Clinical implementation of contrast-enhanced four-dimensional dual-energy computed tomography for target delineation of pancreatic cancer

Ohira, Shingo; Wada, Kentaro; Hirata, Takero; Kanayama, Naoyuki; Ikawa, Toshiki; Karino, Tsukasa; Nitta, Yuya; Isono, Motohide; Ueda, Yoshihiro; Miyazaki, Michihiko; Koizumi, Masahiko; Teshima, Teruki

doi:10.1016/j.radonc.2018.01.012

Cited by 16 publications

(5 citation statements)

References 19 publications

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“…The high image noise caused loss of CNR at the low energy level compared to that at the middle level. Additionally, previous studies for other lesion also concluded that VMIs at 60-70 keV yielded the highest quantitative index 6,18,19 In our study, as VMI at middle level (63 keV) provides the highest objective quantitative measurement (CNR) for brain metastasis in enhanced head DECT, our findings corroborate the previous results on optimal energy-selective monochromatic reconstruction of DECT data.…”

Section: Discussionsupporting

confidence: 90%

Determination of optimal virtual monochromatic energy level for target delineation of brain metastases in radiosurgery using dual-energy CT

Karino

Ohira

Kanayama

et al. 2019

The British Journal of Radiology

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Objective: Determination of the optimal energy level of virtual monochromatic image (VMI) for brain metastases in contrast-enhanced dual-energy CT (DECT) for radiosurgery and assessment of the subjective and objective image quality of VMI at the optimal energy level. Methods: 20 patients (total of 42 metastases) underwent contrast-enhanced DECT. Spectral image analysis of VMIs at energy levels ranging from 40 to 140 keV in 1 keV increments was performed to determine the optimal VMI (VMIopt) as the one corresponding to the highest contrast-to-noise ratio (CNR) between brain parenchyma and the metastases. The objective and subjective values of VMIopt were compared to those of the VMI with 120 kVp equivalent, defined as reference VMI (VMIref, 77 keV). The objective measurement parameters included mean HU value and SD of tumor and brain parenchyma, absolute lesion contrast (LC), and CNR. The subjective measurements included five-point scale assessment of “overall image quality” and “tumor delineation” by three radiation oncologists. Results: The VMI at 63 keV was defined as VMIopt. The LC and CNR of VMIopt were significantly (p < 0.01) higher than those of VMIref (LC: 37.4 HU vs 24.7 HU; CNR: 1.1 vs 0.8, respectively). Subjective analysis rated VMIopt significantly (p < 0.01) superior to VMIref with respect to the overall image quality (3.2 vs 2.9, respectively) and tumor delineation (3.5 vs 2.9, respectively). Conclusion: The VMI at 63 keV derived from contrast-enhanced DECT yielded the highest CNR and improved the objective and subjective image quality for radiosurgery, compared to VMIref. Advances in knowledge: This paper investigated for the first time the optimal energy level of VMI in DECT for brain metastases. The findings will lead to improvement in tumor visibility with optimal VMI and consequently supplement accuracy delineation of brain metastases.

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Section: Discussionsupporting

confidence: 90%

Determination of optimal virtual monochromatic energy level for target delineation of brain metastases in radiosurgery using dual-energy CT

Karino

Ohira

Kanayama

et al. 2019

The British Journal of Radiology

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“…The acquisition parameters were a pixel count of 512 × 512, a slice thickness of 1 mm, tube voltages of 80 and 140 kVp, and a field of view of 350 mm. From the acquired CT scan data, a virtual monochromatic image at 77 keV, which provides Hounsfield unit values equivalent to those of a conventional CT scan (120 kVp), was reconstructed and used for treatment plans.…”

Section: Methodsmentioning

confidence: 99%

Dosimetric effect of rotational setup errors in stereotactic radiosurgery with HyperArc for single and multiple brain metastases

