients with colorectal perforation. Although free air was detected in the abdomen of one patient, perforation could not be located. In patients with GIT perforation who were operated on following DECT imaging, the perforation location shown on DECT correlated 100% with the perforation locations detected during surgery. Conclusion DECT is significantly effective in planning surgical treatment and determining the foci of perforation in GIT perforations.

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“…Virtual monoenergetic images (VMI) represent the standard type of reconstructed images for PCD CT given the technique's inherent multienergy capabilities. However, VMI reconstruction alters tissue attenuation in comparison with conventional polychromatic single-energy reconstruction [19][20][21][22][23]. Thus, the influence of keV levels on the attenuation values of EAT needs further investigation.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Epicardial Adipose Tissue Attenuation and Fat Attenuation Index: Phantom Study and In Vivo Measurements With Photon-Counting Detector CT

Mergen

Ried

Allmendinger

et al. 2022

American Journal of Roentgenology

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The publication of this Accepted Manuscript is provided to give early visibility to the contents of the article, which will undergo additional copyediting, typesetting, and review before it is published in its final form. During the production process, errors may be discovered that could affect the content of the Accepted Manuscript. All legal disclaimers that apply to the journal pertain. The reader is cautioned to consult the definitive version of record before relying on the contents of this document.

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Clinical Implementation of Dual-Energy CT for Gastrointestinal Imaging

Cited by 43 publications

References 82 publications

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

The Role of Dual-Energy Computed Tomography in Locating Gastrointestinal Tract Perforations

Epicardial Adipose Tissue Attenuation and Fat Attenuation Index: Phantom Study and In Vivo Measurements With Photon-Counting Detector CT

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