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Using Breast Tissue Information and Subject-Specific Finite-Element Models to Optimize Breast Compression Parameters for Digital Mammography. ELECTRONICS 2022. [DOI: 10.3390/electronics11111784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Digital mammography has become a first-line diagnostic tool for clinical breast cancer screening due to its high sensitivity and specificity. Mammographic compression force is closely associated with image quality and patient comfort. Therefore, optimizing breast compression parameters is essential. Subjects were recruited for digital mammography and breast magnetic resonance imaging (MRI) within a month. Breast MRI images were used to calculate breast volume and volumetric breast density (VBD) and construct finite element models. Finite element analysis was performed to simulate breast compression. Simulated compressed breast thickness (CBT) was compared with clinical CBT and the relationships between compression force, CBT, breast volume, and VBD were established. Simulated CBT had a good linear correlation with the clinical CBT (R2 = 0.9433) at the clinical compression force. At 10, 12, 14, and 16 daN, the mean simulated CBT of the breast models was 5.67, 5.13, 4.66, and 4.26 cm, respectively. Simulated CBT was positively correlated with breast volume (r > 0.868) and negatively correlated with VBD (r < –0.338). The results of this study provides a subject-specific and evidence-based suggestion of mammographic compression force for radiographers considering image quality and patient comfort.
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Automated Segmentation of Mass Regions in DBT Images Using a Dilated DCNN Approach. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9082694. [PMID: 35154309 PMCID: PMC8828338 DOI: 10.1155/2022/9082694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/02/2022] [Accepted: 01/15/2022] [Indexed: 11/25/2022]
Abstract
To overcome the limitations of conventional breast screening methods based on digital mammography, a quasi-3D imaging technique, digital breast tomosynthesis (DBT) has been developed in the field of breast cancer screening in recent years. In this work, a computer-aided architecture for mass regions segmentation in DBT images using a dilated deep convolutional neural network (DCNN) is developed. First, to improve the low contrast of breast tumour candidate regions and depress the background tissue noise in the DBT image effectively, the constraint matrix is established after top-hat transformation and multiplied with the DBT image. Second, input image patches are generated, and the data augmentation technique is performed to create the training data set for training a dilated DCNN architecture. Then the mass regions in DBT images are preliminarily segmented; each pixel is divided into two different kinds of labels. Finally, the postprocessing procedure removes all false-positives regions with less than 50 voxels. The final segmentation results are obtained by smoothing the boundaries of the mass regions with a median filter. The testing accuracy (ACC), sensitivity (SEN), and the area under the receiver operating curve (AUC) are adopted as the evaluation metrics, and the ACC, SEN, as well as AUC are 86.3%, 85.6%, and 0.852 for segmenting the mass regions in DBT images on the entire data set, respectively. The experimental results indicate that our proposed approach achieves promising results compared with other classical CAD-based frameworks.
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Hoegen P, Spindeldreier CK, Buchele C, Rippke C, Regnery S, Weykamp F, Klüter S, Debus J, Hörner-Rieber J. [Magnetic-resonance-guided radiotherapy : The beginning of a new era in radiation oncology?]. Radiologe 2021; 61:13-20. [PMID: 33052442 DOI: 10.1007/s00117-020-00761-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
CLINICAL ISSUE Image-guided radiotherapy (IGRT) using X‑rays and cone-beam computed tomography (CT) has fostered precision radiotherapy. However, inter- and intrafractional variations of target volume position and organs at risk still limit target volume dose and sparing of radiosensitive organs at risk. METHODOLOGICAL INNOVATIONS Hybrid machines directly combining linear accelerators and magnetic resonance (MR) imaging allow for live imaging during radiotherapy. PERFORMANCE Besides highly improved soft tissue contrast, MR-linacs enable online, on-table adaptive radiotherapy. Thus, adaptation of the treatment plan to the anatomy of the day, dose escalation and superior sparing of organs at risk become possible. ACHIEVEMENTS This article summarizes the underlying intention for the development of MR-guided radiotherapy, technical innovations and challenges as well as the current state-of-the-art. Potential clinical benefits and future developments are discussed. PRACTICAL RECOMMENDATIONS Increasing availability of MR imaging at linear accelerators calls for the ability to review and interpret MR images. Therefore, close collaborations of diagnostic radiologists and radiation oncologists are mandatory to foster this fascinating technique.
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Affiliation(s)
- P Hoegen
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland.,Nationales Centrum für Tumorerkrankungen (NCT), Heidelberg, Deutschland.,Clinical Cooperation Unit Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
| | - C K Spindeldreier
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland
| | - C Buchele
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland
| | - C Rippke
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland
| | - S Regnery
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland.,Nationales Centrum für Tumorerkrankungen (NCT), Heidelberg, Deutschland
| | - F Weykamp
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland.,Nationales Centrum für Tumorerkrankungen (NCT), Heidelberg, Deutschland
| | - S Klüter
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland
| | - J Debus
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland.,Nationales Centrum für Tumorerkrankungen (NCT), Heidelberg, Deutschland.,Clinical Cooperation Unit Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland.,Heidelberger Ionenstrahl-Therapiezentrum (HIT), Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Heidelberg, Deutschland.,Standort Heidelberg, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Heidelberg, Deutschland
| | - J Hörner-Rieber
- Klinik für Radioonkologie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland. .,Heidelberger Institut für Radiooncology (HIRO), Heidelberg, Deutschland. .,Nationales Centrum für Tumorerkrankungen (NCT), Heidelberg, Deutschland. .,Clinical Cooperation Unit Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland.
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Green CA, Goodsitt MM, Lau JH, Brock KK, Davis CL, Carson PL. Deformable Mapping Method to Relate Lesions in Dedicated Breast CT Images to Those in Automated Breast Ultrasound and Digital Breast Tomosynthesis Images. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:750-765. [PMID: 31806500 DOI: 10.1016/j.ultrasmedbio.2019.10.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 10/03/2019] [Accepted: 10/18/2019] [Indexed: 06/10/2023]
Abstract
This work demonstrates the potential for using a deformable mapping method to register lesions between dedicated breast computed tomography (bCT) and both automated breast ultrasound (ABUS) and digital breast tomosynthesis (DBT) images (craniocaudal [CC] and mediolateral oblique [MLO] views). Two multi-modality breast phantoms with external fiducial markers attached were imaged by the three modalities. The DBT MLO view was excluded for the second phantom. The automated deformable mapping algorithm uses biomechanical modeling to determine corresponding lesions based on distances between their centers of mass (dCOM) in the deformed bCT model and the reference model (DBT or ABUS). For bCT to ABUS, the mean dCOM was 5.2 ± 2.6 mm. For bCT to DBT (CC), the mean dCOM was 5.1 ± 2.4 mm. For bCT to DBT (MLO), the mean dCOM was 4.7 ± 2.5 mm. This application could help improve a radiologist's efficiency and accuracy in breast lesion characterization, using multiple imaging modalities.
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Affiliation(s)
- Crystal A Green
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, USA; Department of Radiology, University of Michigan Health System, Ann Arbor, MI, USA.
| | - Mitchell M Goodsitt
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, USA; Department of Radiology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Jasmine H Lau
- Department of Radiology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Kristy K Brock
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Paul L Carson
- Department of Radiology, University of Michigan Health System, Ann Arbor, MI, USA
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