Sagawa

Ohira

Ueda

et al. 2019

J Applied Clin Med Phys

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PurposeIn stereotactic radiosurgery (SRS) with single‐isocentric treatments for brain metastases, rotational setup errors may cause considerable dosimetric effects. We assessed the dosimetric effects on HyperArc plans for single and multiple metastases.MethodsFor 29 patients (1–8 brain metastases), HyperArc plans with a prescription dose of 20–24 Gy for a dose that covers 95% (D95%) of the planning target volume (PTV) were retrospectively generated (Ref‐plan). Subsequently, the computed tomography (CT) used for the Ref‐plan and cone‐beam CT acquired during treatments (Rot‐CT) were registered. The HyperArc plans involving rotational setup errors (Rot‐plan) were generated by re‐calculating doses based on the Rot‐CT. The dosimetric parameters between the two plans were compared.ResultsThe dosimetric parameters [D99%, D95%, D1%, homogeneity index, and conformity index (CI)] for the single‐metastasis cases were comparable (P > 0.05), whereas the D95% for each PTV of the Rot‐plan decreased 10.8% on average, and the CI of the Rot‐plan was also significantly lower than that of the Ref‐plan (Ref‐plan vs Rot‐plan, 0.93 ± 0.02 vs 0.75 ± 0.14, P < 0.01) for the multiple‐metastases cases. In addition, for the multiple‐metastases cases, the Rot‐plan resulted in significantly higher V10Gy (P = 0.01), V12Gy (P = 0.02), V14Gy (P = 0.02), and V16Gy (P < 0.01) than those in the Ref‐plan.ConclusionThe rotational setup errors for multiple brain metastases cases caused non‐negligible underdosage for PTV and significant increases of V10Gy to V16Gy in SRS with HyperArc.

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“…Thus, the DECT system can provide a more stable CT number than the standard SECT scanners, thereby leading to a more accurate dose calculation with the same radiation dose . Moreover, the DECT system can reduce the effect of contrast materials and improve the image quality compared with SECT scanners. For these reasons, we obtained planning CT images using the DECT scanner for all patients.…”

Section: Methodsmentioning

confidence: 99%

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Koike¹,

Ohira²,

Akino

et al. 2019

Medical Physics

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Purpose The aim of this study was to develop a deep learning (DL) method for generating virtual noncontrast (VNC) computed tomography (CT) images from contrast‐enhanced (CE) CT images (VNCDL) and to evaluate its performance in dose calculations for head and neck radiotherapy in comparison with VNC images derived from a dual‐energy CT (DECT) scanner (VNCDECT). Methods This retrospective study included data for 61 patients who underwent head and neck radiotherapy. All planning CT images were obtained with a single‐source DECT scanner (80 and 140 kVp) with rapid kVp switching. The DL‐based method used a pair of virtual monochromatic images (VMIs) at 70 keV with and without contrast materials. VMIs without contrast materials were used as reference true noncontrast (TNC) images. Deformable image registration was used between the TNC and CE images. We used the data of 45 patients, chosen randomly, for training (7922 paired images), and data from the other 16 patients as test data. We generated the VNCDL images with a densely connected convolutional network. As the VNCDECT images, we used VMIs with the iodine signal suppressed, reconstructed from the CE images of the 16 test patients. The CT numbers of the tumor, common carotid artery, internal jugular vein, muscle, fat, bone marrow, cortical bone, and mandible of each VNC image were compared with those of the TNC image. The dose of the reference TNC plan was recalculated using the CE, VNCDL, and VNCDECT images. Difference maps of the dose distributions and dose–volume histograms were evaluated. Results The mean prediction time for the VNCDL images was 3.4 s per patient, and the mean number of slices was 204. The absolute differences in CT numbers of the VNCDL images were significantly smaller than those of the VNCDECT images for the bone marrow (8.0 ± 6.5 vs 175.1 ± 40.9 HU; P < 0.001) and mandible (20.3 ± 19.3 vs 106.2 ± 80.5 HU; P = 0.002). The DL‐based model provided the dose distribution most similar to that of the TNC plan. With the VNCDECT plans, dose errors >1.0% were observed in bone regions. The dose–volume histogram analysis showed that the VNCDL plans yielded the smallest errors for the primary target, although dose differences were <1.0% for all the approaches. For the maximum dose to the mandible, the mean ± SD errors for the CE, VNCDL, and VNCDECT plans were –0.13% ± 0.23% (range: −0.46% to 0.31%; P = 0.037), –0.01% ± 0.22% (range: −0.40% to 0.36%; P = 1.0), and 0.53% ± 0.47% (range: −0.21% to 1.41%; P < 0.001), respectively. Conclusions In this study, we developed a method based on DL that can rapidly generate VNC images from CE images without a DECT scanner. Compared with the DECT approach, the DL‐based method improved the prediction accuracy of CT numbers in bone regions. Consequently, there was greater agreement between the VNCDL and TNC plan dose distributions than with the CE and VNCDECT plans, achieved by suppressing the contrast material signals while retaining the CT numbers of bone structures.

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Clinical implementation of contrast-enhanced four-dimensional dual-energy computed tomography for target delineation of pancreatic cancer

Cited by 16 publications

References 19 publications

Determination of optimal virtual monochromatic energy level for target delineation of brain metastases in radiosurgery using dual-energy CT

Determination of optimal virtual monochromatic energy level for target delineation of brain metastases in radiosurgery using dual-energy CT

Dosimetric effect of rotational setup errors in stereotactic radiosurgery with HyperArc for single and multiple brain metastases

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

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