Assistant ProfessorPhD, University of Western Ontario
Sunnybrook Health Sciences Centre
2075 Bayview Avenue, M6-605
Toronto, Ontario M4N 3M5
Phone: (416) 480-6100 x1018
Fax: (416) 480-6002
Email Dr. Ali Sadeghi-Naini
At A Glance:
- Intrinsically multidisciplinary research lab
- Bridging between challenges in cancer therapeutics on one side and physics and engineering innovations
- Central theme is on image-guided personalized cancer therapeutics
- Development of integrated imaging and computational frameworks at the core of the lab research
- Projects from basic science and engineering research to translational and clinical studies
- In synergy with an Intradisciplinary network of clinician and scientist collaborators with strong emphasis on integrated knowledge translation
- Main applications: non-invasive cancer detection and characterization, interventional planning and navigation, and treatment response monitoring
Dr. Ali Sadeghi-Naini is an Assistant Professor in the Department of Medical Biophysics at the University of Toronto, and a Scientist within the Physical Sciences Platform and Odette Cancer Research Program at Sunnybrook Research Institute. He earned his PhD in biomedical engineering from the University of Western Ontario in 2011, enriched by his participation in the NSERC-CREATE program in Computer-Assisted Medical Interventions (CAMI). Dr. Sadeghi-Naini completed his postdoctoral fellowship in medical biophysics and radiation oncology at Sunnybrook Research Institute, University of Toronto. His postdoctoral research was supported by a Canadian Breast Cancer Foundation postdoctoral fellowship and a CIHR Banting postdoctoral fellowship. Dr. Sadeghi-Naini's areas of research interest include computer-aided image-guided theragnostics, quantitative multimodal imaging, inverse imaging techniques, medical image analysis and machine learning.
Time plays an important role in the outcome of many therapeutic regimens, especially those targeting aggressive disease such as cancer. Whereas an early cancer diagnosis keeps more therapeutic options available for individual patients, an optimized treatment planning and delivery improves the efficacy of treatments. Nevertheless, cancer patients respond differently to identical treatments. Therefore, a standard predefined therapy is not often effective for all patients. An early prognosis provides an opportunity to adjust a standard treatment on an individual patient basis, or to change an ineffective therapy to a more effective one for a non-responding patient, before it is potentially too late.
Our laboratory is focusing on developing computer-aided image-guided technologies to improve personalized cancer therapeutics. In particular, we are interested in developing integrated imaging and computational frameworks to detect and characterize cancer, to plan and navigate cancer-targeting interventions, and to monitor cancer response to treatment.
Quantitative Multimodal Cancer ImagingVarious characteristics of a tumour in terms of micro-structure and function, if quantified precisely, provide important diagnostic and prognostic information to prescribe and optimize a treatment. In this context, we are investigating novel methods of multimodal cancer imaging to explore different stages during cancer development and cure from various structural and functional perspectives. Especially, our laboratory is developing integrated frameworks to adapt complementary aspects of quantitative ultrasound imaging, optical spectroscopy, elastography, computed tomography and magnetic resonance imaging in order to characterize a tumour in terms of its morphology, micro-structure, physiology, perfusion, metabolism and biomechanical properties. We are also investigating alterations in such tumour characteristics to develop sensitive biomarkers of cancer response to treatment.
Modeling Intra-Tumour HeterogeneityA particular area of interest is in developing ad hoc models for quantification of spatial heterogeneity in cancer imaging. Alterations within a tumour during its formation or degeneration are frequently inhomogeneous. Therefore, quantifying intra-tumour heterogeneities can provide further insights into tumour characteristics, or rapidly flag a change in tumour state within its life cycle. In order to quantify intra-tumour heterogeneity non-invasively, our laboratory is developing novel image-processing frameworks to model and analyze the texture within tumour images. We are adapting machine learning techniques to determine how to correspond these textural features to specific tumour characteristics, or to a change that indicates tumour response to treatment.
Image-Guided Minimally-Invasive Cancer Interventions.Crucial components in development of image-guided minimally-invasive interventions include those assisting with an accurate placement of interventional tools, e.g., catheter, or a precise delivery of pre-planned therapies, e.g., radiation dose. In such applications, several sources of anatomical motion and deformation including respiration, diaphragm contact forces and heart beating can considerably diminish the accuracy and impact the procedure outcome. As another area of research focus, our laboratory is transforming multimodal imaging within computational frameworks to facilitate the planning and navigation of interventional procedures such as biopsy, brachytherapy and radiation therapy. We are also developing hybrid frameworks consisting of image processing, biomechanical modeling and machine learning components in order to quantify, formulate, predict and compensate for motions and deformations within a target anatomy during such procedures.
List of Key Publications:
Sadeghi-Naini A, Vorauer E, Chin L, Falou O, Tran WT, Wright FC, Gandhi S, Yaffe MJ, Czarnota GJ. Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images. Med Phys. 2015; 42(11):6130-6146.
Sadeghi-Naini A, Sofroni E, Papanicolau N, Falou O, Sugar L, Morton G, Yaffe M, Nam R, Sadeghian A, Kolios MC, Chung HT, Czarnota GJ. Quantitative ultrasound spectroscopic imaging for characterization of disease extent in prostate cancer patients. Transl Oncol. 2015; 8(1):25-34.
Sadeghi-Naini A, Sannachi L, Pritchard K, Trudeau ME, Gandhi S, Wright FC, Zubovits J, Yaffe MJ, Kolios MC, Czarnota GJ. Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture. Oncotarget. 2014; 5(11):3497-3511.
Mousavi SR, Sadeghi-Naini A, Czarnota GJ, Samani A. Towards clinical prostate ultrasound elastography using full inversion approach. Med Phys. 2014; 41(3):033501.
Sadeghi-Naini A, Papanicolau N, Falou O, Zubovits J, Dent R, Verma S, Trudeau ME, Boileau JF, Spayne J, Iradji S, Sofroni E, Lee J, Lemon-Wong S, Yaffe MJ, Kolios MC, Czarnota GJ. Quantitative ultrasound evaluation of tumour cell death response in locally advanced breast cancer patients receiving chemotherapy. Clin Cancer Res. 2013; 19(8):2163-2174.
Sadeghi-Naini A, Falou O, Tadayyon H, Al-Mahrouki A, Tran WT, Papanicolau N, Kolios MC, Czarnota GJ. Conventional-frequency ultrasonic biomarkers of cancer treatment response in vivo. Transl Oncol. 2013; 6(3):234-243.
Sadeghi-Naini A, Falou O, Hudson JM, Bailey C, Burns PN, Yaffe MJ, Stanisz GJ, Kolios MC, Czarnota GJ. Imaging innovations for cancer therapy response monitoring. Imaging Med. 2012; 4(3):311-327.
Sadeghi-Naini A, Patel RV, Samani A. Measurement of lung hyperelastic properties using inverse finite element approach. IEEE Trans Biomed Eng. 2011; 58(10):2852-2859.
Sadeghi-Naini A, Pierce G, Lee TY, Patel RV, Samani A. CT image construction of a totally deflated lung using deformable model extrapolation. Med Phys. 2011; 38(2):872-883.
Sadeghi-Naini A, Patel RV, Samani A. CT enhanced ultrasound image of a totally deflated lung for image-guided minimally invasive tumour ablative procedures. IEEE Trans Biomed Eng. 2010; 57(10):2627-2630.