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Rytky SJO, Tiulpin A, Finnilä MAJ, Karhula SS, Sipola A, Kurttila V, Valkealahti M, Lehenkari P, Joukainen A, Kröger H, Korhonen RK, Saarakkala S, Niinimäki J. Clinical Super-Resolution Computed Tomography of Bone Microstructure: Application in Musculoskeletal and Dental Imaging. Ann Biomed Eng 2024; 52:1255-1269. [PMID: 38361137 PMCID: PMC10995025 DOI: 10.1007/s10439-024-03450-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data. METHODS Knee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images. RESULTS The SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available. CONCLUSION Model training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.
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Affiliation(s)
- Santeri J O Rytky
- Research Unit of Health Sciences and Technology, University of Oulu, POB 5000, 90014, Oulu, Finland.
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, University of Oulu, POB 5000, 90014, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | - Mikko A J Finnilä
- Research Unit of Health Sciences and Technology, University of Oulu, POB 5000, 90014, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu, Finland
| | - Sakari S Karhula
- Research Unit of Health Sciences and Technology, University of Oulu, POB 5000, 90014, Oulu, Finland
- Department of Radiotherapy, Oulu University Hospital, Oulu, Finland
| | - Annina Sipola
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Väinö Kurttila
- Department of Oral and Maxillofacial Surgery, Oulu University Hospital, Oulu, Finland
| | - Maarit Valkealahti
- Department of Surgery and Intensive Care, Oulu University Hospital, Oulu, Finland
| | - Petri Lehenkari
- Department of Surgery and Intensive Care, Oulu University Hospital, Oulu, Finland
- Cancer and Translational Medical Research Unit, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Antti Joukainen
- Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland
| | - Heikki Kröger
- Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland
| | - Rami K Korhonen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Simo Saarakkala
- Research Unit of Health Sciences and Technology, University of Oulu, POB 5000, 90014, Oulu, Finland
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Jaakko Niinimäki
- Research Unit of Health Sciences and Technology, University of Oulu, POB 5000, 90014, Oulu, Finland
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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Zhuang T, Parsons D, Desai N, Gibbard G, Keilty D, Lin MH, Cai B, Nguyen D, Chiu T, Godley A, Pompos A, Jiang S. Simulation and pre-planning omitted radiotherapy (SPORT): a feasibility study for prostate cancer. Biomed Phys Eng Express 2024; 10:025019. [PMID: 38241733 DOI: 10.1088/2057-1976/ad20aa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 01/19/2024] [Indexed: 01/21/2024]
Abstract
This study explored the feasibility of on-couch intensity modulated radiotherapy (IMRT) planning for prostate cancer (PCa) on a cone-beam CT (CBCT)-based online adaptive RT platform without an individualized pre-treatment plan and contours. Ten patients with PCa previously treated with image-guided IMRT (60 Gy/20 fractions) were selected. In contrast to the routine online adaptive RT workflow, a novel approach was employed in which the same preplan that was optimized on one reference patient was adapted to generate individual on-couch/initial plans for the other nine test patients using Ethos emulator. Simulation CTs of the test patients were used as simulated online CBCT (sCBCT) for emulation. Quality assessments were conducted on synthetic CTs (sCT). Dosimetric comparisons were performed between on-couch plans, on-couch plans recomputed on the sCBCT and individually optimized plans for test patients. The median value of mean absolute difference between sCT and sCBCT was 74.7 HU (range 69.5-91.5 HU). The average CTV/PTV coverage by prescription dose was 100.0%/94.7%, and normal tissue constraints were met for the nine test patients in on-couch plans on sCT. Recalculating on-couch plans on the sCBCT showed about 0.7% reduction of PTV coverage and a 0.6% increasing of hotspot, and the dose difference of the OARs was negligible (<0.5 Gy). Hence, initial IMRT plans for new patients can be generated by adapting a reference patient's preplan with online contours, which had similar qualities to the conventional approach of individually optimized plan on the simulation CT. Further study is needed to identify selection criteria for patient anatomy most amenable to this workflow.
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Affiliation(s)
- Tingliang Zhuang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - David Parsons
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Neil Desai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Grant Gibbard
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Dana Keilty
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Bin Cai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Dan Nguyen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Tsuicheng Chiu
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Andrew Godley
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Arnold Pompos
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States of America
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Bin-Alamer O, Abou-Al-Shaar H, Gersey ZC, Huq S, Kallos JA, McCarthy DJ, Head JR, Andrews E, Zhang X, Hadjipanayis CG. Intraoperative Imaging and Optical Visualization Techniques for Brain Tumor Resection: A Narrative Review. Cancers (Basel) 2023; 15:4890. [PMID: 37835584 PMCID: PMC10571802 DOI: 10.3390/cancers15194890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/26/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023] Open
Abstract
Advancements in intraoperative visualization and imaging techniques are increasingly central to the success and safety of brain tumor surgery, leading to transformative improvements in patient outcomes. This comprehensive review intricately describes the evolution of conventional and emerging technologies for intraoperative imaging, encompassing the surgical microscope, exoscope, Raman spectroscopy, confocal microscopy, fluorescence-guided surgery, intraoperative ultrasound, magnetic resonance imaging, and computed tomography. We detail how each of these imaging modalities contributes uniquely to the precision, safety, and efficacy of neurosurgical procedures. Despite their substantial benefits, these technologies share common challenges, including difficulties in image interpretation and steep learning curves. Looking forward, innovations in this field are poised to incorporate artificial intelligence, integrated multimodal imaging approaches, and augmented and virtual reality technologies. This rapidly evolving landscape represents fertile ground for future research and technological development, aiming to further elevate surgical precision, safety, and, most critically, patient outcomes in the management of brain tumors.
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Affiliation(s)
- Othman Bin-Alamer
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Hussam Abou-Al-Shaar
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Zachary C. Gersey
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Sakibul Huq
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Justiss A. Kallos
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - David J. McCarthy
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Jeffery R. Head
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Edward Andrews
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Xiaoran Zhang
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Constantinos G. Hadjipanayis
- Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA; (O.B.-A.); (H.A.-A.-S.); (Z.C.G.); (S.H.); (J.A.K.); (D.J.M.); (J.R.H.); (E.A.); (X.Z.)
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
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