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Farnia P, Makkiabadi B, Alimohamadi M, Najafzadeh E, Basij M, Yan Y, Mehrmohammadi M, Ahmadian A. Photoacoustic-MR Image Registration Based on a Co-Sparse Analysis Model to Compensate for Brain Shift. SENSORS 2022; 22:s22062399. [PMID: 35336570 PMCID: PMC8954240 DOI: 10.3390/s22062399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/16/2021] [Accepted: 11/18/2021] [Indexed: 12/13/2022]
Abstract
Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images are proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic-MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using an experimental phantom and ex vivo data obtained from a mouse brain. The results of the phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. The results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidates pre-operative MRI.
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Affiliation(s)
- Parastoo Farnia
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran; (P.F.); (B.M.); (E.N.)
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran
| | - Bahador Makkiabadi
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran; (P.F.); (B.M.); (E.N.)
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran
| | - Maysam Alimohamadi
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran;
| | - Ebrahim Najafzadeh
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran; (P.F.); (B.M.); (E.N.)
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran
| | - Maryam Basij
- Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA; (M.B.); (Y.Y.)
| | - Yan Yan
- Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA; (M.B.); (Y.Y.)
| | - Mohammad Mehrmohammadi
- Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA; (M.B.); (Y.Y.)
- Barbara Ann Karmanos Cancer Institute, Detroit, MI 48201, USA
- Correspondence: (M.M.); (A.A.)
| | - Alireza Ahmadian
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran; (P.F.); (B.M.); (E.N.)
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran
- Correspondence: (M.M.); (A.A.)
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Gerard IJ, Kersten-Oertel M, Hall JA, Sirhan D, Collins DL. Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications. Front Oncol 2021; 10:618837. [PMID: 33628733 PMCID: PMC7897668 DOI: 10.3389/fonc.2020.618837] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/22/2020] [Indexed: 11/25/2022] Open
Abstract
Neuronavigation using pre-operative imaging data for neurosurgical guidance is a ubiquitous tool for the planning and resection of oncologic brain disease. These systems are rendered unreliable when brain shift invalidates the patient-image registration. Our previous review in 2015, Brain shift in neuronavigation of brain tumours: A review offered a new taxonomy, classification system, and a historical perspective on the causes, measurement, and pre- and intra-operative compensation of this phenomenon. Here we present an updated review using the same taxonomy and framework, focused on the developments of intra-operative ultrasound-based brain shift research from 2015 to the present (2020). The review was performed using PubMed to identify articles since 2015 with the specific words and phrases: “Brain shift” AND “Ultrasound”. Since 2015, the rate of publication of intra-operative ultrasound based articles in the context of brain shift has increased from 2–3 per year to 8–10 per year. This efficient and low-cost technology and increasing comfort among clinicians and researchers have allowed unique avenues of development. Since 2015, there has been a trend towards more mathematical advancements in the field which is often validated on publicly available datasets from early intra-operative ultrasound research, and may not give a just representation to the intra-operative imaging landscape in modern image-guided neurosurgery. Focus on vessel-based registration and virtual and augmented reality paradigms have seen traction, offering new perspectives to overcome some of the different pitfalls of ultrasound based technologies. Unfortunately, clinical adaptation and evaluation has not seen as significant of a publication boost. Brain shift continues to be a highly prevalent pitfall in maintaining accuracy throughout oncologic neurosurgical intervention and continues to be an area of active research. Intra-operative ultrasound continues to show promise as an effective, efficient, and low-cost solution for intra-operative accuracy management. A major drawback of the current research landscape is that mathematical tool validation based on retrospective data outpaces prospective clinical evaluations decreasing the strength of the evidence. The need for newer and more publicly available clinical datasets will be instrumental in more reliable validation of these methods that reflect the modern intra-operative imaging in these procedures.
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Affiliation(s)
- Ian J Gerard
- Department of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | | | - Jeffery A Hall
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Denis Sirhan
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - D Louis Collins
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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Farnia P, Mohammadi M, Najafzadeh E, Alimohamadi M, Makkiabadi B, Ahmadian A. High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging. Biomed Phys Eng Express 2020; 6:045019. [PMID: 33444279 DOI: 10.1088/2057-1976/ab9a10] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, high-quality real-time intra-operative imaging remains as a challenging problem. Meanwhile, photoacoustic imaging has appeared so promising to provide images of crucial structures such as blood vessels and microvasculature of tumors. To achieve high-quality photoacoustic images of vessels regarding the artifacts caused by the incomplete data, we proposed an approach based on the combination of time-reversal (TR) and deep learning methods. The proposed method applies a TR method in the first layer of the network which is followed by the convolutional neural network with weights adjusted to a set of simulated training data for the other layers to estimate artifact-free photoacoustic images. It was evaluated using a generated synthetic database of vessels. The mean of signal to noise ratio (SNR), peak SNR, structural similarity index, and edge preservation index for the test data were reached 14.6 dB, 35.3 dB, 0.97 and 0.90, respectively. As our results proved, by using the lower number of detectors and consequently the lower data acquisition time, our approach outperforms the TR algorithm in all criteria in a computational time compatible with clinical use.
