The Use of Model-based Iterative Reconstruction to Optimize Chest CT Examinations for Diagnosing Lung Metastases in Patients with Sarcoma

Kaasalainen, Touko; Mäkelä, Teemu; Kelaranta, Anna; Kortesniemi, Mika

doi:10.1016/j.acra.2018.03.028

Cited by 4 publications

(8 citation statements)

References 47 publications

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“…Similar results have been demonstrated in earlier studies (44, 46, 57). Veo had the lowest noise frequencies, since it did not work together with a chest kernel, but studies show that despite this, an improvement in spatial resolution and reticular lung patterns, are seen (57,117). This will improve the visualisation of vessels, even though this was not seen for the smallest vessels in our study.…”

Section: Chestmentioning

confidence: 72%

“…Improved lesion detection compared to filtered back projection is found for all algorithms. In phantoms Kaasalainen et al (117) The main part of the studies showing no improvement in lesion detection with iterative reconstruction, are abdominal examinations where liver lesions are evaluated. These are sensitive to changes in noise power spectrum and degradation of sharpness.…”

Section: Discussionmentioning

confidence: 99%

“…Spatial resolution is not evaluated quantitatively in these studies, but others have found changes in spatial resolution with iterative reconstruction. Kaasalainen et al (117) found decreased full width half maximum using Veo on soft tissue plugs against lung tissue plugs. Imaging air plugs against lung tissue plugs, the full width half maximum increased using Veo.…”

Section: Discussionmentioning

confidence: 99%

“…Applying Veo in such a situation will not improve the image quality further. Veo was one of the algorithms with the largest shift towards lower noise frequencies, but at the same time spatial resolution was improved compared to filtered back projection (117). This is beneficial for detection of small lesions or lesions with low contrast and may prevent small lesions from being masked by low frequency noise.…”

Section: Livermentioning

confidence: 99%

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Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study

Jensen

Hagemo

Tingberg

et al. 2020

J Comput Assist Tomogr

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I would like to thank my main supervisor, Anne Catrine Martinsen, for facilitating the opportunity to start and complete this PhD project. Without you this would not have been possible. Thank you for all the ideas, valuable feedback, discussions and support. Even though your schedule is more than full, you have always had time when I have needed help. Your optimism is invigorating and the engagement and initiative you have had for medical physicists and physics in radiology are impressive. It has been interesting to follow the tasks you have decided to pursue, and now you will pursue something quite different. Good luck and the best wishes! Thank you to my co-supervisors, Anders Tingberg and Erik Fosse. Your different insights and experiences in medical technology, imaging and research are valuable, and I have appreciated your feedback both on content, structure and writing. You have also been encouraging and supportive all the way, and I have always felt uplifted after meetings. Also, a big thank you to Örjan Smedby who introduced me to visual grading regression and STATA, and to Atle Bjørnerud who stepped in as supervisor at the last minute.In addition to my supervisors I have had help with scanning phantoms and writing articles. To have access to firsthand knowledge on CT scanners, anatomy and the radiology field makes my day more interesting. Thank you to all my co-authors and scanning help. Ingrid Helen Hauge and Øystein Bech Gadmar, thank you for proofreading of articles and this dissertation. Your comments have been very helpful.Lunch and coffee breaks are important for thinking, and I am very grateful for having so many nice colleagues always making these breaks fun and enjoyable. A special thanks to Hilde Kjernlie Andersen for facilitating my workday so I could finish this thesis, and to all the physicists at the Department of diagnostic physics. No matter what problems there are, you are always willing to help, and I am grateful to work with you.Thank you to my parents and brother who made my childhood safe, yet still eventful, and gave me the prerequisites to utilize the opportunities brought by life. And to my closest, Marius, Joakim and Hedda; I look forward to every day with you. List of papers1. Comparing five different iterative reconstruction algorithms for computed tomography in an ROC study.

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Section: Chestmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Livermentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study

Jensen

Hagemo

Tingberg

et al. 2020

J Comput Assist Tomogr

View full text Add to dashboard Cite

show abstract

“…In children referred to emergency noncontrast head CT, iterative model reconstruction on average reduce 22% relative dose, compared with FBP, with significantly improved objective and subjective image quality (Elmokadem et al, 2019). Radiation exposure in patients can be reduced significantly (mean absorbed organ and effective doses were reduced by approximately 95%) using low-dose chest CT protocols and model-based iterative reconstruction (MBIR) algorithm while maintaining image quality for detecting round-shaped lung metastases (Kaasalainen et al, 2019). A 1-D wavelet transform-based multiscale image reconstruction technique was introduced by Bhatia et al (1996) using FBP coefficients in expanded form with 1-D wavelet basis.…”

Section: Introductionmentioning

confidence: 99%

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography

et al. 2022

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The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.

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Ultra-low-dose computed tomography and chest X-ray in follow-up of high-grade soft tissue sarcoma—a prospective comparative study

Salminen,

Jäämaa,

Nevala

et al. 2024

Sci Rep

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Ultra-low-dose computed tomography (ULD-CT) may combine the high sensitivity of conventional computed tomography (CT) in detecting sarcoma pulmonary metastasis, with a radiation dose in the same magnitude as chest X-ray (CXR). Fifty patients with non-metastatic high-grade soft tissue sarcoma treated with curative intention were recruited. Their follow-up involved both CXR and ULD-CT to evaluate their different sensitivity. Suspected findings were confirmed by conventional CT if necessary. Patients with isolated pulmonary metastases were treated with surgery or stereotactic body radiation therapy (SBRT) with curative intent if possible. The median effective dose from a single ULD-CT study was 0.27 mSv (range 0.12 to 0.89 mSv). Nine patients were diagnosed with asymptomatic lung metastases during the follow-up. Only three of them were visible in CXR and all nine in ULD-CT. CXR had therefore only a 33% sensitivity compared to ULD-CT. Four patients were operated, and one had SBRT to all pulmonary lesions. Eight of them, however, died of the disease. Two patients developed symptomatic metastatic recurrence involving extrapulmonary sites+/−the lungs between two imaging rounds. ULD-CT has higher sensitivity for the detection of sarcoma pulmonary metastasis than CXR, with a radiation dose considerably lower than conventional CT.Clinical trial registration: NCT05813808. 04-14-2023.

show abstract

The Use of Model-based Iterative Reconstruction to Optimize Chest CT Examinations for Diagnosing Lung Metastases in Patients with Sarcoma

Abstract: Radiation exposure in patients can be reduced significantly by using LD chest CT protocols and MBIR algorithm while maintaining image quality for detecting round-shaped lung metastases.

Cited by 4 publications

References 47 publications

Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study

Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography

Ultra-low-dose computed tomography and chest X-ray in follow-up of high-grade soft tissue sarcoma—a prospective comparative study

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