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Affiliation(s)
- Parastoo Farnia
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran. Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
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Evaluation of multi-wavelengths LED-based photoacoustic imaging for maximum safe resection of glioma: a proof of concept study. Int J Comput Assist Radiol Surg 2020; 15:1053-1062. [PMID: 32451814 DOI: 10.1007/s11548-020-02191-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 04/28/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE A real-time intra-operative imaging modality is required to update the navigation systems during neurosurgery, since precise localization and safe maximal resection of gliomas are of utmost clinical importance. Different intra-operative imaging modalities have been proposed to delineate the resection borders, each with advantages and disadvantages. This preliminary study was designed to simulate the photoacoustic imaging (PAI) to illustrate the brain tumor margin vessels for safe maximal resection of glioma. METHODS In this study, light emitting diode (LED)-based PAI was selected because of its lower cost, compact size and ease of use. We developed a simulation framework based on multi-wavelength LED-based PAI to further facilitate PAI during neurosurgery. This framework considers a multilayer model of the tumoral and normal brain tissue. The simulation of the optical fluence and absorption map in tissue at different depths was computed by Monte Carlo. Then, the propagation of initial photoacoustic pressure was simulated by using k-wave toolbox. RESULTS To evaluate the LED-based PAI, we used three evaluation criteria: signal-to-noise ratio (SNR), contrast ratio (CR) and full width of half maximum (FWHM). Results showed that by using proper wavelengths, the vessels were recovered with the same axial and lateral FWHM. Furthermore, by increasing the wavelength from 532 to 1064 nm, SNR and CR were increased in the deep region. The results showed that vessels with larger diameters at same wavelength have a higher CR with average improvement 28%. CONCLUSION Multi-wavelength LED-based PAI provides detailed images of the blood vessels which are crucial for detection of the residual glioma: The longer wavelengths like 1064 nm can be used for the deeper tumor margins, and the shorter wavelengths like 532 nm for tumor margins closer to the surface. LED-based PAI may be considered as a promising intra-operative imaging modality to delineate tumor margins.
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Machado I, Toews M, George E, Unadkat P, Essayed W, Luo J, Teodoro P, Carvalho H, Martins J, Golland P, Pieper S, Frisken S, Golby A, Wells Iii W, Ou Y. Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data. Neuroimage 2019; 202:116094. [PMID: 31446127 DOI: 10.1016/j.neuroimage.2019.116094] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 07/18/2019] [Accepted: 08/09/2019] [Indexed: 11/16/2022] Open
Abstract
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38 mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78 mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature.
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Affiliation(s)
- Inês Machado
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
| | - Matthew Toews
- Department of Systems Engineering, École de Technologie Supérieure, Montreal, Canada
| | - Elizabeth George
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Prashin Unadkat
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Walid Essayed
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jie Luo
- Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Pedro Teodoro
- Escola Superior Náutica Infante D. Henrique, Lisbon, Portugal
| | - Herculano Carvalho
- Department of Neurosurgery, Hospital de Santa Maria, CHLN, Lisbon, Portugal
| | - Jorge Martins
- Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Steve Pieper
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Isomics, Inc., Cambridge, MA, USA
| | - Sarah Frisken
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - William Wells Iii
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Yangming Ou
- Department of Pediatrics and Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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Farnia P, Najafzadeh E, Ahmadian A, Makkiabadi B, Alimohamadi M, Alirezaie J. Co-Sparse Analysis Model Based Image Registration to Compensate Brain Shift by Using Intra-Operative Ultrasound Imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:1-4. [PMID: 30440252 DOI: 10.1109/embc.2018.8512375] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Notwithstanding the widespread use of image guided neurosurgery systems in recent years, the accuracy of these systems is strongly limited by the intra-operative deformation of the brain tissue, the so-called brain shift. Intra-operative ultrasound (iUS) imaging as an effective solution to compensate complex brain shift phenomena update patients coordinate during surgery by registration of the intra-operative ultrasound and the pre-operative MRI data that is a challenging problem.In this work a non-rigid multimodal image registration technique based on co-sparse analysis model is proposed. This model captures the interdependency of two image modalities; MRI as an intensity image and iUS as a depth image. Based on this model, the transformation between the two modalities is minimized by using a bimodal pair of analysis operators which are learned by optimizing a joint co-sparsity function using a conjugate gradient.Experimental validation of our algorithm confirms that our registration approach outperforms several of other state-of-the-art registration methods quantitatively. The evaluation was performed using seven patient dataset with the mean registration error of only 1.83 mm. Our intensity-based co-sparse analysis model has improved the accuracy of non-rigid multimodal medical image registration by 15.37% compared to the curvelet based residual complexity as a powerful registration method, in a computational time compatible with clinical use.
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